Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning
<|reference_start|>Improving Targeted Molecule Generation through Language Model Fine-Tuning Via Reinforcement Learning: 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.<|reference_end|>
@article{ahmed2024improving,
    title={Improving Targeted Molecule Generation through Language Model
  Fine-Tuning Via Reinforcement Learning},
    author={Salma J. Ahmed, Mustafa A. Elattar},
    journal={arXiv preprint arXiv:2405.06836},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06836},
    primaryClass={q-bio.BM cs.LG}
}