1. Bibliographic Information

1.1. Title

AI-Driven Antimicrobial Peptide Discovery: Mining and Generation

1.2. Authors

Paulina Szymczak, Wojciech Zarzecki, Jiejing Wang, Yiqian Duan, Jun Wang, Luis Pedro Coelho, Cesar de la Fuente-Nunez, and Ewa Szczurek

The authors represent a multidisciplinary group of researchers from various institutions, including the Institute of AI for Health at Helmholtz Munich, University of Warsaw, Chinese Academy of Sciences, Fudan University, and Queensland University of Technology. Their backgrounds span bioinformatics, computer science, microbiology, computational biology, and machine biology, indicating a strong interdisciplinary approach to the problem of antimicrobial resistance (AMR) and antimicrobial peptide (AMP) discovery using artificial intelligence (AI).

1.3. Journal/Conference

ACS Accounts of Chemical Research, 2025, 58, 1831-1846.

ACS Accounts of Chemical Research is a highly respected journal published by the American Chemical Society (ACS), known for featuring concise, personal accounts of research from leading scientists. Its focus on significant advances in chemistry and related fields, presented in an accessible narrative style, underscores the importance and impact of the work being reviewed. Publication in such a journal indicates that the work is considered a significant contribution to the chemical and biomedical sciences, making it influential in the field of drug discovery and AI applications in chemistry.

1.4. Publication Year

Published: June 3, 2025 (Received: November 7, 2024; Revised: April 25, 2025; Accepted: April 28, 2025)

1.5. Abstract

The abstract highlights the pressing global health crisis of antimicrobial resistance (AMR) and positions antimicrobial peptides (AMPs) as a promising alternative to traditional antibiotics due to their bacterial selectivity and slower induction of resistance. The core challenge in AMP design is balancing vast sequence diversity with toxicity and efficacy. The paper reviews how artificial intelligence (AI) is revolutionizing AMP discovery through two main strategies: mining (identifying AMPs from existing biological sequences using discriminative models to predict activity and toxicity) and generation (creating new peptides using generative models optimized for enhanced efficacy and safety). It delves into technical advancements, data integration, and algorithmic improvements that refine peptide prediction and design. The authors underscore AI’s transformative role in accelerating discovery, uncovering novel peptides, and offering new hope against AMR, advocating for continued AI integration into biomedical research.

1.6. Original Source Link

/files/papers/6909d57a4d0fb96d11dd73c3/paper.pdf (This is the official PDF link provided.)

2. Executive Summary

2.1. Background & Motivation

The core problem the paper addresses is the escalating global health crisis of antimicrobial resistance (AMR). This phenomenon, where microorganisms evolve to resist the effects of antibiotics, is projected to surpass cancer as a leading cause of death by 2050. The traditional methods for discovering new antibiotics have proven to be slow and inefficient, failing to keep pace with the rapid evolution of resistance mechanisms. This has led to a "discovery void" in the past three decades, with very few novel antibiotic classes reaching the market.

In this context, antimicrobial peptides (AMPs) emerge as a highly promising class of therapeutics. AMPs are naturally occurring small proteins with diverse mechanisms of action that often target bacterial membranes, leading to slower induction of resistance compared to conventional small-molecule antibiotics. However, designing effective AMPs is fraught with challenges. The combinatorial sequence space for peptides is astronomically large (e.g., $10^{32}$ for peptides up to 25 amino acids), making brute-force discovery computationally infeasible. Furthermore, a critical challenge is balancing efficacy with low toxicity to mammalian cells, as many potent AMPs also exhibit undesirable cytotoxic or hemolytic activities. Existing experimentally verified AMPs are minuscule in number compared to the vast potential, and known peptides have had limited success in clinical applications.

The paper's entry point and innovative idea is to leverage artificial intelligence (AI) to overcome these fundamental limitations. AI offers a powerful paradigm shift, transforming the laborious, time-consuming process of antibiotic discovery into an accelerated, data-driven endeavor. It aims to efficiently navigate the immense chemical space of peptides, identify novel candidates, and optimize their properties for clinical utility.

2.2. Main Contributions / Findings

The paper’s primary contributions lie in systematically reviewing and conceptualizing the application of AI to AMP discovery through two main strategies:

1. AMP Mining: This strategy involves using discriminative models to scan existing biological sequences (from genomes, proteomes, and metagenomes) and identify potential AMP candidates. The paper highlights that this approach successfully yields realistic peptides (i.e., those likely to be naturally produced) and has led to the identification of numerous promising candidates, some of which have been validated experimentally both in vitro (in lab dishes) and in vivo (in living organisms, like animal models). This includes discoveries from diverse sources like the human proteome, extinct organisms (molecular de-extinction), and various microbiomes.

2. AMP Generation: This strategy employs generative models to create entirely novel peptide sequences from scratch. These models learn from existing data distributions and are optimized to design idealistic peptides with desired properties such as increased activity and reduced toxicity. The paper emphasizes the potential for generative AI to produce synthetic peptides that surpass naturally occurring ones in terms of efficacy and safety, despite the challenge of ensuring realistic and experimentally viable sequences.

   Key conclusions and findings reached by the paper include:

• Acceleration of Discovery: AI-based algorithms have drastically accelerated the discovery process, transforming tasks that once took years into those that can be completed in hours.

• Novelty and Diversity: AI enables the identification and generation of AMPs with unprecedented properties, often showing low sequence homology to known AMPs.

• Experimental Validation: Many AI-discovered peptides have shown proven efficacy in preclinical mouse models, demonstrating the practical potential of these approaches.

• Technological Advancements: The integration of advanced algorithms, particularly deep learning models like Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTMs), Convolutional Neural Networks (CNNs), and especially Large Language Models (LLMs) (like BERT and ESM), has refined peptide prediction and design capabilities.

• Addressing the AMR Crisis: The synergy between AI and AMP discovery opens new frontiers in the fight against AMR, offering hope for a future where novel, effective, and safe antimicrobial therapies are readily available.

  The paper underscores AI's transformative role in drug discovery and advocates for its continued integration into biomedical research as a critical tool for developing next-generation antimicrobial therapies.

3. Prerequisite Knowledge & Related Work

3.1. Foundational Concepts

To understand this paper, a foundational grasp of several biological and computational concepts is essential:

• Antimicrobial Resistance (AMR):

  • Conceptual Definition: AMR refers to the ability of microorganisms (like bacteria, fungi, viruses, and parasites) to resist the effects of antimicrobial drugs (like antibiotics) that were once effective against them. This means that the drugs can no longer kill the microbes or stop their growth.
  • Importance: AMR is a severe global public health threat because it makes infections harder to treat, leading to increased rates of illness, disability, and death. It complicates medical procedures (like surgery and chemotherapy) and can render common infections untreatable.

