  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.
