AIMC Topic: Carcinogenicity Tests

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Toward Explainable Carcinogenicity Prediction: An Integrated Cheminformatics Approach and Consensus Framework for Possibly Carcinogenic Chemicals.

Journal of chemical information and modeling
A carcinogenicity assessment of possibly carcinogenic chemicals (International Agency for Research on Cancer: IARC class 2B) was conducted using a consensus framework constructed from three complementary machine learning models: BiLSTM with MACCS fin...

A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression.

Journal of hazardous materials
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...

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
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 ...

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
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...

Recent progress in machine learning approaches for predicting carcinogenicity in drug development.

Expert opinion on drug metabolism & toxicology
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...

A novel support vector machine-based 1-day, single-dose prediction model of genotoxic hepatocarcinogenicity in rats.

Archives of toxicology
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...

Combining machine learning models of in vitro and in vivo bioassays improves rat carcinogenicity prediction.

Regulatory toxicology and pharmacology : RTP
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...

PySmash: Python package and individual executable program for representative substructure generation and application.

Briefings in bioinformatics
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...