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Ligand Binding Sites

Ligand Binding Sites

Ligand Binding Sites

Ligand Binding Sites

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...

Ligand Binding and Linkage

Ligand Binding and Linkage

Ligand Binding and Linkage

Ligand Binding and Linkage

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...

Conserved Binding Sites

Conserved Binding Sites

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.
Binding sites are often located in large pockets, and if their location on a protein’s surface is unknown, it can be predicted using various approaches. The energetic method computationally...

Conserved Binding Sites

Conserved Binding Sites

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Related Experiment Video

Updated: Jan 14, 2026

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps

Published on: July 19, 2024

Hierarchical affinity landscape navigation through learning a shared pocket-ligand space.

Bin Feng¹, Zijing Liu¹, Hao Li¹

¹International Digital Economy Academy (IDEA), Shenzhen, China.

Patterns (New York, N.Y.)

|October 27, 2025

Summary

This summary is machine-generated.

LigUnity, a novel foundation model, unifies ligand and protein pocket structures for accurate drug affinity prediction. This integrated approach enhances both virtual screening and hit-to-lead optimization in drug discovery.

Keywords:

affinity prediction hit-to-lead optimization pharmacophore ranking scaffold discrimination shared space virtual screening

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and development
Structural biology and molecular modeling

Background:

Protein binding pocket structure dictates ligand binding affinity through interactions and spatial fit.
Current drug discovery methods often separate virtual screening and hit-to-lead optimization due to speed-accuracy trade-offs.
Integrating these tasks allows for broader chemical exploration and focused optimization on key substructures.

Purpose of the Study:

To introduce LigUnity, a foundation model for unified affinity prediction by embedding ligands and pockets in a shared space.
To enable simultaneous coarse-grained (scaffold discrimination) and fine-grained (pharmacophore ranking) affinity prediction.
To demonstrate LigUnity's effectiveness and versatility across diverse drug discovery benchmarks.

Main Methods:

Development of LigUnity, a foundation model integrating ligand and protein pocket representations.
Utilizing scaffold discrimination for active/inactive classification and pharmacophore ranking for pocket-specific ligand preference.
Validation across eight benchmarks in six settings, including virtual screening and hit-to-lead optimization scenarios.

Main Results:

LigUnity significantly outperforms 24 existing methods in virtual screening (>50% improvement) with robust generalization to new targets.
Achieves state-of-the-art performance in hit-to-lead optimization across various data splitting strategies.
Demonstrates cost-efficiency compared to free energy perturbation methods and successful application in an active learning framework for TYK2 ligand discovery.

Conclusions:

LigUnity establishes itself as a versatile foundation model for affinity prediction.
Offers broad applicability across the entire drug discovery pipeline, from screening to optimization.
Represents a significant advancement in computational drug design, improving efficiency and accuracy.