药物发现
计算机科学
药品
人工智能
数据科学
深度学习
机器学习
医学
药理学
生物信息学
生物
作者
Alvaro Prat,Hisham Abdel Aty,Orestis Bastas,Gintautas Kamuntavičius,Tanya Paquet,Povilas Norvaišas,Piero Gasparotto,Roy Tal
标识
DOI:10.1021/acs.jcim.4c00481
摘要
We propose HydraScreen, a deep-learning framework for safe and robust accelerated drug discovery. HydraScreen utilizes a state-of-the-art 3D convolutional neural network designed for the effective representation of molecular structures and interactions in protein-ligand binding. We designed an end-to-end pipeline for high-throughput screening and lead optimization, targeting applications in structure-based drug design. We assessed our approach using established public benchmarks based on the CASF-2016 core set, achieving top-tier results in affinity and pose prediction (Pearson's
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