Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions-Reference-Cited by-同舟云学术

Molecular Fingerprint-Based Artificial Neural Networks QSAR for Ligand Biological Activity Predictions

Published:2012-08-31 Issue:10 Volume:9 Page:2912-2923
ISSN:1543-8384
Container-title:Molecular Pharmaceutics
language:en
Short-container-title:Mol. Pharmaceutics

Author:

Myint Kyaw-Zeyar¹,Wang Lirong¹,Tong Qin¹,Xie Xiang-Qun¹

Affiliation:

1. Department of Computational Biology, Joint Carnegie Mellon University-University of Pittsburgh Ph.D. Program, School of Medicine, ‡Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, §Drug Discovery Institute, ∥Pittsburgh Chemical Methods and Library Development (CMLD) Center, and ⊥Department of Structural Biology, University of Pittsburgh, Pittsburgh, Pennsylvania 15260, United States

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Pharmaceutical Science,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/mp300237z

Reference70 articles.

1. Recent Advances in Fragment-Based QSAR and Multi-Dimensional QSAR Methods

2. QUANTITATIVE STRUCTURE–ACTIVITY RELATIONSHIP METHODS: PERSPECTIVES ON DRUG DISCOVERY AND TOXICOLOGY

3. Fragment-based QSAR: perspectives in drug design

4. A Mathematical Contribution to Structure-Activity Studies

5. p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

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