Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Conserved Binding Sites01:49

Conserved Binding Sites

4.2K
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...
4.2K
Ligand Binding Sites02:40

Ligand Binding Sites

12.7K
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...
12.7K

You might also read

Related Articles

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

Sort by
Same author

Ensemble Machine Learning for Interpretable Prediction of Acute Toxicity in Metal-Organic Framework Linkers.

Journal of chemical information and modeling·2026
Same author

Effective Dielectric Constant of Water at the Interface with Charged C<sub>60</sub> Fullerenes.

The journal of physical chemistry. B·2019
See all related articles

Related Experiment Video

Updated: Jun 7, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

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

Published on: May 9, 2025

404

Machine Learning to Predict Potential Energy Surface of Resveratrol Drug: A Quantum-Level Calculation.

Hossein Shirani1, Seyed Majid Hashemianzadeh1

  • 1Molecular Simulation Research Laboratory, Department of Chemistry, Iran University of Science and Technology, Tehran 16846-13114, Iran.

ACS Medicinal Chemistry Letters
|November 20, 2024
PubMed
Summary

This study validates the ANI-1x deep learning model for accurately predicting the potential energy surfaces of Resveratrol, an antiparkinsonian drug. This quantum-level machine learning approach significantly speeds up computational drug discovery research.

More Related Videos

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

1.9K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.2K

Related Experiment Videos

Last Updated: Jun 7, 2025

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
10:29

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

Published on: May 9, 2025

404
Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia
11:06

Network Pharmacology Prediction and Metabolomics Validation of the Mechanism of Fructus Phyllanthi against Hyperlipidemia

Published on: April 7, 2023

1.9K
Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method
05:51

Isotopic Effect in Double Proton Transfer Process of Porphycene Investigated by Enhanced QM/MM Method

Published on: July 19, 2019

6.2K

Area of Science:

  • Quantum chemistry
  • Computational drug discovery
  • Machine learning in pharmacology

Background:

  • Resveratrol is a potential antiparkinsonian drug.
  • Accurate potential energy surfaces are crucial for understanding drug behavior.
  • Traditional methods for calculating these surfaces can be computationally intensive.

Purpose of the Study:

  • To investigate the efficacy of the ANI-1x neural network potential for predicting Resveratrol's potential energy surfaces.
  • To assess the speed and accuracy of this quantum-level machine learning approach.
  • To provide insights for pharmaceutical computational research and drug design.

Main Methods:

  • The ANI-1x neural network potential was trained on density functional theory (DFT) data.
  • The model's performance was validated using DFT calculations at the wB97X/6-31G(d) level of theory.
  • Potential energy surfaces of Resveratrol were forecasted using the trained ANI-1x model.

Main Results:

  • The ANI-1x model accurately predicted the potential energy surfaces of Resveratrol.
  • The quantum-level machine learning approach achieved this in a significantly reduced computing time.
  • Comprehensive validation confirmed the reliability of the ANI-1x technique for this molecule.

Conclusions:

  • The ANI-1x deep learning technique offers a computationally efficient method for drug discovery.
  • This approach provides valuable insights for medicinal chemistry and pharmaceutical research.
  • The study demonstrates the potential of machine learning in accelerating drug design and development.