1. Bibliographic Information

• Title: Fine-tuning is Not Enough: Rethinking Evaluation in Molecular Self-Supervised Learning
• Authors: Anonymous authors (Paper under double-blind review).
• Journal/Conference: The paper was submitted to OpenReview, a platform commonly used for peer review by major machine learning conferences such as ICLR, NeurIPS, and ICML. The venue is reputable and known for high-quality, impactful research.
• Publication Year: The paper is presented as being under review, so a final publication date is not specified. However, the content and references (including one dated 2025) indicate it is a very recent work, likely from 2023 or 2024.
• Abstract: The authors argue that the prevailing method for evaluating molecular Self-Supervised Learning (SSL) models—simple fine-tuning performance—is insufficient for assessing true generalization. The molecular domain poses unique challenges, like high sensitivity to structural changes and the need to generalize across different chemical scaffolds. To address this, they propose a multi-perspective evaluation framework under a unified experimental setting. This framework assesses representation quality via linear probing, measures the actual benefit of pretraining with a Pretrain Gain metric, quantifies knowledge loss ("forgetting") during fine-tuning using parameter shifts, and analyzes model scalability with larger datasets. Their findings are surprising: some models show little to no benefit from pretraining in linear probing, Graph Neural Network (GNN) models undergo significant parameter changes during fine-tuning, and most models do not improve with more pretraining data. The paper concludes that current molecular SSL methods have notable weaknesses and calls for more rigorous evaluation.
• Original Source Link:

  • Official Page: https://openreview.net/forum?id=PNsYrA6CW2

  • PDF: https://openreview.net/pdf?id=PNsYrA6CW2

  • Publication Status: At the time of this analysis, the paper is a preprint under double-blind review.

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2. Executive Summary

• Background & Motivation (Why):

  • Core Problem: The evaluation of Self-Supervised Learning (SSL) models in the molecular domain has been dominated by a single, potentially misleading metric: fine-tuning performance on downstream tasks.
  • Importance & Gaps: The molecular domain demands robust generalization due to (1) high diversity in tasks (e.g., predicting toxicity, solubility), (2) extreme sensitivity of properties to minor structural changes, and (3) the use of scaffold splitting in benchmarks, which tests a model's ability to generalize to unseen molecular frameworks. Relying on fine-tuning is problematic because it modifies the entire pretrained model, making it unclear if good performance comes from the quality of the initial representations or from the model simply adapting heavily to the new, smaller dataset. Furthermore, prior studies used inconsistent experimental setups (different model sizes, datasets, prediction heads), making fair comparisons impossible.
  • Fresh Angle: This paper’s innovation is not a new SSL model but a new evaluation methodology. It proposes a standardized, multi-faceted framework to dissect the performance of existing molecular SSL models, providing a much deeper and more diagnostic analysis of what pretraining actually achieves.

• Main Contributions / Findings (What):

  • A Unified Experimental Setup: The authors create a controlled environment where various molecular SSL models are compared fairly. Key factors like hidden dimensions, downstream prediction heads, and datasets are standardized, isolating the pretraining strategy as the sole variable.
  • A Multi-Perspective Evaluation Framework: They introduce a suite of metrics to go beyond fine-tuning:
    1. Linear Probing: To assess the intrinsic quality and generalizability of the frozen pretrained representations.
    2. Pretrain Gain: A novel metric to quantify the performance improvement of a pretrained model over an identical, randomly initialized one.
    3. Parameter Shift: An analysis to measure how much the model's weights change during fine-tuning, serving as a proxy for catastrophic forgetting.
    4. Scalability Analysis: An investigation into whether models benefit from larger pretraining datasets, a key characteristic of successful SSL paradigms in other fields.
  • Key Discoveries: Their reassessment reveals several critical and unexpected weaknesses in the current state of molecular SSL:

    • Many models show low or even negative Pretrain Gain under linear probing, suggesting their representations are not inherently useful without full fine-tuning.

    • GNN-based models experience substantial parameter shifts, indicating significant forgetting of pretrained knowledge.