• Antimicrobial Peptides (AMPs):

  • Conceptual Definition: AMPs, also known as host defense peptides, are a diverse class of naturally occurring small proteins (typically 10-100 amino acids long) found in virtually all forms of life. They are key components of the innate immune system.
  • Characteristics:
    • Short length: Usually between 10 and 100 amino acids.
    • Net positive charge: Commonly +2 to +9, due to an abundance of basic amino acids like lysine and arginine. This positive charge is crucial for their interaction with negatively charged bacterial membranes.
    • High hydrophobicity: Typically $\ge 30\%$ hydrophobic amino acids, which helps them interact with the lipid bilayers of cell membranes.
    • Diverse structures: Can adopt alpha-helical, beta-sheet, linear extension, or mixed alpha-beta conformations.
  • Mechanisms of Action: Unlike traditional antibiotics that often target specific bacterial enzymes or processes, AMPs typically act on bacterial cell membranes, causing disruption, increased permeability, and eventual lysis (bursting) of the cell. They can also inhibit essential intracellular processes like protein or nucleic acid synthesis, or protease activity.
  • Advantages over Traditional Antibiotics: AMPs generally induce resistance in bacteria more slowly because their primary mechanism (membrane disruption) is harder for bacteria to evolve resistance against compared to specific enzyme inhibition. They also show selectivity towards bacteria, tending not to harm neutral mammalian cell membranes.

• Artificial Intelligence (AI):

  • Conceptual Definition: AI is a broad field of computer science focused on creating machines that can perform tasks that typically require human intelligence, such as learning, problem-solving, decision-making, perception, and understanding language.

• Machine Learning (ML):

  • Conceptual Definition: A subset of AI that enables systems to learn from data without being explicitly programmed. ML algorithms build a mathematical model based on sample data, known as "training data," to make predictions or decisions without being specifically programmed to perform the task.
  • Role in AMPs: Used for identifying patterns in peptide sequences, predicting their properties (activity, toxicity), and guiding the design of new peptides.

• Deep Learning (DL):

  • Conceptual Definition: A subfield of ML that uses artificial neural networks with multiple layers (hence "deep") to learn complex patterns from large amounts of data. DL models are particularly good at learning hierarchical representations of data.
  • Role in AMPs: DL models (like RNNs, LSTMs, CNNs, Transformers) are used for more sophisticated analysis of peptide sequences, often outperforming traditional ML methods due to their ability to automatically extract relevant features and capture long-range dependencies within sequences.

• Generative Models vs. Discriminative Models:

  • Discriminative Models: These models learn to distinguish between different classes or predict a label for a given input. In AMP discovery, they are used to classify a peptide as an AMP or non-AMP, predict its activity level, or predict its toxicity. Examples include Support Vector Machines (SVMs), Random Forests (RFs), and many neural network architectures trained for classification or regression.
  • Generative Models: These models learn the underlying distribution of the training data and can then generate new, similar data samples. In AMP discovery, they learn the patterns of effective AMP sequences and can then generate novel sequences that are likely to be active and non-toxic. Examples include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs) adapted for protein sequences.

• Amino Acid Sequences:

  • Conceptual Definition: Proteins and peptides are polymers made up of smaller units called amino acids, linked together in a specific order. An amino acid sequence is simply the linear order of these amino acids, represented by a string of single-letter codes (e.g., "ALWKTLL"). This sequence determines the peptide's primary structure, which in turn largely dictates its three-dimensional structure and function.

3.2. Previous Works

The paper contextualizes its work by highlighting the historical challenges and the state of the field before the widespread adoption of AI:

• Antibiotic Discovery Void: Following a "boom" in antibiotic discoveries in the 20th century, the past three decades have seen a significant slowdown, with no major new classes reaching the market. This discovery void underscores the limitations of traditional drug discovery pipelines, which are often slow, expensive, and fail to keep pace with evolving resistance.
• Escalating Resistance: Concurrently, resistance to existing antibiotics has intensified, making the development of novel therapeutic strategies critical.
• AMP Promise and Challenges: AMPs have long been recognized as promising candidates due to their distinct mechanisms of action and slower resistance induction. However, their vast combinatorial sequence space (e.g., $10^{32}$ sequences for peptides up to 25 amino acids) makes traditional brute-force search or empirical design impractical.
• Data Scarcity: The number of experimentally verified AMPs is relatively small (e.g., $10^4$ in databases like DBAASP), with even fewer validated against specific bacterial species. This scarce data made it difficult to develop robust predictive models using earlier computational methods.
• Toxicity Concerns: Many potent AMPs exhibit toxicity to mammalian cells (e.g., hemolytic activity, cytotoxicity), posing a major hurdle for clinical translation. Early computational efforts struggled to reliably predict and balance efficacy with low toxicity.
• Early Computational Approaches: Before the rise of deep learning and LLMs, traditional machine learning (ML) methods (like Support Vector Machines, Random Forests) were used for AMP prediction, primarily relying on sequence-derived descriptors (e.g., amino acid composition, hydrophobicity). While useful, these methods often lacked the capacity to capture complex, hidden patterns in sequences or to generate novel sequences effectively. For example, Pane et al. (2017) developed an algorithm using physicochemical properties to predict antimicrobial potency, which laid some groundwork for later AI-driven mining efforts.

3.3. Technological Evolution

The field of AMP discovery has seen an evolution from traditional empirical screening and rational design to advanced computational approaches:

1. Empirical Screening & Rational Design: Historically, AMPs were discovered through laborious screening of natural sources (e.g., amphibian skin secretions) or through rational design based on known AMP motifs. This was slow and limited in scope.

2. Early Computational Methods (Traditional ML): The advent of computational power allowed for the use of traditional ML algorithms. These methods helped in classifying potential AMPs based on handcrafted features derived from their amino acid sequences (e.g., charge, hydrophobicity, amino acid frequency). They provided a first step towards systematic prediction but were limited by the need for expert-defined features and struggled with the vastness of the sequence space.

3. Deep Learning Revolution: The deep learning (DL) revolution brought about neural networks with multiple layers (RNNs, LSTMs, CNNs). These models can automatically learn complex, hierarchical features directly from raw sequence data, overcoming the feature engineering bottleneck of traditional ML. They significantly improved prediction accuracy for AMP classification and activity prediction.

4. Attention Mechanisms and Transformers (LLMs): The introduction of attention mechanisms and the Transformer architecture (the basis of Large Language Models, or LLMs) marked a pivotal shift. Transformers, originally developed for natural language processing, proved remarkably effective for biological sequences (Protein Language Models, or PLMs). They excel at capturing long-range dependencies and contextual relationships within sequences, leading to more nuanced and accurate predictions.

5. Generative AI: The evolution extended beyond prediction to generation. VAEs, GANs, and later LLMs adapted for peptides, enabled the de novo design of novel AMP sequences, moving from identifying existing candidates to creating entirely new ones. This represents a significant leap in drug discovery capabilities.

   This paper’s work fits into the current state-of-the-art by showcasing how LLMs and advanced deep generative models are pushed to their limits in both mining and generation strategies, integrating increasingly complex data and architectures to refine peptide design and prediction.

3.4. Differentiation Analysis

Compared to earlier approaches, the core differences and innovations of this paper's discussed AI-driven methods are:

• Overcoming Combinatorial Explosion: Traditional methods or simple ML models are overwhelmed by the vast combinatorial sequence space of peptides. AI, especially deep learning and generative models, can efficiently navigate this space, either by intelligently mining promising regions or by generating novel sequences that optimize desired properties.

• Automated Feature Extraction: Unlike traditional ML that relies on handcrafted features (e.g., charge, hydrophobicity), deep learning models (CNNs, RNNs, LSTMs) and LLMs can automatically learn complex, high-level features directly from raw amino acid sequences. This eliminates the need for expert knowledge in feature engineering and allows for the discovery of non-obvious patterns.

