Exploring deep recurrent models with reinforcement learning for molecule design

Abstract

The essence of molecular design is to effectively fulfill a molecular property profile that is desirable as a drug. In this paper we consider a number of different generative models for the design of new molecular structures the satisfy specific multiple objectives that are desirable for a particular drug discovery project. In addition to the evaluation of multiple generative models, we also presented as part of this work a benchmarking dataset to the community with the aim to provide an objective set to evaluate other new de novo molecular design models appropriately