    • Most models show negligible scalability, with performance plateauing regardless of the pretraining dataset size.

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3. Prerequisite Knowledge & Related Work

• Foundational Concepts:

  • Self-Supervised Learning (SSL): A machine learning paradigm where a model learns representations from large amounts of unlabeled data. This is done by creating a "pretext task" where parts of the input are hidden, and the model must predict them (e.g., predicting a masked word in a sentence). This pretraining phase is followed by a downstream phase, where the learned model (encoder) is adapted for a specific task (e.g., classification) using a small amount of labeled data.
  • Molecular Representations:
    • Graph-based: Molecules are naturally represented as graphs, where atoms are nodes and chemical bonds are edges. This structure captures connectivity and spatial relationships.
    • Sequence-based (SMILES): Simplified Molecular-Input Line-Entry System (SMILES) is a string-based notation that represents a molecule's structure as a sequence of characters.
  • Graph Neural Networks (GNNs): A class of neural networks designed to operate on graph-structured data. They work via a mechanism called message passing, where each node iteratively aggregates information from its neighbors. This allows GNNs to learn representations that encode both local and global structural information.
  • Transformers: Originally designed for natural language processing, these models rely on a self-attention mechanism to weigh the importance of different parts of an input sequence. They have been adapted for molecular data, typically using SMILES strings as input.
  • Fine-tuning vs. Linear Probing:
    • Fine-tuning: The standard approach where the entire pretrained model (encoder and a new prediction head) is trained on the downstream task. All parameters are updated.
    • Linear Probing: A more diagnostic method where the pretrained encoder's weights are frozen. Only a simple, newly added linear prediction head is trained on the downstream task. This directly tests the quality of the learned representations.
  • Random vs. Scaffold Splitting:
    • Random Split: Data is randomly partitioned into training, validation, and test sets. In chemistry, this is often too easy, as structurally similar molecules can end up in both the training and test sets.
    • Scaffold Split: Molecules are grouped by their core chemical structure (scaffold). All molecules with the same scaffold are placed in the same set (train, validation, or test). This ensures the model is tested on its ability to generalize to fundamentally new types of molecules, providing a much more realistic evaluation of its capabilities.

• Previous Works & Pretext Tasks: The paper categorizes and evaluates several prominent molecular SSL models based on their pretext tasks:

  • Generation-based: Reconstructing masked parts of the input.
    • AttributeMask: Masks and predicts atom properties (nodes) in a molecular graph.
    • EdgePred: Masks and predicts the existence of bonds (edges) in the graph.
    • ChemBERTa: A Transformer model that masks tokens in a SMILES string and predicts them, similar to BERT in NLP.
  • Auxiliary Property-based: Predicting inherent chemical properties.
    • ContextPred: Learns by predicting if a local subgraph (neighborhood) and a broader context graph belong to the same central node.
  • Contrast-based: Learning to distinguish between similar and dissimilar samples.
    • GraphCL: Creates augmented "views" of a molecule (e.g., by masking nodes/edges) and trains the model to pull representations of views from the same molecule closer while pushing representations from different molecules apart.
    • GraphLoG: A hierarchical contrastive learning method that contrasts local instance representations with global "prototype" representations.
    • KANO: A contrastive method that augments molecular graphs with chemical knowledge from a knowledge graph and uses a "prompting" mechanism to align pretraining with downstream tasks.
  • Hybrid: Combining multiple pretext tasks.
    • GROVER: A hybrid Transformer-GNN model that combines generation (subgraph masking) and auxiliary property prediction (predicting chemical motifs).

• Differentiation: This paper's primary contribution is not a new SSL algorithm but a critical reassessment of the field's evaluation standards. While prior works focused on achieving state-of-the-art fine-tuning scores, this paper argues for a more holistic and fair evaluation, revealing that high fine-tuning performance can be deceptive. It systematically exposes the limitations of existing methods through its multi-perspective framework.

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4. Methodology (Core Technology & Implementation)

The paper's core methodology is its proposed multi-perspective evaluation framework. It is designed to provide a systematic and diagnostic analysis of molecular SSL models.