• Enhanced Prediction Accuracy and Specificity:

  • Activity: Modern discriminative models, particularly those using PLM embeddings, demonstrate higher accuracy in distinguishing AMPs from non-AMPs, predicting Minimum Inhibitory Concentration (MIC) values, and even predicting strain-specific activity.
  • Toxicity: While still challenging, AI models are increasingly being developed to predict hemolytic activity and cytotoxicity, crucial for clinical viability, which was a major blind spot for earlier methods.

• De Novo Design Capability (Generative AI): This is a fundamental shift. Prior approaches largely focused on identifying existing AMPs or modifying known ones. Generative AI actively designs entirely novel peptides that may not exist in nature, optimized for specific functions, potentially "surpassing those found in nature."

• Leveraging Massive Data: The ability of deep learning and LLMs to process and learn from large corpuses of biological sequences (genomes, proteomes, metagenomes) is a key differentiator. This enables AMP mining on an unprecedented scale, discovering millions of candidates.

• Molecular De-extinction: The concept of molecular de-extinction, applying AI to extinct organisms' proteomes, is a novel application area that was not feasible with older techniques.

• Controlled Generation: Advanced generative models incorporate mechanisms for controlled generation, allowing researchers to specify desired properties (e.g., target organism, toxicity level, specific structural motifs) and generate peptides that adhere to these constraints, which is a significant improvement over random generation.

• Multi-objective Optimization: Newer models (M3-CAD, HydrAMP) can optimize for multiple properties simultaneously (activity, non-toxicity, specific mechanisms of action, even predicted 3D structure), offering a more holistic design approach.

  In essence, AI transforms AMP discovery from a reactive, labor-intensive screening process into a proactive, intelligent design and generation pipeline, significantly improving speed, scale, and the potential for true novelty.

4. Methodology

The paper outlines an AI-driven Antimicrobial Peptide Discovery framework built upon two primary strategies: AMP mining and AMP generation. Both strategies heavily rely on discriminative methods for evaluating and guiding the discovery process.

4.1. Principles

The core idea behind using AI for AMP discovery is to leverage computational power and advanced algorithms to efficiently explore the vast combinatorial sequence space of peptides, which is infeasible through traditional experimental methods. This exploration aims to:

1. Identify existing AMPs (Mining): By applying discriminative models to large biological sequence databases (genomes, proteomes, metagenomes), AI can predict which naturally occurring peptide sequences are likely to have antimicrobial properties and low toxicity. This aligns with a realism aspect, focusing on peptides that are likely to be biologically produced.

2. Design novel AMPs (Generation): By learning the underlying patterns and properties of known AMPs, generative models can synthesize entirely new peptide sequences. These models can be optimized to produce idealistic peptides with enhanced activity and reduced toxicity, potentially surpassing natural counterparts.

   The theoretical basis for these approaches stems from the understanding that peptide sequences encode functional properties. AI models, particularly deep learning and Large Language Models (LLMs), are adept at learning complex, non-linear relationships and hierarchical features from sequential data (like amino acid sequences). They can capture the "language" of peptides, similar to how they understand human language.

The intuition is that if we can teach an AI what an effective, non-toxic AMP "looks like" (through discriminative models), we can then use that knowledge to either find more of them in biological data or create new ones from scratch (through generative models). This iterative process, often involving experimental validation and feedback to the AI models, forms a powerful drug discovery pipeline.

4.2. Discriminative Methods

Discriminative methods are crucial tools that underpin both AMP mining and AMP generation. Their primary role is to predict the properties of a given peptide sequence, specifically its antimicrobial activity and toxicity.

4.2.1. Tasks and Objectives of Discriminative Models

Discriminative models in AMP discovery serve several key tasks:

• AMP vs. non-AMP Classification: Most models aim to broadly distinguish Antimicrobial Peptides (AMPs) from non-AMPs. Examples include sAMP-pred-GAT, AMPlify, and AMPpredMFA.
• Potency Prediction: More sophisticated approaches predict the degree of activity, either through classification (e.g., highly potent vs. moderately potent) or regression (predicting Minimum Inhibitory Concentration (MIC) values).
• Strain- or Species-Specific Activity: Some models aim to predict activity against particular microbes, such as AMP-META or MBC-attention for E. coli.
• Toxicity Prediction: Crucially, models are developed to predict mammalian cell toxicity, including hemolytic activity (rupturing red blood cells) and cytotoxicity (toxicity to other cell types). Examples include EnDL-HemoLyt, AMP-META, and Macrel.

4.2.2. Models and Architectures

Discriminative methods employ a range of machine learning and deep learning models:

Traditional ML Methods

Traditional machine learning (ML) methods like decision trees, Support Vector Machines (SVMs), and random forest (RF) were among the first to be applied.

• Reliance on Features: These models rely entirely on sequence-derived descriptors (e.g., amino acid composition, net charge, hydrophobicity moment) as input features. These descriptors are often human-engineered, meaning they are calculated based on expert knowledge of peptide chemistry.
• Interpretability: Due to their relative simplicity, traditional ML methods can sometimes infer biological insights, for example, by analyzing Shapley Additive exPlanations (SHAP values) to understand feature importance for different bacterial types.
• Examples:
  • Macrel: A random forest model trained on an unbalanced dataset (mimicking genomic distribution) for AMP and toxicity prediction. It was used successfully in the AMPSphere study.
  • AmPEPpy: Another RF-based tool for AMP prediction.
• Performance: The paper notes that these methods can achieve on-par or even better performance than more sophisticated deep learning approaches for certain tasks, particularly due to their simplicity and robustness when data is limited.

Deep Learning (DL) Models

Deep learning (DL) models, with their ability to learn complex patterns and features automatically, offer increased effectiveness for more challenging prediction tasks.

• Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTMs):
  • Function: RNNs and their variant, LSTMs, are well-suited for processing sequential data like peptide amino acid sequences. They maintain a hidden state that allows them to remember information from previous elements in the sequence, capturing backward and forward relationships.
  • Application: Used for AMP prediction, activity, and toxicity.
  • Examples: Many models incorporate LSTM or BiLSTM (bidirectional LSTM) architectures, such as AMPlify and ESKAPEE-MICpred.
• Convolutional Neural Networks (CNNs):
  • Function: While originally developed for image processing, CNNs are effective for sequence-derived features by using filters (or kernels) to detect local patterns (motifs) within the amino acid sequence.
  • Application: Used for AMP prediction and activity.
  • Examples: MBC-Attention combines a multibranch CNN with an attention mechanism to regress MIC values. AMPpred-MFA uses CNNs alongside Bi-LSTMs.
• Attention Mechanisms:
  • Function: Attention mechanisms allow models to focus on the most relevant parts of an input sequence when making a prediction. They assign weights to different parts of the sequence, indicating their importance.
  • Integration: Often integrated into RNN, LSTM, and CNN architectures to enhance contextual understanding of peptide semantics.
  • Examples: sAMPred-GAT, AMPlify, AMPpred-MFA, MBC-Attention.
• Quantum Support Vector Machine (QSVM):
  • Function: A quantum computing-inspired approach to SVM, potentially offering advantages for complex datasets by leveraging quantum principles.
  • Application: Proposed by Zhuang and Shengxin for peptide toxicity detection based on sequence-derived descriptors.

Large Language Models (LLMs) Applied in Discriminative Methods

Large Language Models (LLMs) based on the Transformer architecture have revolutionized sequence analysis, including for proteins and peptides.

