AA-Score: a New Scoring Function Based on Amino Acid-Specific Interaction for Molecular Docking

Xiaolin Pan, Hao Wang, Yueqing Zhang, Xingyu Wang, Cuiyu Li, Changge Ji, John Z.H. Zhang

Research output: Contribution to journalArticlepeer-review


The protein−ligand scoring function plays an important role in computer-aided drug discovery and is heavily used in virtual screening and lead optimization. In this study, we developed a new empirical protein−ligand scoring function with amino acid-specific interaction components for hydrogen bond, van der Waals, and electrostatic interactions. In addition, hydrophobic, πstacking, π-cation, and metal−ligand interactions are also included in the new scoring function. To better evaluate the performance of the AA-Score, we generated several new test sets for evaluation of scoring, ranking, and docking performances, respectively. Extensive tests show that AA-Score performs well on scoring, docking, and ranking as compared to other widely used traditional scoring functions. The performance improvement of AA-Score benefits from the decomposition of individual interaction into amino acid-specific types. To facilitate applications, we developed an easy-to-use tool to analyze protein−ligand interaction fingerprint and predict binding affinity using the AA-Score. The source code and associated running examples can be found at https://github.com/xundrug/AA-Score-Tool.

Original languageEnglish (US)
Pages (from-to)2499-2509
Number of pages11
JournalJournal of Chemical Information and Modeling
Issue number10
StatePublished - May 23 2022

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Computer Science Applications
  • Library and Information Sciences


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