• Principles: The central idea is that a single performance number from fine-tuning is insufficient. A truly effective pretrained model should generate representations that are:

  1. High-Quality and General: Intrinsically useful for downstream tasks even without extensive modifications (measured by linear probing).
  2. Beneficial: Clearly superior to starting from scratch (measured by Pretrain Gain).
  3. Stable: Retain their learned knowledge during adaptation to new tasks (low parameter shift).
  4. Scalable: Improve with more pretraining data.

• Steps & Procedures: The framework consists of four key components, all conducted under a unified experimental setup where only the pretraining strategy differs across models.

  1. Quality of Learned Representations via Linear Probing

  • To isolate and evaluate the quality of the representations learned during pretraining, the encoder of the pretrained model is frozen.
  • A simple, 2-layer MLP prediction head is attached to the frozen encoder.
  • Only this prediction head is trained on the downstream labeled dataset.
  • Rationale: High performance in this setting indicates that the pretrained representations are linearly separable and contain general features relevant to the downstream task, without needing the encoder itself to be retuned.

  2. Pretrain Gain Against Random Initialization

  • This metric is introduced to quantify the actual benefit derived from the pretraining phase.
  • It is calculated by comparing the performance of a model with pretrained weights to an identical model (same architecture, hyperparameters) with randomly initialized weights.
  • The formula is: $$\mathrm { P r e t r a i n G a i n } = \frac { \mathrm { S c o r e } _ { \mathrm { p r e t r a i n } } - \mathrm { S c o r e } _ { \mathrm { r a n d o m } } } { \mathrm { S c o r e } _ { \mathrm { r a n d o m } } } \times 1 0 0$$
  • Symbol Explanation:
    • $\mathrm{Score}_{\mathrm{pretrain}}$: The downstream task performance (e.g., ROC-AUC) of the model initialized with pretrained weights.
    • $\mathrm{Score}_{\mathrm{random}}$: The downstream task performance of the model with randomly initialized weights.
  • Rationale: This metric normalizes the performance improvement against a strong baseline (training from scratch), revealing the true contribution of pretraining. A low or negative gain suggests pretraining was ineffective.

  3. Quantifying Forgetting Through Parameter Shift

  • Fine-tuning updates all model parameters, which can lead to "catastrophic forgetting" where knowledge from the large pretraining dataset is lost.
  • To measure this, the authors calculate the L2 distance between the encoder's parameters before and after the fine-tuning process.
  • The formula for parameter shift $\Delta_{\mathrm{param}}$ is: $$\Delta _ { \mathrm { p a r a m } } = \sum _ { i = 1 } ^ { N } { { { \left\| { \theta _ { i } ^ { \mathrm { b e f o r e } } - \theta _ { i } ^ { \mathrm { a f t e r } } } \right\| } ^ { 2 } } }$$
  • Symbol Explanation:
    • $\theta^{\mathrm{before}}$: The encoder parameters after pretraining but before fine-tuning.
    • $\theta^{\mathrm{after}}$: The encoder parameters after fine-tuning on a downstream task.
    • $N$: The total number of parameter tensors in the encoder.
  • Rationale: A large parameter shift suggests that the pretrained representations were not well-aligned with the downstream task, requiring significant modification and potentially losing general knowledge. A small shift implies the representations were robust and generalizable.

  4. Scalability in Molecular SSL

  • A hallmark of successful SSL in NLP and computer vision is that performance consistently improves with more pretraining data. This is known as a "scaling law."

  • The authors test this in the molecular domain by pretraining models on datasets of varying sizes: 0.02M, 0.25M, 0.5M, 1.0M, 1.5M, and 2.0M molecules sampled from the ZINC15 dataset.

  • They then evaluate the downstream performance for each pretraining scale.

  • Rationale: This analysis reveals whether current molecular SSL methods can effectively leverage massive unlabeled datasets, which is crucial for building powerful "foundation models."

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5. Experimental Setup

• Datasets:

  • Pretraining Dataset: The ZINC15 dataset, a large collection of commercially available chemical compounds. For the main experiments, a subset of 0.25 million molecules was used. For scalability experiments, subsets ranging from 20,000 to 2 million were used.

