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Titlebook: In Silico Methods for Predicting Drug Toxicity; Emilio Benfenati Book 2016 Springer Science+Business Media New York 2016 Pharmaceutical mo

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11#
發(fā)表于 2025-3-23 13:39:52 | 只看該作者
In Silico Models for Hepatotoxicitycal system and our ability to emulate that in a predictive capacity. We give an overview of the published modeling approaches in this area to date and discuss their design, strengths, and weaknesses. It is interesting to note the shift during the period of this review in the direction of evidenced-b
12#
發(fā)表于 2025-3-23 14:49:36 | 只看該作者
In Silico Models for Ecotoxicity of Pharmaceuticalsls into the environment are industries, hospitals, or direct disposal of unwanted or expired drugs made by the patient. The most important and distinct features of pharmaceuticals are that they are deliberately designed to have an explicit mode of action and designed to exert an effect on humans and
13#
發(fā)表于 2025-3-23 20:40:56 | 只看該作者
14#
發(fā)表于 2025-3-23 22:48:05 | 只看該作者
15#
發(fā)表于 2025-3-24 04:02:29 | 只看該作者
A Systems Biology Approach for Identifying Hepatotoxicant Groups Based on Similarity in Mechanisms omechanisms of action is a crucial strategy. In this chapter, we describe a systems biology approach that incorporates analyses of hepatotoxicant data for 33 compounds from three different sources: a chemical structure similarity analysis based on the 3D Tanimoto coefficient, a chemical structure-bas
16#
發(fā)表于 2025-3-24 06:33:07 | 只看該作者
In Silico Study of In Vitro GPCR Assays by QSAR Modelingays. One goal is to prioritize chemicals for more detailed analyses based on activity in assays that target molecular initiating events (MIEs) of adverse outcome pathways (AOPs). However, the chemical space of interest for environmental exposure is much wider than ToxCast’s chemical library. In sili
17#
發(fā)表于 2025-3-24 13:16:17 | 只看該作者
18#
發(fā)表于 2025-3-24 17:00:58 | 只看該作者
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發(fā)表于 2025-3-24 20:30:23 | 只看該作者
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發(fā)表于 2025-3-25 00:19:34 | 只看該作者
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