对接(动物)
虚拟筛选
计算机科学
机器学习
人工智能
计算生物学
化学
医学
药物发现
生物
生物化学
护理部
作者
Haihan Liu,Baichun Hu,Pei‐Ying Chen,Xiao Wang,Hanxun Wang,Shizun Wang,Jian Wang,Bin Lin,Maosheng Cheng
标识
DOI:10.1021/acs.jcim.4c00072
摘要
In drug discovery, molecular docking methods face challenges in accurately predicting energy. Scoring functions used in molecular docking often fail to simulate complex protein–ligand interactions fully and accurately leading to biases and inaccuracies in virtual screening and target predictions. We introduce the "Docking Score ML", developed from an analysis of over 200,000 docked complexes from 155 known targets for cancer treatments. The scoring functions used are founded on bioactivity data sourced from ChEMBL and have been fine-tuned using both supervised machine learning and deep learning techniques. We validated our approach extensively using multiple data sets such as validation of selectivity mechanism, the DUDE, DUD-AD, and LIT-PCBA data sets, and performed a multitarget analysis on drugs like sunitinib. To enhance prediction accuracy, feature fusion techniques were explored. By merging the capabilities of the Graph Convolutional Network (GCN) with multiple docking functions, our results indicated a clear superiority of our methodologies over conventional approaches. These advantages demonstrate that Docking Score ML is an efficient and accurate tool for virtual screening and reverse docking.
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