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MultiCASE Platform for In Silico Toxicology

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In Silico Methods for Predicting Drug Toxicity

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2425))

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.

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Authors and Affiliations

  1. MultiCASE Inc, Beachwood, OH, USA

    Suman K. Chakravarti & Roustem D. Saiakhov

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  1. Suman K. Chakravarti
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  2. Roustem D. Saiakhov
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Correspondence to Suman K. Chakravarti .

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Editors and Affiliations

  1. Department of Environmental Health Sciences, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy

    Emilio Benfenati

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Chakravarti, S.K., Saiakhov, R.D. (2022). MultiCASE Platform for In Silico Toxicology. In: Benfenati, E. (eds) In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology, vol 2425. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1960-5_19

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  • DOI: https://doi.org/10.1007/978-1-0716-1960-5_19

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