  • Downstream Datasets: Six classification benchmark datasets from MoleculeNet were used, covering a range of biological properties:

    • BACE: Predicting binding results for beta-secretase 1.
    • BBBP: Predicting blood-brain barrier permeability.
    • ClinTox: Predicting toxicity for drugs.
    • Tox21: Predicting toxicity across 12 different targets.
    • ToxCast: Predicting toxicity over a large set of 617 assays.
    • SIDER: Predicting drug side effects across 27 system organ classes.

  • A summary of the downstream dataset characteristics is transcribed below from Table 3. Note: This table is a transcription of the original data, not the original image.

    DATASET	# TASKS	# GRAPHS	# Atoms	# BONDS
    BACE	1	1,513	34.1	36.9
    BBBP	1	2,039	24.1	26.0
    CLINTOX	2	1,478	26.3	28.1
    Tox21	12	7,831	18.6	19.3
    SIDER	27	1,478	34.3	36.1
    TOXCAST	617	8,575	18.8	19.3

• Evaluation Metrics:

  • Conceptual Definition: ROC-AUC (Receiver Operating Characteristic - Area Under Curve) is the primary metric for classification tasks. It measures a model's ability to distinguish between positive and negative classes across all possible classification thresholds. An AUC of 1.0 represents a perfect classifier, while an AUC of 0.5 represents a random classifier.
  • Mathematical Formula: The AUC is the integral of the ROC curve, which plots the True Positive Rate (TPR) against the False Positive Rate (FPR): $\mathrm{AUC} = \int_{0}^{1} \mathrm{TPR}(\mathrm{FPR}^{-1}(x)) dx$
  • Symbol Explanation:
    • $\mathrm{TPR} = \frac{\mathrm{True Positives}}{\mathrm{True Positives} + \mathrm{False Negatives}}$ (Sensitivity or Recall)
    • $\mathrm{FPR} = \frac{\mathrm{False Positives}}{\mathrm{False Positives} + \mathrm{True Negatives}}$ (1 - Specificity)
  • The paper also uses Mean Squared Error (MSE) for regression tasks in the appendix. $\mathrm{MSE} = \frac{1}{n}\sum_{i=1}^{n}(Y_i - \hat{Y_i})^2$ Where $Y_i$ is the true value and $\hat{Y_i}$ is the predicted value for the $i$-th sample.

• Baselines: The primary baseline for each pretrained model is its own architecture with randomly initialized weights. This allows for a direct measurement of the Pretrain Gain and isolates the effect of the pretraining strategy. The paper compares eight different SSL methods against each other and against their random-initialization counterparts: GROVER, AttributeMask, ContextPred, EdgePred, GraphLoG, GraphCL, KANO, and ChemBERTa.

• Implementation Details:

  • Unified Setup: Hidden dimension of 300, 2-layer MLP prediction head.

  • Pretraining: Batch size of 256, 100 epochs.

  • Downstream: Batch size of 32, 50 epochs.

  • Hardware: All experiments were run on a single NVIDIA RTX 3090 GPU.

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6. Results & Analysis

The paper presents a comprehensive analysis that challenges common assumptions in molecular SSL.

• Core Results: Fine-tuning vs. Linear Probing

  The results from Table 2 (transcribed below) show the performance for fine-tuning and linear probing.

  Note: This table is a transcription of Table 2 (A) Fine-tuning.

