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,...

Bias

Bias

Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...

Physical and Chemical Properties of Matter

Physical and Chemical Properties of Matter

The characteristics that enable us to distinguish one substance from another are called properties.

Predicting Products: SN1 vs. SN2

Predicting Products: S_N1 vs. S_N2

Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
With increased substitution on the alkyl halide,...

Experimental Determination of Chemical Formula

Experimental Determination of Chemical Formula

The elemental makeup of a compound defines its chemical identity, and chemical formulas are the most concise way of representing this elemental makeup. When a compound’s formula is unknown, measuring the mass of its constituent elements is often the first step in determining the formula experimentally.

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

You might also read

Related Articles

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

Sort by

Same author

Meta graphical lasso: uncovering hidden interactions among latent mechanisms.

Scientific reports·2024

Same author

Predicting anesthetic infusion events using machine learning.

Scientific reports·2021

Same author

Dual graph convolutional neural network for predicting chemical networks.

BMC bioinformatics·2020

Same author

DDBJ Data Analysis Challenge: a machine learning competition to predict Arabidopsis chromatin feature annotations from DNA sequences.

Genes & genetic systems·2020

Same author

Wisdom of crowds for synthetic accessibility evaluation.

Journal of molecular graphics & modelling·2018

Same author

Predicting disease progression from short biomarker series using expert advice algorithm.

Scientific reports·2015

Search research articles

Related Experiment Video

Updated: Sep 23, 2025

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

Published on: August 2, 2018

Chemical property prediction under experimental biases.

Yang Liu¹, Hisashi Kashima²

¹Department of Intelligence Science and Technology, Kyoto University, Kyoto, 606-8501, Japan. liuyang@ml.ist.i.kyoto-u.ac.jp.

Scientific Reports

|May 17, 2022

Summary

This study mitigates bias in chemical property prediction datasets using causal inference and graph neural networks. These methods improve model performance on real-world data, overcoming limitations of standard machine learning approaches.

More Related Videos

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Related Experiment Videos

Last Updated: Sep 23, 2025

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

Experimental Study of the Relationship Between Particle Size and Methane Sorption Capacity in Shale

Published on: August 2, 2018

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Published on: January 31, 2014

Area of Science:

Computational chemistry
Machine learning
Data science

Background:

Predicting chemical properties is vital for drug and material discovery.
Machine learning models trained on literature data often exhibit bias, leading to poor performance.
Dataset bias arises from experimental design and publication practices.

Purpose of the Study:

To develop methods for mitigating bias in experimental chemical datasets.
To improve the reliability and generalizability of predictive models for chemical properties.
To address the over-fitting issues caused by biased data distributions.

Main Methods:

Utilized graph neural networks to represent molecular structures.
Applied two causal inference techniques: inverse propensity scoring and counter-factual regression.
Evaluated methods across four distinct bias scenarios.

Main Results:

Both inverse propensity scoring and counter-factual regression demonstrated significant improvements in prediction accuracy.
The proposed methods effectively mitigated the negative impacts of dataset bias.
Enhanced model robustness and performance in diverse bias conditions.

Conclusions:

Causal inference techniques combined with graph neural networks offer a powerful solution for bias mitigation in chemical data.
These approaches enhance the utility of machine learning for reliable chemical property prediction.
The findings pave the way for more accurate and dependable computational discovery processes.