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Converging a Knowledge-Based Scoring Function: DrugScore2018.

Jonas Dittrich1, Denis Schmidt1, Christopher Pfleger1

  • 1Mathematisch-Naturwissenschaftliche Fakultät, Institut für Pharmazeutische und Medizinische Chemie , Heinrich-Heine-Universität Düsseldorf , 40225 Düsseldorf , Germany.

Journal of Chemical Information and Modeling
|December 5, 2018
PubMed
Summary
This summary is machine-generated.

DrugScore2018, a new knowledge-based scoring function, utilizes a large dataset of X-ray structures to improve protein-ligand binding predictions. It demonstrates superior performance in scoring, ranking, and docking, aiding structure-based drug design.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Drug discovery

Background:

  • Knowledge-based scoring functions are crucial for predicting protein-ligand interactions in drug discovery.
  • Existing functions often rely on limited training data, impacting their accuracy and scope.
  • There is a need for improved scoring functions that incorporate diverse structural information.

Purpose of the Study:

  • To introduce DrugScore2018, an enhanced knowledge-based scoring function.
  • To leverage a significantly larger and more diverse dataset of X-ray complex structures for improved potential derivation.
  • To evaluate the performance of DrugScore2018 in scoring, ranking, and docking tasks.

Main Methods:

  • Utilized a training dataset of nearly 40,000 X-ray complex structures, the largest to date.
  • Derived pair potentials for an expanded set of atom types, including halogens and metal ions.
  • Conducted comprehensive evaluations on the CASF-2013 dataset, comparing DrugScore2018 with over 30 other scoring functions.
  • Applied DrugScore2018 in large-scale docking trials using AutoDock3 and the PDBbind 2016 dataset.

Main Results:

  • DrugScore2018 demonstrated improved or comparable performance across scoring, ranking, and docking tests compared to previous versions and other leading functions.
  • The function achieved high accuracy in reproducing docked poses (within 2 Å RMSD) in over 75% of cases in large-scale docking trials.
  • Potentials derived solely from crystallographic data showed robust performance without further affinity or decoy data adaptation.
  • The training data size and quality were shown to be converged for DrugScore2018 potentials.

Conclusions:

  • DrugScore2018 represents a significant advancement in knowledge-based scoring functions.
  • Its performance in comprehensive tests suggests it is a competitive tool for structure-based ligand design.
  • The function's ability to accurately predict binding poses from crystallographic data alone highlights its utility and robustness.