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

2021-02-09 · Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury, Neeraj Kumar, Ganesh Sivaraman

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).

📄 PDF Abstract BibTeX arXiv:2102.04977

Code (1)

exalearn/covid-drug-design 공식 구현 pytorch

Tasks

BenchmarkingQ-Learning

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