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Due to the high computational cost in the process of feature generation, the numbers of descriptors used in MLSFs and the characterization of protein\u2013ligand 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.<\/jats:p>","DOI":"10.1186\/s13321-023-00731-x","type":"journal-article","created":{"date-parts":[[2023,7,4]],"date-time":"2023-07-04T13:23:12Z","timestamp":1688476992000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["TB-IECS: an accurate machine learning-based scoring function for virtual screening"],"prefix":"10.1186","volume":"15","author":[{"given":"Xujun","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Chao","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Dejun","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Jintu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Qing","family":"Ye","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Tingjun","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Peichen","family":"Pan","sequence":"additional","affiliation":[]},{"given":"Yu","family":"Kang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,4]]},"reference":[{"key":"731_CR1","doi-asserted-by":"publisher","first-page":"281","DOI":"10.1007\/s10822-007-9114-2","volume":"21","author":"AN Jain","year":"2007","unstructured":"Jain AN (2007) Surflex-Dock 2 1: Robust performance from ligand energetic modeling, ring flexibility and knowledge-based search. 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