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Machine Learning Methods in Computational Toxicology
2018Various methods of machine learning, supervised and unsupervised, linear and nonlinear, classification and regression, in combination with various types of molecular descriptors, both "handcrafted" and "data-driven," are considered in the context of their use in computational toxicology.
Igor I. Baskin, Igor I. Baskin
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Mind the Gap! A Journey towards Computational Toxicology
Molecular Informatics, 2016AbstractComputational methods have advanced toxicology towards the development of target‐specific models based on a clear cause‐effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments.
Mangiatordi, G. F. +12 more
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Public Databases Supporting Computational Toxicology
Journal of Toxicology and Environmental Health, Part B, 2010A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, researchers need quantitative in vitro and ideally in vivo data for large ...
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Computational Toxicology and Risk Assessment
2017Presented at SPH-V 546 Guest ...
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The evolving role of investigative toxicology in the pharmaceutical industry
Nature Reviews Drug Discovery, 2023Francois Pognan, , Richard J Weaver
exaly
Computers in toxicological education: An overview
Medical Informatics, 1985F. Dewhurst +3 more
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Computational toxicology: From cheminformatics to nanoinformatics
Food and Chemical Toxicology, 2018Antreas Afantitis, Georgia Melagraki
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Source, migration and toxicology of microplastics in soil
Environment International, 2020Ming-Hung Wong
exaly

