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Titlebook: Drug Development Supported by Informatics; Hiroko Satoh,Kimito Funatsu,Hiroshi Yamamoto Book 2024 The Editor(s) (if applicable) and The Au

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,Toxicity Prediction System for Chemical Substances Based on Toxicity Expression Mechanisms—AI-SHIPSated dose toxicity tests. However, due to the high cost and time required for animal experiments and from the perspective of animal welfare, alternative methods such as (quantitative) structure–activity relationships ((Q)SAR), which predict toxicity from the structure of chemical substances, are bei
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Potential of High-Spatiotemporal Resolution Live Cell Imaging for Drug Discovery and Development,hniques enable researchers to observe and analyze cellular behavior, monitor the effects of drugs on cellular functions, and gain a deeper understanding of the mechanisms underlying diseases and therapeutic interventions. Data science, including artificial intelligence and machine learning, plays a
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Design of Biomaterials Using Informatics,ntion. However, when compared with areas such as battery technology, catalysis, and magnetic materials, the application of materials informatics within the biomaterials field is less prevalent. In this paper, we first provide an overview of the current status of this field. We introduce representati
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Monitoring and Controlling in Continuous Manufacturing Process,API) content, API mixing uniformity, moisture content, particle coating content, coating performance, etc. in the pharmaceutical product is monitored in real-time for each process such as grinding, mixing, granulation, drying, granulation, tableting, and coating. Appropriate operations are required
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Formulation Using Hansen Solubility Parameters,s: dispersion term, polarization term, and hydrogen bonding term. The three terms are regarded as a three-dimensional vector. Solubility is determined according to the similarity of the vectors. HSP can be used to determine how much of a compound (drug) is soluble in a given solvent. Here, I will ex
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Tony Bridgeman,P. C. Chatwin,C. Plumptonhe similarity between graphs and can be combined with any kernel methods, and ., which are neural networks designed for machine learning such as classification and regression on graphs. We also review ., which enables us to efficiently search desirable drugs in enormous search space.
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https://doi.org/10.1007/978-981-97-4828-0Chemoinformatics; Bioinformatics; Materials informatics; Clinical informatics; Artificial Intelligence
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