• Protein Language Models (PLMs):

  • Process: PLMs are transformer models pretrained on a large corpus of proteins (e.g., UniRef, BFD) in a generative task (e.g., masked language modeling, predicting the next amino acid). They learn highly expressive embeddings (numerical representations) of protein sequences.
  • Fine-tuning: After pretraining, the PLM is fine-tuned for specific downstream tasks (function, property, structure prediction) by adding a prediction head (e.g., a simple Multi-layer perceptron (MLP)) and training on task-specific data.
  • Application: Used to predict antimicrobial activity, nontoxicity, solubility, and secondary structure of peptides.

• Challenges for Peptides: Directly applying PLMs (trained on longer proteins) to shorter peptides can lead to models biased towards protein-like properties. Models trained on shorter sequences (peptides or "chopped proteins") yield more generalized embeddings and perform better for peptide-specific tasks.

• Architectures:

  • BERT (Bidirectional Encoder Representations from Transformers): The most prevalently used LLM architecture, effective for long-distance dependencies and global context information. Many models use BERT (or ProtBert, a BERT specifically pretrained on protein sequences).
  • ESM (Evolutionary Scale Modeling) encoders: Another type of encoder-only architecture that integrates sequence and evolutionary information.
  • Encoder-Decoder Architectures: Full encoder-decoder transformer architectures (e.g., ProfTrans, OntoProtein) have shown to outperform encoder-only models in some AMP prediction tasks.

• Pretraining Corpus: The choice of pretraining data significantly influences performance.

  • Most methods use UniRef50 (a database clustered at 50% sequence identity, offering more diversity).
  • Fewer use UniRef100 (100% identity, less diverse).
  • Others use Pfam, BFD, UniProt, or merged corpuses.

• Additional Fine-tuning: Some approaches include an additional phase of fine-tuning on specific data (e.g., secretory data for toxicity prediction, or data for sequences shorter than 50 amino acids) to better align the model's focus with peptide-like distributions.

  The following Table 1, from the original paper, provides an overview of various discriminative methods, summarizing their frameworks, feature types, tasks, and experimental validation:

The following are the results from Table 1 of the original paper:

*GNN: Graph Neural Network; ATT: attention mechanism, MLP: Multi-layer perceptron, LSTM: Long Short-Term Memory, CNN: Convolutional Neural Network, LGBM: Light Gradient-Boosting Machine, RF: Random Forest, RNN: Recurrent Neural Network, GRU: Gated Recurrent Unit, SVM: Supporting Vector Machine, QSVM: Quantum Supporting Vector Machine, GPT-3: Generative Pre-trained Transformer 3, BERT: Bidirectional Encoder Representations from Transformers, ESM: Evolutionary Scale Modeling, DF: Deep Forest, Bi-LSTM: Bi-directional Long Short-Term Memory.

4.2.3. Representations of Peptides

The input representation of peptides is critical for discriminative models:

• Amino Acid Sequence: The most prevalent representation. It can be directly fed into models like RNNs or LSTMs.
• Sequence-Derived Descriptors: Calculated features from the sequence (e.g., net charge, hydrophobicity, amino acid frequency, secondary structure propensity). Used by traditional ML models and can complement DL models. SenseXAMP improved performance by fusing PLM embeddings with traditional protein descriptors (PD).
• PLM Embeddings: Numerical vectors generated by Protein Language Models that capture contextual and semantic information about the amino acid sequence. These often outperform human-engineered features.
• Image Conversion: Some approaches convert sequences into image-like representations (e.g., using cellular automata or atom connectivity information) and then apply CNNs.
• Structural Information: Incorporating structural data provides a complementary view.
  • Graph-based approaches: Represent peptides as graphs where amino acids are nodes and their interactions are edges. sAMP-pred-GAT uses a Graph Attention Network (GAT) with structural, sequence, and evolutionary information. AMPredictor uses a Graph Convolutional Net with Morgan fingerprints (chemical structure descriptors) and peptide contact maps.
  • Multiview contrastive learning: PepHarmony merges sequence-level encoding (from ESM) with structure-level embedding (from GearNet).

4.3. AMP Mining

AMP mining involves applying discriminative methods to large biological sequence datasets to identify potential AMPs. This approach focuses on finding realistic peptides that are likely to be produced in nature.

4.3.1. Biological Sequence Collections Amenable for AMP Mining

The success of AMP mining heavily relies on the availability of vast biological sequence data:

• Genomes: Complete genetic information of organisms.
• Proteomes: The entire set of proteins expressed by an organism.
• Metagenomes: The collection of genomic material directly recovered from environmental samples, representing the genetic diversity of microbial communities.
• Public Databases: Resources like GMCGv1 (Global Microbial Gene Catalogue, with billions of open reading frames/ORFs from metagenomes), GMSC (Global Microbial Small protein Catalogue, for small ORFs/smORFs), UniProt, and NIH HMP (Human Microbiome Project) provide rich data sources.

4.3.2. AMP Mining of Genomes and Proteomes

This involves screening organism-specific genetic or protein data:

• Human Proteome Mining (Torres et al.):
  • Method: An algorithm utilized key physicochemical properties (sequence length, net charge, average hydrophobicity) to predict antimicrobial activity. It modeled antimicrobial potency as being linearly dependent on physicochemical properties raised to exponents.
  • Application: Scanned 42,361 protein sequences from the human proteome.
  • Outcome: Identified 2,603 potential AMP candidates, many previously unrecognized, which were experimentally validated and showed efficacy in animal models. This approach focused on physicochemical characteristics rather than known AMP motifs to discover novel antimicrobials.
• Molecular De-Extinction (Maasch et al., Wan et al.):
  • Concept: Applied AI to explore proteins from extinct species (e.g., Neanderthals, Denisovans, woolly mammoth) as a source of novel antimicrobials.
  • Methods:
    • panCleave: A random forest model for proteome-wide cleavage site prediction.
    • Consensus of six publicly available traditional ML-based AMP models: Used for candidate AMP selection, including Macrel.
    • APEX: A deep learning model used to mine extinct organisms.
  • Outcome: Led to the discovery of novel AMPs like neanderthalin-1, mammuthusin-2, and elephantin-2, which are now preclinical candidates. This drastically accelerates discovery from years to hours.
• Phage Peptidoglycan Hydrolases (PGHs)-derived Peptides (Wu et al.):
  • Method: A computational pipeline to mine AMPs from ESKAPE microbes (a group of dangerous pathogens) and their associated phages.
  • Model: A CNNs and LSTM layer-based model (similar to Ma et al.) evaluated antibacterial activity.
  • Outcome: Created ESKtides, a database of over 12 million peptides with predicted high antibacterial activity.

4.3.3. AMP Mining of the Microbiome

Microbiomes (collections of microorganisms in an environment) are rich sources for AMPs:

• Human Gut Microbiome (Ma et al.):
  • Method: Used deep learning techniques including LSTM, attention, and BERT to mine the human gut microbiome.
  • Outcome: Identified 181 peptides with antimicrobial activity, many with low homology to known AMPs, showing efficacy against antibiotic-resistant, Gram-negative bacteria in a mouse model.
  • Anticancer Peptide (ACP) Prediction: Leveraging the overlap between ACPs and AMPs, another study identified 40 potential ACPs from gut metagenomic data, with 39 showing anticancer activity in cell lines and two reducing tumor size in a mouse model without toxicity.
• Global Microbiome (Santos-Junior et al.):
  • Method: Used machine learning to analyze 63,410 metagenomes and 87,920 microbial genomes, incorporating proteomics and transcriptomics data as a filtering step.
  • Outcome: Nearly one million new potential AMPs were computationally predicted and deposited in the AMPSphere database.
• Other Microbiomes:
  • Freshwater Polyp Hydra (Klimovich et al.): Used high-throughput transcriptome and genome sequencing with ML-based analysis to reveal rapid evolution and spatial expression patterns of AMPs in Hydra's microbiome.
  • Cockroach Gut Microbiome (Chen et al.): A deep learning model with Dense-Net blocks and a self-attention module was used to study the gut microbiome of cockroaches.

