Lin_F9: A Linear Empirical Scoring Function for Protein-Ligand Docking

Chao Yang, Yingkai Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Molecular docking is one of the most widely used computational tools in structure-based drug design and is critically dependent on accuracy and robustness of the scoring function. In this work, we introduce a new scoring function Lin_F9, which is a linear combination of nine empirical terms, including a unified metal bond term to specifically describe metal-ligand interactions. Parameters in Lin_F9 are obtained with a multistage fitting protocol using explicit water-included structures. For the CASF-2016 benchmark test set, Lin_F9 achieves the top scoring power among all 34 classical scoring functions for both original crystal poses and locally optimized poses with Pearson correlation coefficients (R) of 0.680 and 0.687, respectively. Meanwhile, in comparison with Vina, Lin_F9 achieves consistently better scoring power and ranking power with various types of protein-ligand complex structures that mimic real docking applications, including end-to-end flexible docking for the CASF-2016 benchmark test set using a single or an ensemble of protein receptor structures, as well as for D3R Grand Challenge (GC4) test sets. Lin_F9 has been implemented in a fork of Smina as an optional built-in scoring function that can be used for docking applications as well as for further improvement of scoring functions and docking protocols. Lin_F9 is accessible through https://yzhang.hpc.nyu.edu/Lin_F9/.

Original languageEnglish (US)
Number of pages15
JournalJournal of Chemical Information and Modeling
Volume61
Issue number9
DOIs
StateAccepted/In press - 2021

Keywords

  • Drug Design
  • Ligands
  • Molecular Docking Simulation
  • Proteins
  • Water

ASJC Scopus subject areas

  • Chemical Engineering(all)
  • Chemistry(all)
  • Library and Information Sciences
  • Computer Science Applications

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