NeurIPS 2020

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

Meta Review

This paper proposes a framework, called CogMol, to design a drug-like small molecule for specific targets, which was applied to the problem of designing molecules that bind to three proteins found in SARS-CoV-19. Reviewers raised various concerns and questions and author response largely resolved major criticisms. Overall, based on the technical novelty, experiments, and clarity in writing, this paper passes the bar of acceptance to NeurIPS as a technical paper. However, multiple reviewers expressed a concern about the possibility that readers over-interpret the results in the context of the current pandemic situation, because wet-lab validation experiments have not been performed, (which would be out of scope and not necessary for a ML conference paper.) Thus, it is strongly recommended that the authors revise the manuscript to explicitly state that no experimental validation has been performed and only in-silico binding conclusions can be drawn.