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Cited by 477

The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.

Defining Desirable Central Nervous System Drug Space through the Alignment of Molecular Properties, in Vitro ADME, and Safety Attributes

Open access
Wager, Travis T., Chandrasekaran, Ramalakshmi Y., Hou, Xinjun, Troutman, Matthew D., Verhoest, Patrick R., Villalobos, Anabella, Will, Yvonne·2010·ACS Chemical Neuroscience

Abstract

As part of our effort to increase survival of drug candidates and to move our medicinal chemistry design to higher probability space for success in the Neuroscience therapeutic area, we embarked on a detailed study of the property space for a collection of central nervous system (CNS) molecules. We carried out a thorough analysis of properties for 119 marketed CNS drugs and a set of 108 Pfizer CNS candidates. In particular, we focused on understanding the relationships between physicochemical properties, in vitro ADME (absorption, distribution, metabolism, and elimination) attributes, primary pharmacology binding efficiencies, and in vitro safety data for these two sets of compounds. This scholarship provides guidance for the design of CNS molecules in a property space with increased probability of success and may lead to the identification of druglike candidates with favorable safety profiles that can successfully test hypotheses in the clinic.

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Crossref · 10.1021/cn100007x

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Metadata

Authors
Wager, T. T., Chandrasekaran, R. Y., Hou, X., Troutman, M. D., Verhoest, P. R., Villalobos, A., & Will, Y.
Venue
ACS Chemical Neuroscience
Published
2010-03-25
Volume
1
Issue
6
Pages
420-434
ISSN
1948-7193 (ISSN-L)
OpenAlex
W2030215451
Cited by
477
References
28
FWCI
31.73
Type
journal article
Language
en
Open access
green

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.