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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Huixiao Hong,Jie Liu,Weigong Ge,Sugunadevi Sakkiah,Wenjing Guo,Gokhan Yavas,Chaoyang Zhang,Ping Gong

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Assessment of the Xenobiotics Toxicity Taking into Account Their Metabolismutical drug R&D, experimental metabolite structure is often not yet available. To increase the safety profile of novel pharmaceutical agents, a computer-aided assessment of toxicity should be performed based on the structural formulae of both parent compounds and their metabolites. In this chapter,

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Emerging Machine Learning Techniques in Predicting Adverse Drug Reactionsne learning models have been developed to characterize, predict and prevent ADRs. However, it is a challenge for the models to effectively extract features and make predictions based on multiple sources of heterogeneous and complex data. In this chapter, different types of drug-related features and

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Drug Effect Deep Learner Based on Graphical Convolutional Networkmulti-source information such as the gene interaction networks of human cells, the structure of drug molecules, and the gene expressions induced by drugs. In the model, genes, cells, even drug effects are all represent by 1024-dimensional vectors. Based on the vector representations, we develop a de

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AOP-Based Machine Learning for Toxicity Prediction imperative to evaluate the toxic effects of these compounds. However, the cost and time required to obtain toxicity data through traditional in vivo experimental methods are high. The promise of obtaining toxicity data through virtual screening, especially machine learning (ML) algorithms, has attr

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