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

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

You might also read

Related Articles

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

Sort by

Same author

Design and synthesis of a chemically diverse, lead-like DNA-encoded library from sequential amide coupling.

RSC medicinal chemistry·2025

Same author

Computational Hit Finding: An Industry Perspective.

Journal of medicinal chemistry·2025

Same author

Rapid traversal of vast chemical space using machine learning-guided docking screens.

Nature computational science·2025

Same author

In Silico Identification of Potential Thyroid Hormone System Disruptors among Chemicals in Human Serum and Chemicals with a High Exposure Index.

Environmental science & technology·2022

Same author

Design of a Potent, Selective, and Brain-Penetrant Inhibitor of Wnt-Deactivating Enzyme Notum by Optimization of a Crystallographic Fragment Hit.

Journal of medicinal chemistry·2022

Same author

Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening.

Journal of chemical information and modeling·2022

Search research articles

Related Experiment Video

Updated: Nov 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Deep Learning-Based Conformal Prediction of Toxicity.

Jin Zhang¹, Ulf Norinder^2,3,4, Fredrik Svensson⁵

¹Department of Drug Metabolism and Pharmacokinetics, Janssen Pharmaceutica NV, B-2340 Beerse, Belgium.

Journal of Chemical Information and Modeling

|May 27, 2021

Summary

This summary is machine-generated.

This study combines deep learning toxicity prediction models with conformal prediction to quantify uncertainty. The results show this approach provides reliable toxicity predictions with statistical guarantees, improving minority class accuracy.

More Related Videos

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Related Experiment Videos

Last Updated: Nov 4, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Demonstration of the Sequence Alignment to Predict Across Species Susceptibility Tool for Rapid Assessment of Protein Conservation

Published on: February 10, 2023

Area of Science:

Toxicology
Computational Chemistry
Machine Learning

Background:

Predictive toxicity modeling is crucial for risk assessment and regulatory decisions.
Quantifying uncertainty in toxicity predictions is essential for reliable model utility.
Deep learning models show promise for toxicity prediction, but uncertainty quantification remains a challenge.

Purpose of the Study:

To investigate the combination of deep learning models with conformal prediction for toxicity prediction.
To generate highly predictive toxicity models with well-defined uncertainty estimates.
To evaluate the performance of conformal predictors against underlying machine learning models.

Main Methods:

Utilized deep feedforward neural networks and graph neural networks.
Applied the conformal prediction framework to deep learning models.
Evaluated performance on data from the Tox21 challenge.

Main Results:

Achieved highly predictive models with efficient conformal predictors, even at high confidence levels.
Conformal predictors provided statistical guarantees on model performance.
Demonstrated improved predictions for the minority class compared to underlying models.

Conclusions:

Conformal prediction is a valuable method for delivering toxicity predictions with confidence.
This approach enhances the reliability and utility of deep learning-based toxicity models.
The findings support the use of conformal prediction in regulatory toxicology and risk assessment.