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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 Sites

Ligand Binding Sites

The Two-State Receptor Model

The Two-State Receptor Model

The two-state receptor model explains a drug's interaction with receptors, such as G protein-coupled receptors and ligand-gated ion channels, to induce or inhibit a biological response. When no natural ligands are present, a receptor exists in an equilibrium of inactive (Ri) and active (Ra) conformations. The inactive form does not produce a response, while the active form generates a basal effect known as constitutive activity.
The binding affinity of a drug determines its interaction with...

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...

The Equilibrium Binding Constant and Binding Strength

The Equilibrium Binding Constant and Binding Strength

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:

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...

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Related Experiment Video

Updated: Dec 11, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

Ligand-based Activity Cliff Prediction Models with Applicability Domain.

Shunsuke Tamura¹, Tomoyuki Miyao^1,2, Kimito Funatsu^1,2,3

¹Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara, 630-0192, Japan.

Molecular Informatics

|August 25, 2020

Summary

This summary is machine-generated.

This study improves activity cliff prediction for drug discovery by incorporating applicability domains and considering substituent attachment points. This enhanced method accurately predicts cliffs even with distinct molecular scaffolds in training and testing data.

Keywords:

activity cliff prediction applicability domain drug design ligand-based approach structure-activity relationships

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Area of Science:

Medicinal Chemistry
Computational Chemistry
Drug Discovery

Background:

Activity cliffs (ACs) are crucial in drug discovery, representing structurally similar compounds with significant potency differences.
Existing AC prediction models, often based on matched molecular pair (MMP) cliffs, have limitations in extrapolation due to scaffold overlap and unconsidered attachment points.

Purpose of the Study:

To enhance ligand-based AC prediction using molecular fingerprints.
To improve model extrapolation by incorporating applicability domains and considering the local chemical environment around attachment points.

Main Methods:

Utilized molecular fingerprints for AC prediction.
Incorporated an applicability domain defined by R-path fingerprints to capture the local environment of attachment points.
Evaluated model extrapolation using MMP cliffs across nine biological targets.

Main Results:

The developed AC prediction method demonstrated accurate prediction of MMP cliffs for nine biological targets.
Including training MMPs with scaffolds distinct from test MMPs significantly improved prediction accuracy compared to models trained on similar scaffolds.

Conclusions:

The proposed method enhances the prediction of activity cliffs, particularly improving extrapolation capabilities.
Considering the local environment around attachment points and using diverse training data are key for robust AC prediction in drug discovery.