Domain-Adapted Molecular Language Models for Efficient Search of Make-on-Demand Libraries

2026-08-18

AI Summary

研究通过微调分子语言模型以适应特定虚拟库，提高其在药物发现、有机材料和催化等领域的分子发现效率。

Abstract

Pretrained molecular language models are increasingly used as molecular encoders for learning structure-property relationships. However, their practical suitability for molecular discovery within and beyond their pretraining domain remains unclear. Herein, we systematically benchmark four molecular language models across six virtual molecular libraries spanning drug discovery, organic materials, and catalysis. Native molecular language model embeddings show substantial variation in discovery performance across libraries, whereas molecular fingerprints provide a consistently strong and robust baseline. Consistent with a potential domain-representation mismatch, we show that explicit domain adaptation substantially improves representation performance. Fine-tuning molecular language model encoders on structures from the target virtual library consistently improves sample efficiency, with several adapted encoders emerging as the top-performing representations across the benchmark tasks. These results show that molecular representation quality depends strongly on the target domain and that explicit adaptation can improve the practical utility of molecular foundation models. More broadly, our findings establish domain-adapted molecular representations as a promising strategy for sample-efficient adaptive decision making in virtual screening and self-driving laboratories.

Problem

• molecular language models
• domain adaptation
• virtual molecular libraries
• structure-property relationships

Innovation

• domain adaptation
• molecular language models
• fine-tuning
• sample efficiency