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Updated: Jan 28, 2026

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

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Published on: June 20, 2025

Allosteric Modulator Discovery: From Serendipity to Structure-Based Design.

Shaoyong Lu^1,2, Xinheng He¹, Duan Ni¹

¹Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Renji Hospital , Shanghai Jiao-Tong University School of Medicine , Shanghai 200025 , China.

Journal of Medicinal Chemistry

|March 1, 2019

Summary

This summary is machine-generated.

Discovering allosteric modulators, which offer pharmacological advantages over orthosteric ligands, is now feasible. Integrated computational and experimental approaches guide structure-based discovery, overcoming traditional serendipitous methods.

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A Kinetic Fluorescence-based Ca2+ Mobilization Assay to Identify G Protein-coupled Receptor Agonists, Antagonists, and Allosteric Modulators

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Area of Science:

Pharmacology and Drug Discovery
Computational Chemistry
Structural Biology

Background:

Allosteric modulators offer superior pharmacological advantages compared to orthosteric ligands.
Historically, allosteric modulator discovery has relied on serendipity due to the complexity of allosteric modulation.
Advances in understanding allosteric mechanisms and structural data availability have enabled computational approaches.

Purpose of the Study:

To outline the evolution of the allosteric concept.
To describe advantages and challenges in allosteric modulator discovery.
To highlight integrated computational and experimental strategies for structure-based allosteric modulator discovery.

Main Methods:

Review of the evolution of allosteric concepts.
Analysis of current computational approaches for drug discovery.
Integration of experimental techniques with computational modeling.
Case studies of successful combined applications.

Main Results:

Structure-based discovery of allosteric modulators is increasingly feasible.
Computational methods, guided by structural data, accelerate the identification of allosteric modulators.
Integrated approaches combining computational and experimental methods yield successful outcomes.

Conclusions:

The traditional serendipitous discovery of allosteric modulators is being replaced by rational, structure-based approaches.
An integrated computational and experimental paradigm significantly enhances the feasibility of discovering novel allosteric modulators.
This perspective aims to raise awareness of the potential of structure-based allosteric modulator discovery.