Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets
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摘要There is a growing body of evidence showing that machine learning regression results in more accurate structure-based prediction of protein-ligand binding affinity. Docking methods that aim at optimizing the affinity of ligands for a target rely on how accurate their predicted ranking is. However, despite their proven advantages, machine-learning scoring functions are still not widely applied. This seems to be due to insufficient understanding of their properties and the lack of user-friendly software implementing them. Here we present a study where the accuracy of AutoDock Vina, arguably the most commonly-used docking software, is strongly improved by following a machine learning approach. We also analyse the factors that are responsible for this improvement and their generality. Most importantly, with the help of a proposed benchmark, we demonstrate that this improvement will be larger as more data becomes available for training Random Forest models, as regression models implying additive functional forms do not improve with more training data. We discuss how the latter opens the door to new opportunities in scoring function development. In order to facilitate the translation of this advance to enhance structure-based molecular design, we provide software to directly re-score Vina-generated poses and thus strongly improve their predicted binding affinity. The software is available at http://istar.cse.cuhk.edu.hk/rf-score-3.tgz and http://crcm.marseille.inserm.fr/fileadmin/rf-score-3.tgz
著者Li HJ, Leung KS, Wong MH, Ballester PJ
期刊名稱Molecular Informatics
出版年份2015
月份2
日期1
卷號34
期次2-3
出版社WILEY-V C H VERLAG GMBH
頁次115 - 126
國際標準期刊號1868-1743
電子國際標準期刊號1868-1751
語言英式英語
關鍵詞Docking; Drug lead optimization; Machine learning
Web of Science 學科類別Chemistry, Medicinal; Computer Science; Computer Science, Interdisciplinary Applications; Mathematical & Computational Biology; Pharmacology & Pharmacy

上次更新時間 2020-27-11 於 01:52