Exploiting Domain Knowledge for Improved Quantitative High-Throughput Screening Curve Fitting-Reference-Cited by-同舟云学术

Exploiting Domain Knowledge for Improved Quantitative High-Throughput Screening Curve Fitting

Published:2011-11-01 Issue:11 Volume:51 Page:2808-2820
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Bergeron Charles¹,Moore Gregory¹,Krein Michael¹,Breneman Curt M.¹,Bennett Kristin P.¹

Affiliation:

1. Department of Mathematical Sciences and ‡Department of Chemistry and Chemical Biology, Rensselaer Polytechnic Institute, 110 8th Street, Troy, New York 12180, United States

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci200210d

Reference22 articles.

1. Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries

2. Is It Possible To Estimate the Parameters of the Sigmoid Emax Model with Truncated Data Typical of Clinical Studies?

3. Computational chemistry-driven decision making in lead generation

4. Statistical practice in high-throughput screening data analysis

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