MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction (2411.00737v1)

Abstract: Bridging biomolecular modeling with natural language information, particularly through LLMs, has recently emerged as a promising interdisciplinary research area. LLMs, having been trained on large corpora of scientific documents, demonstrate significant potential in understanding and reasoning about biomolecules by providing enriched contextual and domain knowledge. However, the extent to which LLM-driven insights can improve performance on complex predictive tasks (e.g., toxicity) remains unclear. Further, the extent to which relevant knowledge can be extracted from LLMs also remains unknown. In this study, we present Molecule Caption Arena: the first comprehensive benchmark of LLM-augmented molecular property prediction. We evaluate over twenty LLMs, including both general-purpose and domain-specific molecule captioners, across diverse prediction tasks. To this goal, we introduce a novel, battle-based rating system. Our findings confirm the ability of LLM-extracted knowledge to enhance state-of-the-art molecular representations, with notable model-, prompt-, and dataset-specific variations. Code, resources, and data are available at github.com/Genentech/molcap-arena.

• The paper introduces MolCap-Arena, a novel benchmark integrating language-enhanced captions with GNN models for improved molecular property prediction.
• It demonstrates that domain-specific LLMs, such as BioT5, significantly boost accuracy in tasks like toxicity prediction and bioactivity assessment.
• The study’s battle-based rating system and task-specific prompts provide granular evaluation and actionable insights for advancing drug discovery research.

Overview of MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction

The paper "MolCap-Arena: A Comprehensive Captioning Benchmark on Language-Enhanced Molecular Property Prediction" introduces a novel benchmark aimed at assessing the role of language-enhanced molecular representations in property prediction tasks. The integration of LLMs with biomolecular modeling, particularly through the use of LLMs, has opened a novel interdisciplinary frontier with significant implications for computational chemistry and drug discovery.

Contributions and Methodology

The authors release "Molecule Caption Arena" (MolCap-Arena), the first comprehensive benchmark for LLM-augmented molecular property prediction. The benchmark specifically evaluates the impact of over twenty LLMs across a variety of tasks like toxicity prediction and bioactivity characterization. The benchmark includes domain-specific and general-purpose LLM molecule captioners, employing a novel battle-based rating system for evaluation. This rating system is inspired by methodologies like the Bradley-Terry model but modified for the multimodal integration required in chemical domains.

The proposed pipeline involves augmenting traditional graph neural networks (GNNs) with text-derived knowledge from LLM captioners. Each model's embeddings are combined and used to train a shallow support vector machine (SVM) to predict target properties, a process that allows for examining the contributions of each modality independently before fusion.

Key Findings

• Performance Enhancement: The integration of LLM-derived captions consistently improves upon baseline GNN models across tasks, indicating the promising potential of LLM-induced enhancements in molecular property prediction.
• Domain-Specific Superiority: Captions from domain-specific models, such as BioT5, generally outperform those from general-purpose LLMs. However, some large-scale general-purpose models, particularly Llama variants, also yield high effectiveness.
• Impact of Model Size and Persona: Results show a correlation between LLM model size and performance improvement, with larger models typically outperforming smaller counterparts. Moreover, specific personas and molecular representations present task-dependent impacts on performance.
• Task-Specific Prompts: The evaluation of task-specific captions reveals that tuned prompts can significantly benefit predictive outcomes, highlighting the importance of custom-tailored language inputs in maximizing the utility of LLM-leveraged knowledge.
• Novel Rating System Efficacy: The battle-based rating system provides a robust and granular evaluation across multiple tasks and datasets, allowing for a nuanced comparison of LLM impacts which is not captured by standard metrics alone.

Implications and Future Directions

The establishment of MolCap-Arena benchmarks a critical step toward understanding and quantifying the role of natural language in molecular modeling. This benchmark creates pathways for more comprehensive integration of multimodal data in chemical informatics, which could substantially aid in the drug discovery process by enhancing model explainability and prediction accuracy.

This benchmark sets the stage for future investigations into advanced molecule-language fused architectures, the development of innovative text-based resources, and the exploration of new, multimodal task applications. Future studies could extend this work by incorporating more diverse datasets and employing more sophisticated fusion techniques to fully exploit the richness of LLM knowledge in biologically-relevant contexts.

In conclusion, the MolCap-Arena paper provides a sophisticated framework for evaluating and integrating language-enhanced information in molecular modeling, underscoring the capabilities of LLMs in enriching molecular representations and broadening the landscape of computational chemistry and drug discovery research. This academic exercise highlights the transformative potential of cross-disciplinary advancements, bridging artificial intelligence and molecular sciences.