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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

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...

Ligand Binding and Linkage

Ligand Binding and Linkage

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

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DeepMice: a novel protein-ligand molecular docking model based on multilevel mapping modules.

Jiawei Liu¹, Qi Wang^2,3, Yanzhao Jin^2,3

¹Ministry of Education Key Laboratory of Molecular and Cellular Biology, Hebei Anti-Tumor Molecular Target Technology Innovation Center, Hebei Research Center of the Basic Discipline of Cell Biology; College of Life Science, Hebei Normal University, Shijiazhuang, 050024, People's Republic of China.

Molecular Diversity

|October 5, 2025

Summary

DeepMice, an AI-driven molecular docking framework, enhances drug discovery by accurately predicting protein-ligand binding. Its novel approach improves virtual screening efficiency and accuracy for accelerated new drug development.

Keywords:

Artificial intelligence Graph neural network Mixture density network Molecular docking Scoring function

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Area of Science:

Computational chemistry
Artificial intelligence in drug discovery
Molecular modeling

Background:

Accurate prediction of protein-ligand binding is crucial for drug discovery.
Existing molecular docking methods face challenges in accuracy and efficiency.
The development of advanced computational tools is needed to accelerate the drug development pipeline.

Purpose of the Study:

To introduce DeepMice, a novel artificial intelligence-based molecular docking framework.
To improve the accuracy of protein-ligand binding conformation prediction.
To enhance the efficiency of virtual screening for drug discovery.

Main Methods:

Utilized a graph transformer network (GTN) for enhanced representation precision in scoring functions.
Incorporated a multilevel mapping module to reduce computational complexity.
Employed a hybrid conformational search strategy combining Differential Evolution (DE) and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms.

Main Results:

DeepMice outperformed existing virtual screening technologies (Glide SP, RTMScore) on DEKOIS2.0 and DUD-E datasets.
Achieved superior performance in AUROC, BEDROC, and EF values.
Demonstrated advanced molecular docking capabilities on the CASF-2016 standard test set, considering multiscale protein structures.

Conclusions:

DeepMice is an efficient and accurate molecular docking model.
The framework accelerates new drug research and development.
DeepMice provides a powerful, freely available tool for drug discovery.