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Related Concept Videos

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Conserved Binding Sites01:49

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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The equilibrium binding constant (Kb) quantifies the strength of a protein-ligand interaction. Kb can be calculated as follows when the reaction is at equilibrium:
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Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
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Enhancing CYP450-Ligand Binding Predictions: A Comparative Analysis of Ligand-Based and Hybrid Machine Learning

Anastasiia Tikhonova1,2, Eric Chun Yong Chan2, Hao Fan1,3,4,5,6

  • 1Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore 138671, Republic of Singapore.

Journal of Chemical Information and Modeling
|January 13, 2026
PubMed
Summary

Predicting cytochrome P450 (CYP450) ligand binding is crucial for drug discovery. A new hybrid machine learning model improves prediction accuracy over existing methods, aiding in early-stage safety and efficacy assessments.

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Area of Science:

  • Pharmacology and Computational Chemistry
  • Drug Discovery and Development

Background:

  • Cytochrome P450 (CYP450) metabolism significantly impacts drug efficacy, safety, and adverse reactions.
  • Experimental determination of CYP450-ligand interactions is time-consuming and resource-intensive.
  • Existing computational methods, particularly ligand-based approaches, often neglect crucial protein-ligand structural details.

Purpose of the Study:

  • To develop a hybrid machine learning framework for accurate prediction of CYP450 ligand binding.
  • To overcome limitations of current computational methods in capturing protein-ligand structural intricacies.
  • To provide a robust computational tool for prioritizing CYP450 binding assessments in early drug discovery.

Main Methods:

  • Developed a hybrid machine learning model integrating ligand, protein, and protein-ligand interaction descriptors.
  • Incorporated molecular docking parameters, rescoring function components, and structural interaction fingerprints (SIFt).
  • Evaluated model performance on CYP1A2 and CYP17A1 isoforms using cross-validation.

Main Results:

  • The hybrid model demonstrated superior predictive accuracy compared to stand-alone molecular docking and ligand-based methods.
  • Benchmarking showed enhanced performance against state-of-the-art tools like SwissADME and ADMETlab 3.0.
  • The framework effectively integrates diverse data types for improved binding prediction.

Conclusions:

  • The developed hybrid machine learning framework offers a versatile and accurate approach for CYP450 ligand binding prediction.
  • This computational tool can significantly accelerate early-stage drug discovery by efficiently prioritizing compounds.
  • The study advances computational strategies for assessing drug metabolism and safety profiles.