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Exploring the Scoring Function Space with Lasso Regression.

Amauri Duarte da Silva1, Stéphanie Baud2, Walter Filgueira de Azevedo3

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This summary is machine-generated.

Artificial intelligence (AI) accelerates drug discovery by improving protein-ligand interaction analysis. This study introduces Lasso regression in SAnDReS 2.0 for predicting anticancer drug efficacy, enhancing computational methods.

Keywords:
AlphaFoldArtificial intelligenceCDK2Docking screensLassoMachine learningScoring function space

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Area of Science:

  • Computational biology
  • Drug discovery
  • Artificial intelligence in medicine

Background:

  • Protein-ligand interactions are crucial for drug discovery.
  • Artificial intelligence (AI) has shown promise in modeling protein structures and scoring functions.
  • AI can potentially accelerate drug discovery and improve computational method reliability.

Purpose of the Study:

  • To present the Lasso regression method within SAnDReS 2.0.
  • To demonstrate building a regression model for predicting protein target inhibition in anticancer drug development.
  • To provide insights into developing models for binding affinity prediction using scoring functions.

Main Methods:

  • Utilizing the Lasso regression method from SAnDReS 2.0.
  • Developing a regression model to predict protein target inhibition.
  • Focusing on open-source software and freely accessible databases.
  • Making all discussed code available on GitHub.

Main Results:

  • The Lasso regression method was applied to build a predictive model.
  • The study provides a framework for developing models to predict binding affinity.
  • The approach utilizes accessible tools for reproducibility.

Conclusions:

  • AI, specifically Lasso regression, offers a powerful approach to model complex biological systems like protein-ligand interactions.
  • This method can significantly contribute to accelerating the drug discovery pipeline for anticancer agents.
  • The use of open-source tools and accessible databases promotes transparency and wider adoption in computational drug discovery.