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In Silico Methods for Predicting Drug Toxicity

Om In Silico Methods for Predicting Drug Toxicity

This fully updated book explores all-new and revised protocols involving the use of in silico models, particularly with regard to pharmaceuticals. Divided into five sections, the volume covers the modeling of pharmaceuticals in the body, toxicity data for modeling purposes, in silico models for multiple endpoints, a number of platforms for evaluating pharmaceuticals, as well as an exploration of challenges, both scientific and sociological. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice necessary for successful results. Authoritative and comprehensive, In Silico Methods for Predicting Drug Toxicity, Second Edition aims to guide the reader through the correct procedures needed to harness in silico models, a field which now touches a wide variety of research specialties.

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  • Språk:
  • Engelska
  • ISBN:
  • 9781071619629
  • Format:
  • Häftad
  • Sidor:
  • 696
  • Utgiven:
  • 1. mars 2022
  • Utgåva:
  • 22002
  • Mått:
  • 178x38x254 mm.
  • Vikt:
  • 1286 g.
Leveranstid: 2-4 veckor
Förväntad leverans: 27. november 2024

Beskrivning av In Silico Methods for Predicting Drug Toxicity

This fully updated book explores all-new and revised protocols involving the use of in silico models, particularly with regard to pharmaceuticals. Divided into five sections, the volume covers the modeling of pharmaceuticals in the body, toxicity data for modeling purposes, in silico models for multiple endpoints, a number of platforms for evaluating pharmaceuticals, as well as an exploration of challenges, both scientific and sociological. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice necessary for successful results.
Authoritative and comprehensive, In Silico Methods for Predicting Drug Toxicity, Second Edition aims to guide the reader through the correct procedures needed to harness in silico models, a field which now touches a wide variety of research specialties.

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