Small Molecule Optimization with Large Language Models

Abstract:Molecular optimization, the process of designing molecules with desirable properties, represents a critical challenge in drug discovery. Recent advancements in large language models (LLMs) have opened new opportunities for their integration with traditional molecular optimization algorithms to improve performance. In this work, we propose Molecular Language Model powered Evolutionary Algorithm (Mol-E), an evolutionary algorithm that relies on the generative capabilities of LLMs trained on molecules and molecular properties. Scientific Contribution. Mol-E establishes new state-of-the-art results on the Practical Molecular Optimization benchmark, with summed Top-10 AUC values of 17.500 in the task-agnostic regime, in which the oracle is treated strictly as a black box, and 20.551 in the task-informed regime, in which the optimizer receives a fixed semantic description of the objective. Mol-E also improves over the evaluated baselines on multi-property optimization with docking against DRD2, MK2, and AChE.

Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE); Quantitative Methods (q-bio.QM)

arXiv:2407.18897 [cs.LG]

(or arXiv:2407.18897v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2407.18897 Focus to learn more arXiv-issued DOI via DataCite

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