Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening-Reference-Cited by-同舟云学术

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Streamlining Computational Fragment-Based Drug Discovery through Evolutionary Optimization Informed by Ligand-Based Virtual Prescreening

Published:2024-05-02 Issue:9 Volume:64 Page:3826-3840
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Chandraghatgi Rohan¹,Ji Hai-Feng²^ORCID,Rosen Gail L.³,Sokhansanj Bahrad A.³^ORCID

Affiliation:

1. Department of Biology, Drexel University, Philadelphia, Pennsylvania 19104, United States

2. Department of Chemistry, Drexel University, Philadelphia, Pennsylvania 19104, United States

3. Department of Electrical & Computer Engineering, Drexel University, Philadelphia, Pennsylvania 19104, United States

Funder

Division of Biological Infrastructure

Office of Advanced Cyberinfrastructure

Division of Information and Intelligent Systems

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.4c00234

Reference86 articles.

1. Computational drug discovery

2. Computational Methods in Drug Discovery

3. Comparing AutoDock and Vina in Ligand/Decoy Discrimination for Virtual Screening

4. In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery

5. Artificial intelligence to deep learning: machine intelligence approach for drug discovery

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