March 27, 2018
ICLR 2018

Exploring deep recurrent models with reinforcement learning for molecule design

Daniel Neil, Marwin Segler, Laura Guasch, Mohamed Ahmed, Dean Plumbley, Matthew Sellwood, Nathan Brown

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