Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19

• URL: http://arxiv.org/abs/2102.04977v1
• Date: Tue, 9 Feb 2021 17:49:26 GMT
• Title: Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
• Authors: Logan Ward and Jenna A. Bilbrey and Sutanay Choudhury and Neeraj Kumar and Ganesh Sivaraman
• Abstract summary: Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates.
• Score: 11.853524110656991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative models and a comparison study of two state-of-the-art graph generative models for designing COVID-19 targeted drug candidates: 1) a variational autoencoder-based approach (VAE) that uses prior knowledge of molecules that have been shown to be effective for earlier coronavirus treatments and 2) a deep Q-learning method (DQN) that generates optimized molecules without any proximity constraints. We evaluate the novelty of the automated molecule generation approaches by validating the candidate molecules with drug-protein binding affinity models. The VAE method produced two novel molecules with similar structures to the antiretroviral protease inhibitor Indinavir that show potential binding affinity for the SARS-CoV-2 protein target 3-chymotrypsin-like protease (3CL-protease).

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