Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates

Autor:	Bilbrey, Jenna, Ward, Logan, Choudhury, Sutanay, Kumar, Neeraj, Sivaraman, Ganesh
Rok vydání:	2021
Předmět:	Quantitative Biology - Biomolecules Computer Science - Machine Learning
Zdroj:	Published at ICLR 2021 Workshop on Machine Learning for Preventing and Combating Pandemics
Druh dokumentu:	Working Paper
Popis:	We examine a pair of graph generative models for the therapeutic design of novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of urgency, we chose well-validated models with unique strengths: an autoencoder that generates molecules with similar structures to a dataset of drugs with anti-SARS activity and a reinforcement learning algorithm that generates highly novel molecules. During generation, we explore optimization toward several design targets to balance druglikeness, synthetic accessability, and anti-SARS activity based on \icfifty. This generative framework\footnote{https://github.com/exalearn/covid-drug-design} will accelerate drug discovery in future pandemics through the high-throughput generation of targeted therapeutic candidates. Comment: arXiv admin note: substantial text overlap with arXiv:2102.04977
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2105.10489 Zobrazit plný text záznamu View this record from Arxiv