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Decision Tree for Prediction of Binding Affinity.

Amauri Duarte da Silva1, Walter Filgueira de Azevedo2

  • 1Graduate Program in Information Technologies and Health Management, Federal University of Health Sciences of Porto Alegre, Porto Alegre, RS, Brazil.

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Summary

Machine learning models, specifically Decision Trees, can predict protein-ligand binding affinity. This approach integrates docking results for drug discovery, offering a new tool for analyzing protein systems.

Keywords:
Artificial intelligenceBiological systemsComplex systemsDecision treeMachine learningSAnDReS 2.0Scoring function space

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Machine learning

Background:

  • Predicting protein-ligand binding affinity is crucial for early-stage drug discovery.
  • Machine learning (ML) offers powerful tools for developing accurate scoring functions.
  • Integrating docking simulations with ML enhances the modeling of protein systems.

Purpose of the Study:

  • To apply Decision Tree regression models for predicting protein-ligand interactions.
  • To introduce the SKReg4Model program for exploring scoring function space.
  • To provide a practical framework for building predictive models in drug discovery.

Main Methods:

  • Utilized Decision Trees, a robust ML technique, for regression analysis.
  • Employed features from Vina Force Field and energy terms from docking programs (e.g., Molegro Virtual Docker).
  • Developed the SKReg4Model program, based on SAnDReS 2.0 and Scikit-Learn, for model building.

Main Results:

  • Demonstrated the efficacy of Decision Trees in modeling complex protein-ligand interactions.
  • Showcased the application of SKReg4Model for regression tasks in computational drug discovery.
  • Made datasets and a Jupyter Notebook available for reproducibility and further research.

Conclusions:

  • Decision Tree regression models are effective for predicting binding affinity.
  • SKReg4Model provides a valuable tool for drug discovery research.
  • The integration of ML and docking methods advances the field of computational chemistry.