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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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New machine learning and physics-based scoring functions for drug discovery.

Isabella A Guedes1,2, André M S Barreto1, Diogo Marinho1

  • 1Laboratório Nacional de Computação Científica, Petrópolis, 25651-075, Brazil.

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|February 5, 2021
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Summary
This summary is machine-generated.

New scoring functions, DockTScore, improve in silico drug discovery by combining physics-based terms and machine learning for accurate protein-ligand binding affinity prediction.

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Accurate prediction of binding affinity is crucial for in silico drug discovery.
  • Current scoring functions exhibit heterogeneous performance across different target classes.
  • There is a need for scoring functions that better represent protein-ligand recognition using physics-based descriptors.

Purpose of the Study:

  • To develop novel empirical scoring functions, named DockTScore, integrating physics-based terms and machine learning.
  • To create target-specific scoring functions for proteases and protein-protein interactions.
  • To enhance the prediction of binding energy and ranking of potential drug candidates.

Main Methods:

  • Developed empirical scoring functions (DockTScore) using physics-based terms and machine learning algorithms (MLR, SVM, RF).
  • Incorporated optimized MMFF94S force-field terms, solvation, lipophilic interactions, and ligand torsional entropy.
  • Derived general and target-specific functions for proteases and protein-protein interactions.

Main Results:

  • DockTScore functions demonstrated competitive performance against leading scoring functions.
  • Achieved accurate binding energy prediction and ranking on four DUD-E datasets.
  • Target-specific functions showed promise for specialized drug discovery applications.

Conclusions:

  • DockTScore offers a significant advancement in scoring function development for in silico drug design.
  • The functions are valuable for general protein targets and specific classes like proteases and protein-protein interactions.
  • The MLR-based DockTScore is publicly available for research use.