  Method	BACE	BBBP	ClinTox	Tox21	ToxCast	SIDER	AVG
  GROVER	85.93±1.18	92.73±3.60	84.90±6.71	84.91±2.05	62.41±0.69	70.33±1.27	80.20
  AttributeMask	77.12±5.09	68.46±1.37	72.27±4.43	76.84±0.39	62.75±0.81	64.04±0.17	70.25
  ContextPred	76.53±3.19	68.62±1.66	65.63±3.49	74.70±1.04	62.76±0.58	64.08±1.47	68.72
  EdgePred	72.29±2.96	63.85±1.01	51.87±3.16	72.40±0.62	54.64±2.50	59.96±0.68	62.50
  GraphLoG	83.51±0.76	63.13±1.34	63.78±4.76	73.26±0.39	60.39±0.69	62.64±0.84	67.79
  GraphCL	78.83±1.31	63.84±0.51	58.59±4.79	73.17±0.79	60.13±0.16	63.00±1.51	66.26
  KANO	84.73±2.18	94.61±1.14	88.08±4.32	83.52±2.52	59.36±1.33	72.41±2.19	80.45
  ChemBERTa	77.24±1.20	78.12±1.04	85.73±6.45	70.75±1.92	69.73±1.47	52.23±2.78	72.30

  Note: This table is a transcription of Table 2 (B) Linear probing.

  Method	BACE	BBBP	ClinTox	Tox21	ToxCast	SIDER	AVG
  GROVER	82.97±4.40	91.91±2.77	76.68±5.08	81.62±2.43	61.96±0.87	66.99±2.01	77.02
  AttributeMask	61.76±0.69	60.09±0.56	65.27±1.82	69.55±0.23	54.56±0.67	57.65±1.29	61.48
  ContextPred	60.07±1.58	63.43±0.16	23.49±0.55	68.29±0.44	60.77±0.82	58.21±0.69	55.71
  EdgePred	63.36±7.09	56.57±1.03	49.91±0.49	51.60±2.07	51.51±0.46	49.96±0.40	53.82
  GraphLoG	72.28±1.64	61.34±1.07	62.18±5.31	68.73±0.41	59.78±0.18	56.17±0.88	63.41
  GraphCL	70.05±3.79	62.43±0.40	56.36±2.17	66.40±0.63	58.92±0.61	58.84±0.71	62.17
  KANO	78.54±4.95	91.92±3.99	61.40±16.11	81.15±3.28	59.57±0.96	68.46±1.22	73.51
  ChemBERTa	69.02±0.37	76.03±0.54	32.99±4.28	70.33±0.63	65.79±1.04	50.40±0.44	60.76

  • Finding 1: Fine-tuning performance is deceptive. KANO achieves the highest average fine-tuning score (80.45), but GROVER achieves the highest linear probing score (77.02). This suggests GROVER's pretrained representations are more inherently generalizable.

  • Finding 2: Large Performance Gaps. As shown in Image 7, some models like ContextPred and ChemBERTa have a very large performance drop between fine-tuning and linear probing (>10 points). This indicates their pretrained representations are not very useful without significant modification by the fine-tuning process. Models with a smaller gap (GROVER, GraphLoG, GraphCL) have more robust representations.

    该图像为条形图，比较了不同分子自监督学习模型在ROC-AUC指标上的表现。蓝色条表示线性探测（Linear probing）性能，红色条表示微调（Fine-tuning）性能，灰色区域代表两者之间的性能差距（Performance Gap）。图中显示各模型微调均优于线性探测，且性能差距在不同模型间差异显著。

• Analysis of Pretrain Gain

  Image 3 visualizes the Pretrain Gain for both fine-tuning and linear probing.

  该图像为图表，展示了不同自监督预训练方法在分子任务中微调（Fine-tuning）与线性探测（Linear Probing）两种评估方式下的ROC-AUC表现对比。横轴为ROC-AUC分数，蓝色代表随机初始化基线，红色为预训练后表现，橙色和绿色分别表示性能提升（Positive Gain）和下降（Negative Gain）。图(A)显示多数方法微调后有明显提升，图(B)则显示部分方法在线性探测时性能反而下降，反映预训练效果在不同评估方式下存在显著差异。

  • Finding 3: Pretraining benefit is not guaranteed. In fine-tuning (left), most models show a positive gain. However, KANO, the best fine-tuning performer, has a negligible Pretrain Gain of 0.34%. This shocking result suggests its high performance comes from its architecture or training procedure, not from the knowledge learned during pretraining.
  • Finding 4: Linear probing reveals weaker representations. In linear probing (right), the gains are much smaller across the board. Shockingly, several models, including AttributeMask, EdgePred, KANO, and ChemBERTa, show negative average Pretrain Gain, meaning the pretrained model performs worse than a randomly initialized one when the encoder is frozen. This is a critical failure, indicating the pretraining created representations that are poorly suited for direct use in downstream tasks.

