Literature DB >> 21554079

Integrating structure-based and ligand-based approaches for computational drug design.

Gregory L Wilson1, Markus A Lill.   

Abstract

Methods utilized in computer-aided drug design can be classified into two major categories: structure based and ligand based, using information on the structure of the protein or on the biological and physicochemical properties of bound ligands, respectively. In recent years there has been a trend towards integrating these two methods in order to enhance the reliability and efficiency of computer-aided drug-design approaches by combining information from both the ligand and the protein. This trend resulted in a variety of methods that include: pseudoreceptor methods, pharmacophore methods, fingerprint methods and approaches integrating docking with similarity-based methods. In this article, we will describe the concepts behind each method and selected applications.

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Year:  2011        PMID: 21554079     DOI: 10.4155/fmc.11.18

Source DB:  PubMed          Journal:  Future Med Chem        ISSN: 1756-8919            Impact factor:   3.808


  27 in total

Review 1.  Global phenotypic screening for antimalarials.

Authors:  W Armand Guiguemde; Anang A Shelat; Jose F Garcia-Bustos; Thierry T Diagana; Francisco-Javier Gamo; R Kiplin Guy
Journal:  Chem Biol       Date:  2012-01-27

Review 2.  Computational methods in drug discovery.

Authors:  Gregory Sliwoski; Sandeepkumar Kothiwale; Jens Meiler; Edward W Lowe
Journal:  Pharmacol Rev       Date:  2013-12-31       Impact factor: 25.468

3.  Identification of New Human Malaria Parasite Plasmodium falciparum Dihydroorotate Dehydrogenase Inhibitors by Pharmacophore and Structure-Based Virtual Screening.

Authors:  Elumalai Pavadai; Farah El Mazouni; Sergio Wittlin; Carmen de Kock; Margaret A Phillips; Kelly Chibale
Journal:  J Chem Inf Model       Date:  2016-03-08       Impact factor: 4.956

4.  Hybrid receptor structure/ligand-based docking and activity prediction in ICM: development and evaluation in D3R Grand Challenge 3.

Authors:  Polo C-H Lam; Ruben Abagyan; Maxim Totrov
Journal:  J Comput Aided Mol Des       Date:  2018-08-09       Impact factor: 3.686

Review 5.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

6.  Design, synthesis, and biological evaluation of indenoisoquinoline rexinoids with chemopreventive potential.

Authors:  Martin Conda-Sheridan; Eun-Jung Park; Daniel E Beck; P V Narasimha Reddy; Trung X Nguyen; Bingjie Hu; Lian Chen; Jerry J White; Richard B van Breemen; John M Pezzuto; Mark Cushman
Journal:  J Med Chem       Date:  2013-03-08       Impact factor: 7.446

Review 7.  Systematic review on role of structure based drug design (SBDD) in the identification of anti-viral leads against SARS-Cov-2.

Authors:  Nilesh Gajanan Bajad; Swetha Rayala; Gopichand Gutti; Anjali Sharma; Meenakshi Singh; Ashok Kumar; Sushil Kumar Singh
Journal:  Curr Res Pharmacol Drug Discov       Date:  2021-05-14

Review 8.  Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies.

Authors:  Katarina Nikolic; Lazaros Mavridis; Teodora Djikic; Jelica Vucicevic; Danica Agbaba; Kemal Yelekci; John B O Mitchell
Journal:  Front Neurosci       Date:  2016-06-10       Impact factor: 4.677

9.  A perspective on multi-target drug discovery and design for complex diseases.

Authors:  Rona R Ramsay; Marija R Popovic-Nikolic; Katarina Nikolic; Elisa Uliassi; Maria Laura Bolognesi
Journal:  Clin Transl Med       Date:  2018-01-17

10.  Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis.

Authors:  Aliakbar Pourhatami; Mohammad Kaviyani-Charati; Bahareh Kargar; Hamed Baziyad; Maryam Kargar; Carlos Olmeda-Gómez
Journal:  Scientometrics       Date:  2021-06-15       Impact factor: 3.238

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