Manan Goel received his MS Dual Degree in  Computational Natural Sciences (CNS). His research work was supervised by Prof. Deva Priya Kumar. Here’s a summary of his research work on Deep Generative Modelling and Reinforcement Learning for Molecule Generation:

The recent advances in Machine Learning and other data driven techniques have transformed domains like robotics, computer vision and natural language processing. These algorithms are now also being applied to a wide array of chemistry problems as well. One such class of machine learning algorithms is deep generative modelling which has found its application in de novo molecule generation. Generating lead molecules in silico, helps reduce the wastage of limited computational and laboratory resources by reducing the number of generated bad leads. For every task, the lead molecules may exist in different regions of the chemical space which would have to be explored appropriately. The drug discovery process is long, arduous and expensive while also being plagued by the problems caused by bad leads especially for novel targets and the design of new inhibitors for novel targets is now more important than ever especially in the current scenario with the world being plagued by COVID-19. Drug molecules must possess certain properties to be effective so that they can be considered for further validation. Conventional computational approaches such as high-throughput virtual screening require extensive combing through existing database and evaluating all the molecules in them in the hope of finding possible hits that possess the required properties. However, existing libraries of drug-like molecules, advances in deep generating modelling and optimization techniques like reinforcement learning has provided a new avenue to tackle this problem.

In this thesis, a computational strategy is proposed for the de novo generation of molecules with high binding affinities to the specified target and other desirable properties for drug-like molecules using reinforcement learning. A deep generative model built using a stack-augmented recurrent neural network initially trained to generate drug-like molecules is optimized using reinforcement learning to start generating molecules with desirable properties like LogP, Quantitative Estimate of Drug Likeliness, Topological Polar Surface Area, and Hydration Free Energy along with the binding affinity. For multi-objective optimization, a novel strategy was also devised 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 an enhanced ability to generate a significantly higher number of molecules with desirable properties while also exploring new regions of the chemical space for multiple targets.