Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 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:

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...

Protein-protein Interfaces

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...

Metal-Ligand Bonds

Metal-Ligand Bonds

The hemoglobin in the blood, the chlorophyll in green plants, vitamin B-12, and the catalyst used in the manufacture of polyethylene all contain coordination compounds. Ions of the metals, especially the transition metals, are likely to form complexes.
In these complexes, transition metals form coordinate covalent bonds, a kind of Lewis acid-base interaction in which both of the electrons in the bond are contributed by a donor (Lewis base) to an electron acceptor (Lewis acid). The Lewis acid in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Solvent Effects on Nonadiabatic Coupling: Interfacing Time-Dependent Density Functional Theory with the Effective Fragment Potential Method.

Journal of chemical theory and computation·2026

Same author

Prediction of stability constants of metal-ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity.

The Journal of chemical physics·2024

Same author

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification.

Analytical chemistry·2023

Same author

Structure and Magnetic Properties of Homoleptic Trivalent Tris(alkyl)lanthanides.

Inorganic chemistry·2023

Same author

High-Throughput Non-targeted Chemical Structure Identification Using Gas-Phase Infrared Spectra.

Analytical chemistry·2021

Same author

Combined quantum Monte Carlo - effective fragment molecular orbital method: fragmentation across covalent bonds.

Physical chemistry chemical physics : PCCP·2021

Search research articles

Related Experiment Video

Updated: Jan 12, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

A Comprehensive Machine Learning Model for Metal-Ligand Binding Prediction: Applications in Chemistry and Biology.

Erandika Karunaratne^1,2, Federico Zahariev^1,2, Marilú Pérez García^1,2

¹Critical Materials Innovation Hub, Ames National Laboratory, Ames, Iowa 50011, United States.

Journal of Chemical Information and Modeling

|October 31, 2025

Summary

A new machine-learning model accurately predicts metal-ligand binding constants using extensive experimental data. This tool offers a fast, cost-effective alternative to traditional methods for diverse applications.

More Related Videos

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Related Experiment Videos

Last Updated: Jan 12, 2026

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

Published on: January 26, 2024

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA

Published on: February 23, 2024

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational Chemistry
Materials Science
Chemical Engineering

Background:

Accurate prediction of metal-ligand binding constants is crucial for various scientific and industrial applications.
Existing computational approaches are often limited in scope, focusing on specific metals or ligand families.
There is a need for a generalized, efficient, and accessible tool for predicting these binding constants.

Purpose of the Study:

To develop and validate a machine-learning model for predicting metal-ligand binding constants.
To create a generalized model that surpasses the limitations of existing approaches.
To provide a computationally efficient alternative to traditional methods like density functional theory (DFT).

Main Methods:

Utilized the open-source Chemprop software to develop a machine-learning model.
Trained the model on over 30,000 experimental log K1 values, encompassing protonation and metal-ligand stability constants.
Incorporated SMILES-based molecular representations, metal ion descriptors, and experimental conditions into the model.

Main Results:

The best-performing model achieved an external test R² value of 0.942 and an MAE of 0.834.
A simplified "SMILES-only" model also demonstrated accurate predictions and preserved binding trends.
The "SMILES-only" model showed comparable performance to DFT calculations with significantly reduced computational resources.

Conclusions:

The developed machine-learning model provides a rapid, reliable, and broadly applicable tool for predicting metal-ligand binding constants.
The model demonstrates effectiveness across diverse fields such as bioinorganic chemistry, heavy metal remediation, and sensor development.
The accessible "SMILES-only" version serves as a valuable screening tool for researchers and industry professionals lacking extensive computational expertise.