MoleGuLAR: Molecule Generation using Reinforcement Learning with Alternating Rewards

Design of new inhibitors for novel targets is a very important problem especially in the current scenario with the world being plagued by COVID-19. Conventional approaches undertaken to this end, like, high-throughput virtual screening require extensive combing through existing datasets in the hope of finding possible matches. In this study we propose a computational strategy for de novo generation of molecules with high binding affinities to the specified target. A deep generative model is built using a stack augmented recurrent neural network for initially generating drug like molecules and then it is optimized using reinforcement learning to start generating molecules with desirable properties--primarily the binding affinity. The reinforcement learning section of the pipeline is further extended to multi-objective optimization showcasing the model's ability to generate molecules with a wide variety of properties desirable for drug like molecules, like, LogP, Quantitative Estimate of Drug Likeliness etc.. For multi-objective optimization, we have devised a novel strategy in which the property being used to calculate the reward is changed periodically. In comparison to the conventional approach of taking a weighted sum of all rewards, this strategy shows enhanced ability to generate a significantly higher number of molecules with desirable properties.