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Related Concept Videos

Protein-Drug Binding: Determination Methods01:22

Protein-Drug Binding: Determination Methods

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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
Indirect methods involve isolating the bound drug from its free form in biological samples such as blood, serum, or plasma. These techniques aim to measure the percentage of drugs bound to proteins. Equilibrium dialysis is a commonly used method where the free drug concentration at equilibrium is measured by separating the bound...
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Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Related Experiment Video

Updated: Jul 15, 2025

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
08:49

Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis

Published on: June 20, 2025

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Benchmarking Cross-Docking Strategies for Structure-Informed Machine Learning in Kinase Drug Discovery.

David Schaller1,2, Clara D Christ3, John D Chodera2

  • 1In Silico Toxicology and Structural Bioinformatics, Institute of Physiology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

Biorxiv : the Preprint Server for Biology
|September 25, 2023
PubMed
Summary

Accurately predicting protein:ligand complex structures is key for machine learning in drug discovery. Combining docking methods, particularly Posit, significantly improves the prediction of binding poses for kinase inhibitors.

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Area of Science:

  • Computational chemistry
  • Structural biology
  • Machine learning in drug discovery

Background:

  • Machine learning (ML) is revolutionizing drug discovery, especially small molecule design.
  • Accurate prediction of protein:ligand complex structures is crucial for ML-based bioactivity prediction.
  • Current methods face limitations in reliably and automatically predicting these complex structures.

Conclusions:

  • Ligand-biased and multi-structure docking strategies enhance the accuracy of protein:ligand complex structure prediction.
  • The Posit approach offers an efficient method for generating reliable binding poses.
  • The findings, while focused on kinases, are potentially transferable to other protein families for ML applications.