4.3.4. Exhaustive Mining of Combinatorial AMP Sequence Spaces for Short Peptides

Instead of natural sources, some efforts evaluate all possible short peptide sequences:

• Hexapeptides, Heptapeptides, Octapeptides (Huang et al., Ji et al.):
  • Method: A machine-learning-based pipeline systematically identified AMPs from vast virtual libraries of short peptides (6-9 amino acids). The pipeline involved multiple sequential machine-learning modules for filtering, classification, ranking, and efficacy prediction.
  • Discriminator Refinement: The discriminators were trained on the GRAMPA dataset and refined using a two-step experimental validation strategy to mitigate biases.
  • Outcome: Identified potent hexapeptides effective against multidrug-resistant pathogens, comparable to penicillin in mice, with low toxicity. Another study focused on Acinetobacter baumannii specific AMPs using few-shot learning due to scarce training data.

4.4. AMP Generation

AMP generation involves creating novel peptide sequences by learning from existing data, with the goal of designing idealistic peptides optimized for specific properties.

4.4.1. Modeling Frameworks Employed in AMP Generation

Various generative AI frameworks have been applied:

• Variational Autoencoders (VAEs) and Wasserstein Autoencoders (WAEs):
  • Principle: Autoencoders learn to encode input data into a lower-dimensional latent space and then decode it back to the original data. VAEs add a probabilistic twist, learning a distribution over the latent space, enabling the generation of new samples by sampling from this learned distribution. WAEs improve stability by using Wasserstein distance in the loss function.
  • Application: Widely used for AMP generation. HydrAMP and CLaSS are examples using cVAE and WAE respectively.
• Generative Adversarial Networks (GANs):
  • Principle: Consist of two neural networks: a generator that creates new data samples, and a discriminator that tries to distinguish real data from generated data. They are trained adversarially, with the generator trying to fool the discriminator and the discriminator trying to correctly identify fakes.
  • Application: Used to generate novel AMP sequences. AMP-GAN, AMPGAN v2, Multi-CGAN, and PandoraGAN are examples.
• Autoregressive Models (e.g., LSTMs):
  • Principle: Predict the next element in a sequence based on the preceding elements. While explored, they are less frequently used for de novo generation compared to VAEs and GANs. AMPTrans-LSTM is an example combining LSTM with a transformer.

4.4.2. Controlled AMP Generation

A major goal is to direct the generation process to acquire desired properties:

• Auxiliary Discriminators:
  • Principle: Train separate discriminative models to predict desired properties (e.g., activity, non-toxicity). The generative model then produces candidates, which are filtered or guided by these discriminators.
  • Examples:
    • CLaSS: Discriminative models trained on the latent space of a WAE guide generation towards peptides with targeted activity and toxicity.
    • PandoraGAN: Uses positive-only learning, meaning only highly active peptides are used for training, implicitly guiding generation.
    • Zeng et al., Pandi et al., Cao et al., Diff-AMP, Capecchi et al., ProT-Diff also use discriminator-guided filtering.
• Conditional Variants (cGANs, cVAEs):
  • Principle: These models allow conditioning the generation on specific properties (e.g., target sequence length, microbial target, desired activity range, toxicity profile) during the training or generation phase.
  • Examples:
    • AMP-GAN, AMPGAN v2: Condition on sequence length, microbial target, target mechanism, activity.
    • Multi-CGAN: Optimizes generation for multiple properties simultaneously (activity, nontoxicity, structure).
    • M3-CAD (Multimodal, Multitask, Multilabel cVAE): Targets eight feature categories, including predicted 3D structure, species-specific antimicrobial activities, mechanisms of action, and toxicity.
    • HydrAMP: A cVAE that generates highly active AMPs by conditioning on low MIC values. It includes a pretrained classifier to ensure desired properties, uses loss function terms for training stability and latent space matching, and offers analogue generation with a creativity parameter. It can improve both known and non-active peptides.
• Latent Space Sampling:
  • Principle: The latent space of VAEs or WAEs is a continuous representation where similar peptides are close together. Sampling from specific regions of this space can generate peptides with desired attributes.
  • Examples:
    • LSSAMP: Discretizes the latent representation to encode sequence and structural information, facilitating generation of peptides with desired secondary structures.
    • Renaud and Mansbach: Uses latent space sampling for AMP and hydrophobicity.
• Direct Optimized Generation:
  • Principle: Instead of indirect guidance, these methods directly optimize generation using tailored cost functions or search algorithms.
  • Examples:
    • QMO: Uses zeroth-order gradient optimization to navigate the latent space.
    • Active learning with GFlowNets: Jain et al.
    • Quantum annealing: MOQA uses a D-wave quantum annealer with a binary VAE for activity and non-toxicity.
    • Bayesian optimization: MODAN for optimized generation.
    • Evolutionary algorithms: AMPEMO for AMP and diversity.

4.4.3. Large Language Models (LLMs) Applied in AMP Generation

The success of LLMs in text generation has led to their application in AMP generation:

• Architectures:

  • Decoder-like architectures: Similar to GPT (Generative Pre-trained Transformer), used for protein design.
  • Diffusion processes: Trained on continuous embeddings obtained from pretrained PLMs.

• Controlled Design: So far, LLM-based generation has largely relied on simpler strategies for controlled design, such as positive-only learning or discriminator-guided filtering.

• Contrastive Learning: A promising direction is contrastive learning, as in MMCD, where a diffusion-based model is trained by contrasting embeddings of known positive AMP examples with negative ones to improve the learned distribution.

  The following Table 3, from the original paper, provides an overview of various generative methods, summarizing their generation mode, controlled generation techniques, aimed properties, frameworks, and experimental validation:

The following are the results from Table 3 of the original paper:

The paper includes Table 2: Mining approaches for AMD discovery, which appears to have corrupted or placeholder text in some cells. I will transcribe it as provided, noting the presence of placeholder text.

The following are the results from Table 2 of the original paper:

Note: Table 2 in the original paper appears to contain placeholder or corrupted text in many of its cells, particularly in the columns for 'model function', 'proforemen', 'biophysical sequerce source', 'lotsiblogicalfrontill', and the three 'activity' columns. It has been transcribed exactly as presented.

5. Experimental Setup

As a review paper, this document synthesizes findings from numerous individual research studies rather than presenting a single, unified experimental setup. However, it extensively discusses the types of datasets, evaluation metrics, and comparative baselines used in the field of AI-driven AMP discovery.

