<scp>PharmRF</scp> : A machine‐learning scoring function to identify the best protein‐ligand complexes for structure‐based pharmacophore screening with high enrichments-Reference-Cited by-同舟云学术

PharmRF : A machine‐learning scoring function to identify the best protein‐ligand complexes for structure‐based pharmacophore screening with high enrichments

Published:2022-03-18 Issue:12 Volume:43 Page:847-863
ISSN:0192-8651
Container-title:Journal of Computational Chemistry
language:en
Short-container-title:J Comput Chem

Author:

Kumar Sivakumar Prasanth¹²³^ORCID,Dixit Nandan Y.³^ORCID,Patel Chirag N.³^ORCID,Rawal Rakesh M.¹²^ORCID,Pandya Himanshu A.¹²³^ORCID

Affiliation:

1. Institute of Defence Studies and Research Gujarat University Ahmedabad India

2. Department of Life Sciences University School of Sciences, Gujarat University Ahmedabad India

3. Department of Botany, Bioinformatics, and Climate Change Impacts Management University School of Sciences, Gujarat University Ahmedabad India

Publisher

Wiley

Subject

Computational Mathematics,General Chemistry

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.26840

Reference56 articles.

1. Pharmacophore definition and 3D searches

2. From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

3. On the use of LUDI to search the Fine Chemicals Directory for ligands of proteins of known three-dimensional structure

4. All in One: Cavity Detection, Druggability Estimate, Cavity-Based Pharmacophore Perception, and Virtual Screening

5. GBPM: GRID-based pharmacophore model: concept and application studies to protein-protein recognition

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