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

You might also read

Related Articles

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

Sort by

Same author

Fundamental Discoveries in Enzymology through Undergraduate Research.

ACS omega·2025

Same author

A Review of Methionine and Cysteine: Their Roles as Sinks for Reactive Oxygen and Nitrogen Species, Including Bioinformatic Analysis of Their Percent Compositions in Metabolic Proteins.

Indian journal of clinical biochemistry : IJCB·2025

Same author

Graph Convolutional Neural Network-Enabled Frontier Molecular Orbital Prediction: A Case Study with Neurotransmitters and Antidepressants.

Journal of chemical information and modeling·2025

Same author

Efficient and Explainable Virtual Screening of Molecules through Fingerprint-Generating Networks Integrated with Artificial Neural Networks.

ACS omega·2025

Same author

Polyethylene Glycol Impacts Conformation and Dynamics of <i>Escherichia coli</i> Prolyl-tRNA Synthetase Via Crowding and Confinement Effects.

Biochemistry·2024

Same author

Writing a literature review as a class project in an upper-level undergraduate biochemistry course.

Biochemistry and molecular biology education : a bimonthly publication of the International Union of Biochemistry and Molecular Biology·2024

Search research articles

Related Experiment Video

Updated: Jun 26, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Virtual Screening of Molecules via Neural Fingerprint-based Deep Learning Technique.

Rivaaj Monsia¹, Sudeep Bhattacharyya¹

¹University of Wisconsin- Eau Claire.

Research Square

|May 20, 2024

Summary

A new machine learning model uses neural network fingerprints for faster, more efficient drug screening. This computational drug discovery approach accurately identifies molecules with favorable drug-target binding affinities.

Keywords:

Artificial neural network deep learning drug design machine learning neural network-based fingerprinting structure-based inhibitor design

More Related Videos

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 26, 2025

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Area of Science:

Computational chemistry
Machine learning in drug discovery
Bioinformatics

Background:

Drug screening is crucial for identifying potential therapeutics.
Existing methods for molecular screening can be time-consuming and computationally intensive.
Machine learning offers promising avenues for accelerating drug discovery processes.

Purpose of the Study:

To develop and optimize a novel machine learning-based drug screening technique.
To compare the efficiency of neural network-derived fingerprints against fixed Morgan fingerprints for drug-target binding affinity prediction.
To enhance the speed and accuracy of computational drug discovery.

Main Methods:

A machine learning model was developed utilizing convolutional neural network-derived fingerprints.
The model's weights were optimized and compared to fixed Morgan fingerprints.
Binary classification was performed on drug-target binding affinity using six target proteins and molecules from the ZINC15 database.
The model was trained and tuned for predictive capability.

Main Results:

The new neural fingerprint-based screening model demonstrated superior efficiency in identifying molecules with favorable drug-target binding.
The model effectively screened molecules with less favorable binding and retained those with favorable binding.
Despite using a smaller dataset, the model mapped chemical space comparable to contemporary screening algorithms.
The training and tuning phases of the model were significantly faster than existing methods.

Conclusions:

The developed neural fingerprint-based model represents a significant advancement in machine learning-embedded computational drug discovery.
This approach offers a faster and more efficient method for molecular screening and hit identification.
The model's ability to accurately predict drug-target binding affinity holds great potential for accelerating therapeutic development.