Literature DB >> 29920436

Improving small molecule virtual screening strategies for the next generation of therapeutics.

Bentley M Wingert1, Carlos J Camacho2.   

Abstract

The new generation of post-genomic targets, such as protein-protein interactions (PPIs), often require new chemotypes not well represented in current compound libraries. This is one reason for why traditional high throughput screening (HTS) approaches are not more successful in delivering medicinal chemistry starting points for PPIs. In silico screening methods of an expanded chemical space are then potential alternatives for developing novel chemical probes to modulate PPIs. In this review, we report on the state-of-the-art pipelines for virtual screening, emphasizing prospectively validated methods capable of addressing the challenge of drugging difficult targets in the human interactome. Collectively, we show that optimal strategies for structure based virtual screening vary depending on receptor structure and degree of flexibility. Published by Elsevier Ltd.

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Year:  2018        PMID: 29920436      PMCID: PMC6089075          DOI: 10.1016/j.cbpa.2018.06.006

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  48 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Development and testing of a general amber force field.

Authors:  Junmei Wang; Romain M Wolf; James W Caldwell; Peter A Kollman; David A Case
Journal:  J Comput Chem       Date:  2004-07-15       Impact factor: 3.376

Review 3.  Critical review of the role of HTS in drug discovery.

Authors:  Ricardo Macarron
Journal:  Drug Discov Today       Date:  2006-04       Impact factor: 7.851

4.  Extra precision glide: docking and scoring incorporating a model of hydrophobic enclosure for protein-ligand complexes.

Authors:  Richard A Friesner; Robert B Murphy; Matthew P Repasky; Leah L Frye; Jeremy R Greenwood; Thomas A Halgren; Paul C Sanschagrin; Daniel T Mainz
Journal:  J Med Chem       Date:  2006-10-19       Impact factor: 7.446

5.  A D3R prospective evaluation of machine learning for protein-ligand scoring.

Authors:  Jocelyn Sunseri; Matthew Ragoza; Jasmine Collins; David Ryan Koes
Journal:  J Comput Aided Mol Des       Date:  2016-09-03       Impact factor: 3.686

6.  Ensemble modeling of substrate binding to cytochromes P450: analysis of catalytic differences between CYP1A orthologs.

Authors:  Jahnavi C Prasad; Jared V Goldstone; Carlos J Camacho; Sandor Vajda; John J Stegeman
Journal:  Biochemistry       Date:  2007-02-15       Impact factor: 3.162

7.  D3R grand challenge 2015: Evaluation of protein-ligand pose and affinity predictions.

Authors:  Symon Gathiaka; Shuai Liu; Michael Chiu; Huanwang Yang; Jeanne A Stuckey; You Na Kang; Jim Delproposto; Ginger Kubish; James B Dunbar; Heather A Carlson; Stephen K Burley; W Patrick Walters; Rommie E Amaro; Victoria A Feher; Michael K Gilson
Journal:  J Comput Aided Mol Des       Date:  2016-09-30       Impact factor: 3.686

8.  AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility.

Authors:  Garrett M Morris; Ruth Huey; William Lindstrom; Michel F Sanner; Richard K Belew; David S Goodsell; Arthur J Olson
Journal:  J Comput Chem       Date:  2009-12       Impact factor: 3.376

9.  CDOCKER and λ-dynamics for prospective prediction in D₃R Grand Challenge 2.

Authors:  Xinqiang Ding; Ryan L Hayes; Jonah Z Vilseck; Murchtricia K Charles; Charles L Brooks
Journal:  J Comput Aided Mol Des       Date:  2017-09-07       Impact factor: 3.686

Review 10.  Exploring the role of receptor flexibility in structure-based drug discovery.

Authors:  Ferran Feixas; Steffen Lindert; William Sinko; J Andrew McCammon
Journal:  Biophys Chem       Date:  2013-11-09       Impact factor: 2.352

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  8 in total

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2.  Fine tuning for success in structure-based virtual screening.

Authors:  Emilie Pihan; Martin Kotev; Obdulia Rabal; Claudia Beato; Constantino Diaz Gonzalez
Journal:  J Comput Aided Mol Des       Date:  2021-11-20       Impact factor: 3.686

Review 3.  Anticancer drug discovery by targeting cullin neddylation.

Authors:  Qing Yu; Yihan Jiang; Yi Sun
Journal:  Acta Pharm Sin B       Date:  2019-09-20       Impact factor: 11.413

Review 4.  Medicinal chemistry strategies towards the development of effective SARS-CoV-2 inhibitors.

Authors:  Shenghua Gao; Tianguang Huang; Letian Song; Shujing Xu; Yusen Cheng; Srinivasulu Cherukupalli; Dongwei Kang; Tong Zhao; Lin Sun; Jian Zhang; Peng Zhan; Xinyong Liu
Journal:  Acta Pharm Sin B       Date:  2021-08-31       Impact factor: 11.413

5.  A specific inhibitor of ALDH1A3 regulates retinoic acid biosynthesis in glioma stem cells.

Authors:  Jianfeng Li; Silvia Garavaglia; Zhaofeng Ye; Andrea Moretti; Olga V Belyaeva; Alison Beiser; Md Ibrahim; Anna Wilk; Steve McClellan; Alla V Klyuyeva; Kelli R Goggans; Natalia Y Kedishvili; E Alan Salter; Andrzej Wierzbicki; Marie E Migaud; Steven J Mullett; Nathan A Yates; Carlos J Camacho; Menico Rizzi; Robert W Sobol
Journal:  Commun Biol       Date:  2021-12-21

6.  A feature transferring workflow between data-poor compounds in various tasks.

Authors:  Xiaofei Sun; Jingyuan Zhu; Bin Chen; Hengzhi You; Huiqing Xu
Journal:  PLoS One       Date:  2022-03-30       Impact factor: 3.240

7.  Identification of Novel Inhibitors Targeting SGK1 via Ensemble-Based Virtual Screening Method, Biological Evaluation and Molecular Dynamics Simulation.

Authors:  Hui Zhang; Chen Shen; Hong-Rui Zhang; Hua-Zhao Qi; Mei-Ling Hu; Qing-Qing Luo
Journal:  Int J Mol Sci       Date:  2022-08-03       Impact factor: 6.208

8.  Deep Learning in Drug Design: Protein-Ligand Binding Affinity Prediction.

Authors:  Mohammad A Rezaei; Yanjun Li; Dapeng Wu; Xiaolin Li; Chenglong Li
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2022-02-03       Impact factor: 3.710

  8 in total

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