Improving structure-based virtual screening performance via learning from scoring function components

Guo-Li Xiong, Wen-Ling Ye, Chao Shen, Ai-Ping Lu, Ting-Jun Hou*, Dong-Sheng Cao*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

34 Citations (Scopus)


Scoring functions (SFs) based on complex machine learning (ML) algorithms have gradually emerged as a promising alternative to overcome the weaknesses of classical SFs. However, extensive efforts have been devoted to the development of SFs based on new protein–ligand interaction representations and advanced alternative ML algorithms instead of the energy components obtained by the decomposition of existing SFs. Here, we propose a new method named energy auxiliary terms learning (EATL), in which the scoring components are extracted and used as the input for the development of three levels of ML SFs including EATL SFs, docking-EATL SFs and comprehensive SFs with ascending VS performance. The EATL approach not only outperforms classical SFs for the absolute performance (ROC) and initial enrichment (BEDROC) but also yields comparable performance compared with other advanced ML-based methods on the diverse subset of Directory of Useful Decoys: Enhanced (DUD-E). The test on the relatively unbiased actives as decoys (AD) dataset also proved the effectiveness of EATL. Furthermore, the idea of learning from SF components to yield improved screening power can also be extended to other docking programs and SFs available.
Original languageEnglish
Article numberbbaa094
Number of pages14
JournalBriefings in Bioinformatics
Issue number3
Publication statusPublished - May 2021

Scopus Subject Areas

  • Information Systems
  • Molecular Biology

User-Defined Keywords

  • docking program
  • machine learning
  • scoring function (SF)
  • virtual screening


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