5.1. Datasets

The datasets used in AI-driven AMP discovery span various biological sequence sources and curated collections:

• Biological Sequence Collections for Mining:

  • Genomes, Proteomes, and Metagenomes: These are the primary raw data sources for AMP mining. Examples mentioned include:

    • Human proteome: Used by Torres et al. to identify 2,603 potential AMP candidates. A data sample would be a protein sequence from the human body, e.g., "MGLSQPK..."
    • Proteomes of extinct species: Such as Neanderthals, Denisovans, and woolly mammoth. A data sample would be a predicted protein sequence from ancient DNA, e.g., "AQGWVL..."
    • Global Microbial Gene Catalogue (GMCGv1): Contains billions of open reading frames (ORFs) from thousands of metagenomes across numerous habitats. A data sample might be a DNA sequence encoding a small protein, e.g., "ATGGCGTTAG..."
    • Global Microbial Small protein Catalogue (GMSC): Derived from thousands of publicly available metagenomes and isolate genomes, containing nearly a million nonredundant smORFs (small open reading frames).
    • Human gut microbiome: Used by Ma et al. and others. A data sample could be a metagenomic sequence from a human gut sample.
    • Microbiomes of other organisms: E.g., freshwater polyp Hydra, cockroaches.

  • DBAASP (Database of Antimicrobial/Cytotoxic Activity and Structure of Peptides): A key database of experimentally verified AMPs, providing sequences and associated activity/toxicity data. This serves as a primary source for training discriminative models.

  • GRAMPA dataset: A compiled collection of Minimum Inhibitory Concentration (MIC) measurements, used to train discriminator models in studies focusing on exhaustive mining of short peptide spaces.

  • AMPSphere database: A repository for computationally predicted new AMPs, particularly from global microbiome analyses.

    These datasets are chosen because they represent the vast natural diversity of peptides and the genetic potential for producing them. They are effective for validating methods by providing a rich source of known and unknown sequences, allowing for both the identification of existing AMPs and the training of models to generate novel ones.

5.2. Evaluation Metrics

The paper discusses several critical evaluation metrics used to assess the efficacy and safety of AMPs, both in computational prediction and experimental validation.

• Minimum Inhibitory Concentration (MIC):

  1. Conceptual Definition: The Minimum Inhibitory Concentration (MIC) is the lowest concentration of an antimicrobial agent (like an AMP) that prevents visible growth of a microorganism after a standard incubation period (e.g., 18-24 hours). It is a fundamental measure of the antimicrobial potency of a substance. A lower MIC value indicates higher antimicrobial activity.
  2. Mathematical Formula: MIC is an experimentally determined value and does not have a single calculation formula. It is typically found through dilution methods where a range of antimicrobial concentrations are tested against a microbial culture. The concentration in the first well/tube where no visible microbial growth is observed is reported as the MIC.
  3. Symbol Explanation: N/A (as it's an experimentally determined value rather than a formula).

• Hemolytic Activity:

  1. Conceptual Definition: Hemolytic activity refers to the ability of a substance to cause hemolysis, which is the rupture or destruction of red blood cells (erythrocytes). It is a crucial measure of an AMP's potential toxicity to mammalian cells, as high hemolytic activity indicates a lack of selectivity for bacterial cells.
  2. Mathematical Formula: Hemolytic activity is typically quantified as the percentage of red blood cells lysed. The standard formula involves measuring the absorbance of hemoglobin released from lysed cells. $ \text{Hemolysis (%) } = \frac{A_{\text{sample}} - A_{\text{negative control}}}{A_{\text{positive control}} - A_{\text{negative control}}} \times 100 $
  3. Symbol Explanation:
     • $A_{\text{sample}}$: Absorbance of the supernatant from the sample (peptide-treated red blood cells), indicating hemoglobin release.
     • $A_{\text{negative control}}$: Absorbance of the supernatant from red blood cells incubated without the peptide (e.g., in PBS buffer), representing spontaneous lysis.
     • $A_{\text{positive control}}$: Absorbance of the supernatant from red blood cells completely lysed (e.g., with a detergent like Triton X-100 or distilled water), representing 100% lysis.

• Cytotoxicity:

  1. Conceptual Definition: Cytotoxicity is the quality of being toxic to cells. In the context of AMPs, it refers to the ability of a peptide to induce damage or death in various types of mammalian cells (e.g., fibroblasts, epithelial cells), beyond just red blood cells. It is another key indicator of an AMP's safety profile.
  2. Mathematical Formula: Cytotoxicity is often measured indirectly through cell viability assays (e.g., MTT, MTS, WST-1 assays). These assays quantify metabolic activity or membrane integrity of living cells. The formula typically calculates the percentage of viable cells relative to untreated control cells. $ \text{Cell Viability (%) } = \frac{A_{\text{sample}} - A_{\text{background}}}{A_{\text{untreated control}} - A_{\text{background}}} \times 100 $ And then, $Cytotoxicity ($.
  3. Symbol Explanation:
     • $A_{\text{sample}}$: Absorbance (or fluorescence/luminescence, depending on the assay) from cells treated with the peptide.
     • $A_{\text{background}}$: Absorbance from the assay medium without cells (for background subtraction).
     • $A_{\text{untreated control}}$: Absorbance from untreated cells, representing 100% viability.

5.3. Baselines

The paper implicitly compares AI-driven methods against several baselines:

• Traditional Antibiotics: The overall discovery void and escalating resistance to traditional antibiotics serve as the overarching problem that AI-driven AMP discovery aims to address.

• Traditional Experimental Discovery: The laborious and time-consuming nature of empirical screening or rational design is the fundamental baseline for the speed and scale of discovery.

• Traditional Machine Learning (ML) Methods: Within the computational domain, deep learning (DL) and Large Language Models (LLMs) are often compared against traditional ML methods (e.g., decision trees, SVMs, Random Forests) that rely on human-engineered features. The paper notes that DL can offer increased effectiveness for complex challenges and improve prediction accuracy.

• Earlier AI Models: For generative AI, newer, more advanced models (cVAEs, cGANs, LLM-based diffusion models) are implicitly compared against earlier or simpler generative approaches that might lack controlled generation capabilities or multi-objective optimization.

• Brute-Force Search: The sheer impossibility of brute-force searching the combinatorial sequence space ($10^{32}$ for short peptides) highlights the necessity and efficiency of AI.

  The representativeness of these baselines is high because they define the current state-of-the-art or historical limitations that the new AI approaches aim to overcome. They demonstrate the advancements in capability, efficiency, and the ability to handle complexity that AI brings to AMP discovery.

6. Results & Analysis

The paper, being a review, synthesizes the collective results and successes from numerous studies on AI-driven AMP discovery rather than presenting new experimental data. The core message is that AI has already achieved significant breakthroughs, demonstrating strong validation for its effectiveness.

