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Related Concept Videos

Protein-protein Interfaces02:04

Protein-protein Interfaces

Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a polypeptide...
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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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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...

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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors
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Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Advances in computational methods for PROTAC drug discovery.

Massyel S Martinez-Cortés1, Carlos A Velázquez-Martínez2, José L Medina-Franco1

  • 1DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico.

Drug Discovery Today
|February 19, 2026
PubMed
Summary

Proteolysis-targeting chimeras (PROTACs) offer a novel drug discovery approach by degrading target proteins. This review details computational tools that aid PROTAC design, optimization, and clinical translation.

Keywords:
Artificial intelligencechemoinformaticscomputer-aided molecular designmachine learningmolecular dynamics

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Published on: July 8, 2025

Area of Science:

  • Drug Discovery
  • Medicinal Chemistry
  • Computational Chemistry

Background:

  • Proteolysis-targeting chimeras (PROTACs) represent a significant advancement in drug discovery, offering targeted protein degradation.
  • PROTACs present unique design and optimization challenges due to their complex structures and non-traditional drug-like properties.

Purpose of the Study:

  • To provide a comprehensive overview of computational advancements supporting PROTAC development.
  • To highlight tools for key aspects of PROTAC design, including warhead and linker selection, ternary complex modeling, and prediction of degradation efficiency and ADMET profiles.
  • To discuss current limitations and future directions for enhancing PROTAC design and accelerating clinical translation.

Main Methods:

  • Review of recent literature on computational tools in PROTAC development.
  • Categorization of computational approaches including chemoinformatics, structural bioinformatics, molecular modeling, and machine learning.
  • Analysis of tools for specific PROTAC design elements and property predictions.

Main Results:

  • Identification of diverse computational resources applicable to PROTAC research.
  • Demonstration of computational tools aiding in warhead/linker design and ternary complex modeling.
  • Highlighting predictive capabilities for degradation efficiency and ADMET properties.

Conclusions:

  • Computational strategies are crucial for overcoming PROTAC design challenges.
  • Advancements in chemoinformatics, bioinformatics, and machine learning accelerate PROTAC development.
  • Enhanced computational approaches are key to improving PROTAC design effectiveness and clinical translation.