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多案例计算机毒理学平台。

MultiCASE Platform for In Silico Toxicology.

机构信息

MultiCASE Inc, Beachwood, OH, USA.

出版信息

Methods Mol Biol. 2022;2425:497-518. doi: 10.1007/978-1-0716-1960-5_19.

DOI:10.1007/978-1-0716-1960-5_19
PMID:35188644
Abstract

Predictive and computational toxicology, a highly scientific and research-based field, is rapidly progressing with wider acceptance by regulatory agencies around the world. Almost every aspect of the field has seen fundamental changes during the last decade due to the availability of more data, usage, and acceptance of a variety of predictive tools and an increase in the overall awareness. Also, the influence from the recent explosive developments in the field of artificial intelligence has been significant. However, the need for sophisticated, easy to use and well-maintained software platforms for in silico toxicological assessments remains very high. The MultiCASE suite of software is one such platform that consists of an integrated collection of software programs, tools, and databases. While providing easy-to-use and highly useful tools that are relevant at present, it has always remained at the forefront of research and development by inventing new technologies and discovering new insights in the area of QSAR, artificial intelligence, and machine learning. This chapter gives the background, an overview of the software and databases involved, and a brief description of the usage methodology with the aid of examples.

摘要

预测和计算毒理学是一个高度科学和基于研究的领域,随着全球监管机构的更广泛接受,它正在迅速发展。由于更多数据的可用性、各种预测工具的使用和接受度的提高以及整体意识的提高,该领域的几乎所有方面在过去十年中都发生了根本性的变化。此外,人工智能领域的近期爆炸式发展也产生了重大影响。然而,对于用于计算机毒性评估的复杂、易于使用和维护良好的软件平台的需求仍然非常高。MultiCASE 软件套件就是这样一个平台,它由一整套集成的软件程序、工具和数据库组成。虽然提供了易于使用且非常有用的工具,但它始终通过发明新技术和在 QSAR、人工智能和机器学习领域发现新的见解,处于研究和开发的前沿。本章介绍了背景、所涉及的软件和数据库概述,以及借助示例简要描述了使用方法。

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1
MultiCASE Platform for In Silico Toxicology.多案例计算机毒理学平台。
Methods Mol Biol. 2022;2425:497-518. doi: 10.1007/978-1-0716-1960-5_19.
2
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Mol Nutr Food Res. 2010 Feb;54(2):186-94. doi: 10.1002/mnfr.200900259.
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Prediction of rodent carcinogenic potential of naturally occurring chemicals in the human diet using high-throughput QSAR predictive modeling.使用高通量定量构效关系预测模型预测人类饮食中天然存在的化学物质的啮齿动物致癌潜力。
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Development and application of consensus models for advancing high-throughput toxicological predictions.用于推进高通量毒理学预测的共识模型的开发与应用。
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本文引用的文献

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Management of pharmaceutical ICH M7 (Q)SAR predictions - The impact of model updates.药品人用药品注册技术协调会国际协调会议(ICH)M7定量构效关系(QSAR)预测的管理——模型更新的影响
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Transitioning to composite bacterial mutagenicity models in ICH M7 (Q)SAR analyses.向 ICH M7(Q)SAR 分析中的组合细菌致突变性模型过渡。
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用于预测埃姆斯致突变性的定量构效关系(QSAR)工具的改进:埃姆斯/QSAR国际挑战赛项目的成果
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Searching for an enhanced predictive tool for mutagenicity.寻找一种用于致突变性的增强预测工具。
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