Search research articles

Related Concept Videos

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.

You might also read

Related Articles

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

Sort by

Same author

High performance of point-of-care rapid tests for advanced HIV disease diagnosis by lay providers in Malawi: Results from a prospective diagnostic accuracy study supporting decentralized advanced HIV disease screening.

PloS one·2026

Same author

Developing a surveillance system for HIV pre-exposure prophylaxis (PrEP) use in pregnancy in Malawi.

BMC pregnancy and childbirth·2026

Same author

HIV low-level viraemia: considerations for prevention and treatment in an era of highly effective and durable antiretroviral therapy regimens.

Journal of the International AIDS Society·2026

Same author

"I just thought I was lucky to be protected from HIV:" Qualitative evaluation of barriers and facilitators of pre-exposure prophylaxis use for adolescent girls and young women at higher risk of HIV acquisition in Lilongwe, Malawi.

PloS one·2025

Same author

HIV Drug Resistance to Dolutegravir Among Adults Investigated for Antiretroviral Treatment Failure in Malawi: A 2023 National Cross-Sectional Survey.

Journal of acquired immune deficiency syndromes (1999)·2025

Same author

Characteristics of individuals who received post-exposure prophylaxis and HIV seroconversion in Malawi: an analysis of national routine HIV testing data.

Journal of the International AIDS Society·2025

Same journal

Unified heterogeneity-aware benchmark of drug synergy prediction: a cross-study analysis of traditional machine learning and graph deep learning models.

Journal of cheminformatics·2026

Same journal

Count your bits: fingerprint benchmarking to assess broad chemical space representation.

Journal of cheminformatics·2026

Same journal

Sampling out-of-distribution chemical spaces via Bayesian flow.

Journal of cheminformatics·2026

Same journal

Hold on tight: the kinetic profiling of opioid receptor ligands using the CORAL-MD.

Journal of cheminformatics·2026

Same journal

Transformer-accelerated discovery of inhibitors targeting the RpsA<sub>Δ438</sub> deletion in PZA-resistant tuberculosis.

Journal of cheminformatics·2026

Same journal

DICL: a manually curated database of ion channels and ligands as a useful platform for drug discovery targeting ion channels.

Journal of cheminformatics·2026

See all related 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

Search research articles

Related Experiment Video

Updated: Jun 3, 2026

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Published on: October 2, 2016

Interpreting linear support vector machine models with heat map molecule coloring.

Lars Rosenbaum¹, Georg Hinselmann, Andreas Jahn

¹University of Tübingen, Center for Bioinformatics (ZBIT), Sand 1, 72076 Tübingen, Germany. lars.rosenbaum@uni-tuebingen.de.

Journal of Cheminformatics

|March 29, 2011

Summary

This summary is machine-generated.

This study introduces a novel heat map molecule coloring technique to interpret linear support vector machine (SVM) models. This method visualizes atomic and bond importance, aiding drug discovery by highlighting key substructures for lead compound optimization.

More Related Videos

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Related Experiment Videos

Last Updated: Jun 3, 2026

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Rapid High-throughput Species Identification of Botanical Material Using Direct Analysis in Real Time High Resolution Mass Spectrometry

Published on: October 2, 2016

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Area of Science:

Computational Chemistry
Cheminformatics
Machine Learning in Drug Discovery

Background:

Virtual screening is crucial for early drug discovery, utilizing high-throughput screening data for machine learning models.
Interpretable machine learning models are essential for guiding compound optimization.
Linear support vector machines (SVMs) offer strong performance on large datasets.

Purpose of the Study:

To present a novel heat map molecule coloring technique for interpreting linear SVM models.
To visualize the importance of atoms and bonds within a molecule based on model weights.

Main Methods:

Developed a heat map coloring technique based on linear model weights.
Applied the visualization to interpret machine learning models for various datasets.

Main Results:

The method effectively visualizes structure-property and structure-activity relationships.
Heat map coloring aids in determining correct ligand orientation within binding pockets.
Identified key substructures crucial for inhibitor binding.

Conclusions:

Linear SVMs combined with heat map coloring can guide compound modification in drug discovery.
Identified substructures serve as starting points for lead compound optimization.
Heat map coloring complements structure-based modeling for better understanding of inhibitor binding modes.