• Quantifying Forgetting via Parameter Shift

  Image 4 visualizes the L2 distance of encoder parameters before and after fine-tuning. A transcription of the full data from Table 5 is provided below.

  该图像为图表，展示了不同分子自监督学习预训练方法在多个下游任务（如BBBP、BACE、ClinTot、Tox21、ToxCast、SIDER）及平均表现上的性能对比。图中以气泡大小和颜色深浅表示性能值，Tox21和ToxCast任务中多数方法表现较好，部分方法在平均指标上表现明显优于其他。

  Note: This table is a transcription of Table 5.

  Method	BACE	BBBP	ClinTox	Tox21	ToxCast	SIDER	AVG
  GROVER	150.56 ±6.79	114.52 ±5.01	14.47 ±0.72	146.88 ±7.99	83.40±4.08	202.52 ±10.55	118.73
  AttributeMask	7342.73 ±324.47	7512.03 ±342.73	1621.39 ±81.04	33802.75±1467.07	37624.28±1623.60	9871.82 ±460.89	16295.83
  ContextPred	13259.14±585.36	959.35 ±42.16	12196.34±567.81	53328.02±2256.96	56409.84±2435.61	19260.63±841.49	25902.22
  EdgePred	10292.06±487.97	7128.03 ±337.83	2880.88 ±133.83	49563.34±2209.74	46475.73±2074.52	18025.51±856.93	22394.26
  GraphLoG	3575.96 ±189.63	13064.01±707.52	8800.22 ±482.51	41243.57±2083.18	39080.37±1757.49	8217.41 ±358.15	18996.92
  GraphCL	10232.89±531.38	351.13 ±15.59	1395.80 ±63.51	34420.74±1581.78	37580.58±1720.92	3829.59 ±174.62	14635.12
  KANO	2406.61 ±109.54	3817.20 ±190.18	2454.36 ±110.82	13678.55±701.49	28898.60 ±1502.89	2227.76 ±103.35	8913.85
  ChemBERTa	1777.71 ±24.31	1649.05±22.61	1463.93 ±20.10	1393.99 ±19.26	553.03 ±7.87	31.30 ±0.61	1144.83

  • Finding 5: GNNs forget more than Transformers. The GNN-based models (AttributeMask, ContextPred, EdgePred, etc.) exhibit massive parameter shifts (orders of magnitude larger than Transformer-based models). In contrast, GROVER and ChemBERTa show much smaller shifts, suggesting their pretrained representations are more stable and less prone to forgetting.

  • Finding 6: Advanced GNNs can mitigate forgetting. KANO, despite being a GNN, shows a relatively smaller parameter shift compared to other GNNs. The authors attribute this to its prompt-based mechanism and use of a knowledge graph, which better aligns its pretraining task with downstream chemical properties.

  • Finding 7: Parameter shift correlates with poor generalization. As shown in Image 8, there is a clear linear relationship between the parameter shift and the performance gap (from linear probing vs. fine-tuning). Models that change more during fine-tuning also tend to be the ones whose frozen representations perform poorly, reinforcing the idea that parameter shift is a good proxy for measuring representation generality.

    该图像为二维散点图，横轴表示“Performance Gap”，纵轴表示“Parameter Shift”。图中展示了多个分子自监督学习模型在这两个指标上的表现，带有一条斜率为1的虚线。大部分模型点分布在虚线上下，显示模型性能差距与参数变化之间的关系，且不同预训练策略模型表现出差异性的参数变动与性能改进。

• Scalability of Molecular SSL

  Image 5 shows the average Pretrain Gain as the pretraining dataset size increases.

