Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning

• URL: http://arxiv.org/abs/2405.06836v1
• Date: Fri, 10 May 2024 22:19:12 GMT
• Title: Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning
• Authors: Salma J. Ahmed, Mustafa A. Elattar,
• Abstract summary: We introduce an innovative de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins. Our method integrates a composite reward function, combining considerations of drug-target interaction and molecular validity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Developing new drugs is laborious and costly, demanding extensive time investment. In this study, we introduce an innovative de-novo drug design strategy, which harnesses the capabilities of language models to devise targeted drugs for specific proteins. Employing a Reinforcement Learning (RL) framework utilizing Proximal Policy Optimization (PPO), we refine the model to acquire a policy for generating drugs tailored to protein targets. Our method integrates a composite reward function, combining considerations of drug-target interaction and molecular validity. Following RL fine-tuning, our approach demonstrates promising outcomes, yielding notable improvements in molecular validity, interaction efficacy, and critical chemical properties, achieving 65.37 for Quantitative Estimation of Drug-likeness (QED), 321.55 for Molecular Weight (MW), and 4.47 for Octanol-Water Partition Coefficient (logP), respectively. Furthermore, out of the generated drugs, only 0.041\% do not exhibit novelty.

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