6.1. Core Results Analysis

The main experimental results highlighted across the reviewed studies strongly validate the effectiveness of proposed AI methods:

• Accelerated Discovery: AI has dramatically accelerated the process of identifying preclinical candidates, reducing the time from years to hours. This is a crucial advantage compared to traditional antibiotic discovery methods which have not kept pace with evolving resistance.
• Identification of Novel AMPs from Diverse Sources:
  • Human Proteome: Torres et al. identified 2,603 potential AMP candidates from the human proteome, many previously unrecognized, with some demonstrating in vitro and in vivo efficacy. This highlights the ability of AI to mine encrypted peptide antibiotics.
  • Extinct Organisms (Molecular De-Extinction): Studies using models like APEX led to the discovery of novel AMPs (e.g., neanderthalin-1, mammuthusin-2, elephantin-2) from extinct species, which are now preclinical candidates. This showcases AI's ability to uncover therapeutic molecules from previously inaccessible biological sources.
  • Microbiome:
    • Ma et al. identified 181 peptides from the human gut microbiome using deep learning, many with less than 40% sequence homology to known AMPs. These showed significant efficacy against antibiotic-resistant, Gram-negative bacteria and reduced bacterial load in a mouse model of lung infection.
    • A global microbiome analysis using machine learning led to the discovery of nearly one million new potential AMPs, deposited in the AMPSphere database.
    • Similar mining efforts in other microbiomes (e.g., Hydra, cockroaches) revealed novel AMPs and their ecological roles.
• Generation of Potent Synthetic Peptides:
  • HydrAMP (Szymczak et al.) discovered 15 novel, highly potent AMPs that were active against several bacterial strains, including multidrug-resistant strains, and were experimentally validated for activity and toxicity. This demonstrates the power of generative models to design AMPs with enhanced properties.
  • Studies on exhaustive mining of short peptide spaces (e.g., 6-9 amino acids) identified potent hexapeptides with efficacy comparable to penicillin against multidrug-resistant pathogens in mice and low toxicity.
• Validation of Discriminative Models: Many discriminative models have shown strong predictive capabilities, enabling the filtering and ranking of potential AMPs. While not all models undergo experimental validation, those that do (e.g., AMPlify, AMP-META, APEX, ESKAPEE-MICpred) confirm their utility in identifying active and sometimes non-toxic candidates.
• In Vitro and In Vivo Efficacy: A significant number of AI-discovered AMPs have been experimentally validated through microbiological assays (e.g., MIC measurements), hemolysis assays, cytotoxicity assays, and crucial in vivo animal models, indicating real-world therapeutic potential.

Advantages and Disadvantages Compared to Baselines:

• Advantages:
  • Speed and Scale: AI significantly outperforms traditional discovery in terms of the number of candidates screened or generated and the time required.
  • Novelty: AI can identify and design peptides with novel sequences and mechanisms that might be missed by traditional methods, which often focus on known motifs.
  • Optimization: Generative AI allows for the optimization of multiple properties (activity, toxicity, structure) simultaneously, leading to better-balanced candidates.
  • Data Exploitation: AI can effectively learn from and leverage vast amounts of diverse biological sequence data that would be intractable for human analysis.
• Disadvantages/Challenges (as highlighted in the paper's limitations section):
  • Data Scarcity for Specific Tasks: Despite large generic datasets, high-quality, labeled data for specific tasks (e.g., toxicity prediction, strain-specific activity for multidrug-resistant strains, negative examples) remains limited.
  • Reliability of Toxicity Predictors: Toxicity prediction is still less reliable than activity prediction.
  • Generalizability: The robustness and generalizability of models across different independent datasets require more objective evaluation.
  • Handling Modified Peptides: Current models primarily focus on linear peptides with the 20 canonical amino acids, limiting their applicability to complex chemically modified or nonribosomal peptides already in clinical use.
  • Clinical Translation Gap: While many AMPs are validated in preclinical animal models, very few have progressed to human clinical trials.
  • Evaluation of Generative Models: Benchmarking generative models is difficult, as diversity, novelty, and similarity to training data are often proxies for true activity/toxicity.

6.2. Data Presentation (Tables)

The following are the results from Table 1 of the original paper:

Table 1 provides a comprehensive overview of various discriminative methods, highlighting the transition from traditional ML to deep learning and LLM-based approaches. It shows a growing trend towards using pLM embeddings and attention mechanisms. Notably, only a subset of these methods include experimental validation, with even fewer extending to in vivo animal models, indicating a gap between computational prediction and experimental verification.

The following are the results from Table 2 of the original paper:

Table 2, Mining approaches for AMD discovery, appears to contain corrupted or placeholder text in many cells. As such, it is difficult to extract specific comparative performance analysis from this table. However, it implicitly aims to categorize different mining tools and their applications, emphasizing the diverse sources (e.g., 'biophysical sequerce source') and target activities for discovery.

The following are the results from Table 3 of the original paper:

Table 3 illustrates the diversity of generative models and their applications. A key observation is the shift from purely unconstrained generation to controlled generation methods, often involving conditional generation or discriminator-guided filtering, to steer the models towards desired properties like activity and nontoxicity. While many studies report microbiological assays, fewer have progressed to in vivo animal models, indicating the higher bar for validation in generative approaches. The 'MD' column implies whether Molecular Dynamics simulations were used, which can provide additional descriptors for activity prediction or validation.

6.3. Ablation Studies / Parameter Analysis

The review does not detail specific ablation studies or parameter analyses from individual papers, as it provides a broad overview of the field. However, it implicitly points to the importance of such analyses through discussions on:

• Feature Types: The comparison between sequence-derived descriptors, PLM embeddings, and the fusion of both (as in SenseXAMP) indicates an ongoing analysis of which input representations yield the best performance.

• Architectural Choices: The discussion on the superiority of full encoder-decoder transformer architectures over encoder-only models for AMP classification (Dee vs. Elnaggar et al.) suggests internal benchmarking and architectural ablation studies within the community.

• Pretraining Corpuses: The observation that more diverse corpuses (e.g., UniRef50 over UniRef100) improve model performance without architectural changes indicates that studies have analyzed the impact of pretraining data.

• Fine-tuning Strategies: The mention of additional fine-tuning phases (e.g., using secretory data for toxicity, or data for shorter peptides) highlights the effectiveness of specialized training steps, implying that the impact of these steps has been investigated.

• Creativity Parameter in HydrAMP: The HydrAMP model explicitly features a creativity parameter that controls the diversity of generated analogues. This is a form of parameter analysis, where different creativity levels would lead to varied outcomes, allowing researchers to balance novelty with similarity to known AMPs.

• Conditional Generation Parameters: The conditioning parameters in models like cGANs and cVAEs (e.g., sequence length, microbial target, MIC values) are critical hyperparameters whose influence on generation quality would be analyzed in individual studies.

  These discussions collectively underscore the community's efforts to understand and optimize the components and parameters of AI models for AMP discovery, even if the specific experimental details are not provided in this high-level review.

7. Conclusion & Reflections

7.1. Conclusion Summary

The paper provides a comprehensive overview of how artificial intelligence (AI) is poised to revolutionize the discovery of antimicrobial peptides (AMPs), offering new hope in the global fight against antimicrobial resistance (AMR). It effectively categorizes AI applications into two core strategies: AMP mining and AMP generation, both underpinned by advanced discriminative models.

• AMP Mining: This strategy leverages AI to scan vast biological sequence data (genomes, proteomes, metagenomes) to identify naturally occurring AMP candidates. It has led to the discovery of thousands to millions of novel AMPs from diverse sources, including the human proteome, extinct organisms, and various microbiomes, with several candidates demonstrating preclinical efficacy.

• AMP Generation: This strategy employs generative AI models (VAEs, GANs, LLMs) to de novo design entirely new peptide sequences, optimized for desired properties like high activity and low toxicity. Advanced techniques like conditional generation and latent space sampling enable targeted design, leading to the discovery of highly potent synthetic AMPs like those from HydrAMP.

• Transformative Impact: AI significantly accelerates the drug discovery process, transforming tasks from years to hours, and enables the discovery of AMPs with unprecedented novelty and properties. The integration of deep learning and Large Language Models (LLMs) has been instrumental in refining prediction accuracy and design capabilities.

  The paper concludes that the synergy between AI and AMP discovery represents a critical frontier in developing next-generation antimicrobial therapies, advocating for sustained integration of AI into biomedical research.

