MolProphecy: Bridging medicinal chemists' knowledge and molecular pre-trained models via a multi-modal framework.

introductionIn drug discovery, the tacit domain knowledge of experts plays a critical role in guiding molecular design and decision-making.However, existing molecular pre-trained models rarely incorporate such expert knowledge, leading to suboptimal molecular design decisions.

objectivesThis study proposes MolProphecy, a proxy-human-in-the-loop (proxy-HITL) multi-modal framework that integrates chemists' domain knowledge with structural molecular information to improve predictive accuracy and interpretability in drug discovery.

methodsMolProphecy simulates chemist reasoning using ChatGPT to generate expert-level chemist insights for target molecules.This knowledge is encoded by a large language model (LLM) and fused with graph-based molecular features via a gated multi-head cross-attention module.The framework jointly reasons over human-derived and structural information.

resultsMolProphecy consistently outperforms baseline models across nine MoleculeNet benchmarks.For illustration, on FreeSolv it achieves an RMSE of 0.796, a 9.1% reduction over the best baseline.On BACE, SIDER, and ClinTox, MolProphecy improves AUROC by 5.39%, 1.43%, and 1.06%, respectively.In addition, MolProphecy demonstrates strong generalization on an independent solubility dataset, demonstrating robustness and applicability of our multi-modal fusion framework.

conclusionMolProphecy provides a generalizable framework for molecular property prediction by integrating simulated chemist expertise with structural data. Its design allows simulated input to be replaced with real chemist knowledge without retraining, establishing a pathway toward collaborative and interpretable drug discovery.