AIMC Topic: Carcinogenicity Tests

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AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity.

Mutation research. Genetic toxicology and environmental mutagenesis Feb 26, 2025
This study aimed to develop an in silico model for predicting human carcinogenicity using advanced deep learning techniques, specifically Graph Neural Networks (GNN), through a multitask learning (MTL) approach. The MTL framework leveraged auxiliary ...
Neural Networks, Computer Computer Simulation Humans Carcinogens Deep Learning Animals Carcinogenicity Tests
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Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.

Journal of environmental science and health. Part C, Toxicology and carcinogenesis Sep 3, 2024
The escalating apprehension surrounding the carcinogenic potential of chemicals emphasizes the imperative need for efficient methods of assessing carcinogenicity. Conventional experimental approaches such as in vitro and in vivo assays, albeit effect...
Humans Deep Learning Carcinogenicity Tests Carcinogens Machine Learning Animals Neural Networks, Computer
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Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Expert opinion on drug metabolism & toxicology May 27, 2024
INTRODUCTION: This review explores the transformative impact of machine learning (ML) on carcinogenicity prediction within drug development. It discusses the historical context and recent advancements, emphasizing the significance of ML methodologies...
Animals Deep Learning Machine Learning Carcinogenicity Tests Carcinogens Drug Development Artificial Intelligence Humans
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A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

Archives of toxicology May 18, 2024
The development of a rapid and accurate model for determining the genotoxicity and carcinogenicity of chemicals is crucial for effective cancer risk assessment. This study aims to develop a 1-day, single-dose model for identifying genotoxic hepatocar...
Carcinogenicity Tests Oligonucleotide Array Sequence Analysis Carcinogens Mutagens Animals Reproducibility of Results Support Vector Machine Rats Administration, Oral Liver Risk Assessment Gene Expression Profiling Male
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Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction.

Regulatory toxicology and pharmacology : RTP Jan 11, 2018
In vitro genotoxicity bioassays are cost-efficient methods of assessing potential carcinogens. However, many genotoxicity bioassays are inappropriate for detecting chemicals eliciting non-genotoxic mechanisms, such as tumour promotion, this necessita...
Carcinogenicity Tests Carcinogens Rats Biological Assay Models, Biological Computer Simulation Animals Machine Learning
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PySmash: Python package and individual executable program for representative substructure generation and application.

Briefings in bioinformatics Sep 2, 2021
BACKGROUND: Substructure screening is widely applied to evaluate the molecular potency and ADMET properties of compounds in drug discovery pipelines, and it can also be used to interpret QSAR models for the design of new compounds with desirable phys...
Carcinogenicity Tests Humans Software Drug Screening Assays, Antitumor Drug Discovery Carcinogens Machine Learning Computational Biology
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