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Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.

Yunyi Wu¹, Guanyu Wang²

¹Department of Biology, Guangdong Provincial Key Laboratory of Cell Microenviroment and Disease Research, Southern University of Science and Technology, Shenzhen 518055, China. wuyy3@mail.sustc.edu.cn.

International Journal of Molecular Sciences

|August 15, 2018

Summary

Machine learning enhances drug toxicity prediction, reducing costly preclinical and clinical trials. Combining chemical structures with human transcriptome data significantly improves prediction accuracy for public health.

Keywords:

chemical structure deep learning machine learning molecular fingerprint molecular fragment toxicity prediction transcriptome

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

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Published on: March 14, 2019

Area of Science:

Computational chemistry
Bioinformatics
Toxicology

Background:

Toxicity prediction is crucial for public health and reducing drug development costs.
Machine learning (ML) offers powerful tools for analyzing complex biological data.
Traditional drug evaluation methods are time-consuming and expensive.

Purpose of the Study:

To review machine learning methods applied to toxicity prediction.
To discuss advancements in input parameters for ML toxicity prediction models.
To highlight the impact of integrating human transcriptome data.

Main Methods:

Review of machine learning algorithms: deep learning, random forests, k-nearest neighbors, support vector machines.
Analysis of input data parameters for ML models.
Exploration of integrating chemical structure data with human transcriptome data.

Main Results:

Machine learning methods show excellent performance in toxicity prediction.
Combining chemical structure descriptions with human transcriptome data enhances prediction accuracy.
Various ML algorithms are applicable to toxicity prediction tasks.

Conclusions:

Machine learning, particularly with integrated data, offers a promising approach to improve toxicity prediction.
This can significantly reduce the need for extensive cellular, animal, and clinical drug evaluations.
Advancements in ML and data integration are vital for public health and efficient drug development.