Author:
Zhang Xujun,Shen Chao,Jiang Dejun,Zhang Jintu,Ye Qing,Xu Lei,Hou Tingjun,Pan Peichen,Kang Yu
Abstract
AbstractMachine learning-based scoring functions (MLSFs) have shown potential for improving virtual screening capabilities over classical scoring functions (SFs). Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein–ligand interactions are always limited, which may affect the overall accuracy and efficiency. Here, we propose a new SF called TB-IECS (theory-based interaction energy component score), which combines energy terms from Smina and NNScore version 2, and utilizes the eXtreme Gradient Boosting (XGBoost) algorithm for model training. In this study, the energy terms decomposed from 15 traditional SFs were firstly categorized based on their formulas and physicochemical principles, and 324 feature combinations were generated accordingly. Five best feature combinations were selected for further evaluation of the model performance in regard to the selection of feature vectors with various length, interaction types and ML algorithms. The virtual screening power of TB-IECS was assessed on the datasets of DUD-E and LIT-PCBA, as well as seven target-specific datasets from the ChemDiv database. The results showed that TB-IECS outperformed classical SFs including Glide SP and Dock, and effectively balanced the efficiency and accuracy for practical virtual screening.
Funder
Natural Science Foundation of Zhejiang Province
National Key Research and Development Program of China
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Springer Science and Business Media LLC
Subject
Library and Information Sciences,Computer Graphics and Computer-Aided Design,Physical and Theoretical Chemistry,Computer Science Applications
Cited by
3 articles.
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