7.2. Limitations & Future Work

The authors are rigorous in outlining the current limitations and suggesting future research directions:

7.2.1. Challenges to Be Addressed in the Realm of Discriminative Models

• Data Scarcity:
  • Limited Data Volume: The relatively small number of experimentally validated AMPs hinders model development. Transfer learning and pretrained LLMs offer partial solutions, but more data sharing and experimental validation initiatives are needed.
  • Toxicity Data: There's a severe lack of training data for predicting hemolytic activity and cytotoxicity, leading to suboptimal performance, especially for cytotoxicity.
  • Multiresistant Strains: Insufficient data for multiresistant strains makes training strain-specific activity predictors difficult.
• Lack of Experimentally Validated Negative Examples: There's little incentive to generate negative data (non-AMPs or inactive peptides), which poses a significant challenge for supervised learning. Peptides can also falsely appear negative due to technical issues. Solutions involve modifying loss functions (e.g., asymmetric loss), data sampling procedures, and standardizing experimental conditions for collecting both positives and negatives.
• Limited Structural Information: Full use of peptide structural information (secondary, tertiary structures, post-translational modifications) for functional prediction is limited by data scarcity. Available structures are often obtained without considering physiological contexts (e.g., membrane proximity, self-association).
• Lack of Robustness and Generalizability Evaluation: Existing deep learning models often lack objective evaluation on external independent datasets.
• Inapplicability to Modified Peptides: Current discriminative methods are limited to linear peptides with canonical amino acids. They cannot effectively handle noncanonical building blocks (cycles, $\beta$-amino acids, modified cysteines, lipid attachments) or nonribosomal peptides, which are clinically relevant (e.g., polymyxins). More data on these complex peptides is needed.
• Missing Important Properties: Lack of training data for crucial properties like in vivo half-lives and ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) properties prevents training better classifiers. Large experimental studies or adaptation of small molecule ADMET prediction methods (with caution due to size differences) are suggested.

7.2.2. Challenges to Be Addressed in AMP Mining

• Dependence on Discriminative Methods' Limitations: As mining relies on discriminative methods, their limitations (e.g., inability to detect complex modified peptides) directly transfer.
• Underutilization of Genomic Context: Current mining approaches often process sequences independently, overlooking valuable information in genomic context and natural variations. Integrating natural language processing (NLP) techniques for gene function prediction and using multisequence alignments are promising.
• Limited Data Types: Mining could benefit from integrating additional data types beyond genomics and proteomics, such as transcriptomics and ribosomal sequencing data, though these are less abundant.
• Exploiting Biosynthetic Gene Clusters: Leveraging biosynthetic gene clusters (BGCs) could be beneficial for AMP discovery, as many peptides are encoded by single genes or derived from precursor proteins.
• Lack of Clinical Translation: While preclinical testing has been successful, no AI-discovered AMPs have yet reached clinical studies.

7.2.3. Challenges to Be Addressed in AMP Generation

• Evaluation and Benchmarking: Difficult due to measuring diversity, novelty, and similarity to training data as proxies, rather than direct experimental validation for all generated peptides. The choice of auxiliary discriminators is arbitrary, making comparisons difficult.
• Efficient Candidate Ranking: Generative AI can produce thousands of candidates, requiring efficient methods to rank top candidates beyond extensive filtering and expert knowledge.
• Low Data Availability: Generative models also suffer from limited data, and generating out-of-distribution examples (potent peptides beyond current knowledge) is a recognized challenge.
• Limited to 20 Amino Acids: Most generative models work only with the 20-letter amino acid alphabet, neglecting post-translational modifications or nonstandard amino acids, which are critical for the full complexity and potency of therapeutic peptides. While rational design can add modifications later, direct generation would be ideal.
• Limited Preclinical/Clinical Validation: Fewer generative AI-derived AMPs have undergone in vivo preclinical testing compared to mining, and none have reached clinical trials. This highlights the need for collaborative efforts between AI, chemistry, biology labs, and industrial partners.
• Model Suitability: Many emerging generative AI methods are developed for text/images and may not be optimally suited for peptides without specific modeling extensions for controlled generation.

7.3. Personal Insights & Critique

This review paper provides an excellent, timely, and comprehensive synthesis of the rapidly evolving field of AI-driven AMP discovery. Its clear distinction between mining and generation strategies, coupled with a detailed breakdown of underlying discriminative models, offers a valuable framework for understanding the state-of-the-art.

Strengths:

• Beginner-Friendly yet Deep: The paper successfully introduces complex AI concepts in the context of biology, making it accessible while maintaining academic rigor. The structured presentation of methods and challenges is highly informative.
• Comprehensive Coverage: It covers a wide range of AI techniques, from traditional ML to advanced LLMs and various generative architectures, demonstrating the breadth of innovation.
• Emphasis on Practical Impact: The consistent mention of experimental validation (in vitro and in vivo) throughout the paper underscores the practical, translational potential of these AI methods, moving beyond theoretical advancements to real-world solutions for AMR.
• Highlighting Key Challenges: The dedicated section on Limitations & Future Work is particularly strong, providing a realistic assessment of the field's hurdles. The detailed discussion of data scarcity (especially for toxicity and negative examples), the need for structural information, and the gap in handling modified peptides are crucial insights.

Potential Issues/Areas for Improvement (as derived from the paper's own critique and broader understanding):

• The "Black Box" Problem: While AI models offer unprecedented predictive power, many deep learning models, especially LLMs, can be opaque. The paper implicitly acknowledges this by mentioning interpretability for traditional ML but doesn't extensively discuss how to make complex DL/LLM-based AMP predictions more interpretable, which is important for drug development (e.g., understanding mechanism of action).
• Data Quality and Bias: The paper rightly points out data scarcity, but data quality and potential biases in existing AMP databases (e.g., publication bias towards positive results, varied experimental conditions) are also critical. AI models are only as good as the data they are trained on. The problem of lack of incentive to generate negative data is a systemic issue.
• Reproducibility: With the complexity of models and datasets, ensuring reproducibility of AI-driven discoveries can be challenging. Standardized benchmarks and open-source implementations are crucial.
• Generalizability Across Pathogens: While some models address strain-specific activity, truly generalizable AMPs effective against a broad spectrum of pathogens (including emerging ones) remain a major goal.
• Table 2 Issue: The corrupted Table 2 is a minor publication issue but hinders immediate understanding of mining tools.

Inspirations and Applications to Other Domains:

• The dual strategy of mining existing biological diversity and generating novel solutions is highly transferable to other biomolecule discovery problems (e.g., enzyme design, antibody design, drug lead discovery in other areas beyond antimicrobials).

• The application of LLMs and Transformer architectures to protein/peptide sequences is a paradigm shift with broad implications for protein engineering, functional annotation, and structural prediction in general.

• The methodologies for controlled generation (conditional generation, latent space manipulation, multi-objective optimization) are directly applicable to designing molecules with specific, desired properties in fields like materials science or catalyst discovery.

• The emphasis on integrating physicochemical properties, structural information, and evolutionary data alongside sequence information is a powerful approach for developing holistic AI models in biology.

• The molecular de-extinction concept is particularly inspiring, showcasing how AI can unlock therapeutic potential from unexpected and historical biological sources.

  Overall, this paper serves as an excellent guide for researchers, highlighting the immense potential of AI to accelerate the discovery of urgently needed new therapies, while also realistically addressing the scientific and technical hurdles that must still be overcome.