Titlebook: Machine Learning and Deep Learning in Computational Toxicology; Huixiao Hong Book 2023 This is a U.S. government work and not under copyri

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Multitask Learning for Quantitative Structure–Activity Relationships: A Tutorialonships and computational toxicology, multitask learning is gaining more and more interest, owed to its potential to improve the predictive performance of underrepresented tasks and to predict the multi-property profile of molecules. In this chapter, after introducing the multitask problem formulati

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ED Profiler: Machine Learning Tool for Screening Potential Endocrine-Disrupting Chemicalserious effects of EDCs on the endocrine system of living organisms, we should identify and screen potential EDCs from the current myriad of commercially used chemicals. Computational models and software have been increasingly recognized as a valuable, effective, and powerful high-throughput virtual

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Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dyere we proposed a new concept of dynamic protein–ligand interaction descriptors (dyPLIDs) derived from molecular dynamics (MD) simulations and developed a novel quantitative target-specific toxicity prediction modeling (QTTPM) approach by integrating dyPLIDs with machine learning. In this proof-of-c

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Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predictingxicity of chemicals. Mold2 is a software tool developed in C++ for fast calculating molecular descriptors from two-dimensional structures. Mold2 descriptors contain rich information and can be used to build high-performance models in computational toxicology. Multiple studies have compared Mold2 des

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Applicability Domain Characterization for Machine Learning QSAR Modelsedented success in developing “high-performance” QSAR models with various machine learning algorithms. Nonetheless, QSAR models are intrinsically data-driven models, in which patterns or rules are learned from training samples and thus can only be valid within limited applicability domains (AD). In

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Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk to complex survey data remain virtually unknown, which discourages analysis on survey data more complex than that of prevalence. The National Health and Nutrition Examination Survey (NHANES) is a long-running, complex survey designed to assess the health and well-being of adults and children in the

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