  该图像为图表，展示了不同分子自监督学习模型在两种评估方式下随预训练数据集规模变化的Pretrain Gain。左图(A)为微调（Fine-tuning）结果，多数模型呈现正向且相对稳定的增益；右图(B)为线性探测（Linear Probing）结果，模型表现更为分散，部分模型甚至出现负增益，显示预训练效果在不同评估策略下差异显著。

  • Finding 8: Molecular SSL models do not scale. Unlike in NLP and vision, where more data consistently leads to better models, the performance of all tested molecular SSL models remains largely flat. Increasing the pretraining dataset from 20,000 to 2 million molecules provides almost no benefit. This is a major finding, suggesting a fundamental limitation in current pretraining strategies for molecules. They may not be capturing the kind of information that benefits from scale, possibly due to a disconnect between structure-based pretext tasks and property-based downstream tasks.

• Integrated Evaluation

  The radar chart in Image 6 provides a holistic summary across all proposed evaluation metrics.

  该图像为图表，是一个雷达图，展示了不同分子自监督学习模型在多项指标上的表现，包括线性探测增益（Pretrain Gain LP）、微调增益（Pretrain Gain FT）、性能差距（Performance Gap(R)）、微调和线性探测的可扩展性（FT Scalability、LP Scalability）、参数变化（Parameter Shift(R)）以及线性探测（LP）和微调（FT）性能。图中七个模型（如GROVER、AttributeMask等）在各指标的相对表现以不同颜色区域展示，反映模型在预训练效果、泛化能力和参数稳定性方面的差异。

  • Finding 9: No single model excels everywhere. The chart clearly shows that different models have different strengths and weaknesses. GROVER emerges as the best all-around model, with strong performance across most axes. In contrast, KANO, the top fine-tuning model, scores poorly on Pretrain Gain and scalability, highlighting the weakness of relying on a single metric.

  • Overall Takeaways: Transformer-based architectures (GROVER, ChemBERTa) appear more robust. For GNNs, advanced strategies like contrastive learning (GraphLoG, KANO) are more effective than simpler pretext tasks.

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7. Conclusion & Reflections

• Conclusion Summary: The paper successfully demonstrates that the conventional wisdom of using fine-tuning performance as the sole benchmark for molecular SSL is flawed and insufficient. By introducing a multi-perspective evaluation framework—encompassing linear probing, Pretrain Gain, parameter shift, and scalability—the authors reveal significant weaknesses in existing models. Key findings include the limited generalizability of representations (poor linear probing results), substantial forgetting in GNNs (high parameter shifts), and a concerning lack of scalability across all models. The work concludes by advocating for the adoption of more comprehensive evaluation protocols to guide future research toward developing truly generalizable and robust molecular representations.

• Limitations & Future Work:

  • Authors' Acknowledged Limitations: The authors do not explicitly state limitations, but the conclusion implies that future work should focus on designing new pretraining strategies that can overcome the identified issues of poor generalization, forgetting, and lack of scalability.
  • Implicit Limitations: The study is confined to a specific set of eight SSL models and six classification datasets. While comprehensive, the findings might not generalize to all possible molecular tasks (e.g., 3D conformational tasks, reaction prediction) or newer model architectures. The unified setting (e.g., fixed hidden dimension of 300) may have inadvertently disadvantaged models originally designed for larger scales, although the appendix partially addresses this by testing a 1200 dimension setting.

• Personal Insights & Critique:

  • This is a highly impactful and important paper. Its contribution is not an incremental improvement on a leaderboard but a fundamental call for greater scientific rigor in the field. Such meta-analytical work is crucial for preventing the community from optimizing for flawed metrics.
  • The concept of Pretrain Gain is simple yet powerful, providing a clear, quantitative measure of pretraining's value. The negative Pretrain Gain results are particularly damning and should prompt a deep rethinking of what some pretext tasks are actually learning.
  • The finding on scalability is perhaps the most significant. It suggests a potential "wall" for current molecular SSL methods and hints that simply throwing more data at the problem won't work. Future breakthroughs will likely require pretext tasks that capture more complex chemical and physical principles beyond simple graph structure.
  • This paper sets a new standard for how molecular SSL models should be evaluated. Future papers in this domain will likely be expected to report on these or similar multi-faceted metrics, moving the field towards more robust and meaningful progress.