Literature DB >> 31995074

Combined strategies in structure-based virtual screening.

Zhe Wang1, Huiyong Sun1, Chao Shen1, Xueping Hu1, Junbo Gao1, Dan Li1, Dongsheng Cao2, Tingjun Hou1.   

Abstract

The identification and optimization of lead compounds are inalienable components in drug design and discovery pipelines. As a powerful computational approach for the identification of hits with novel structural scaffolds, structure-based virtual screening (SBVS) has exhibited a remarkably increasing influence in the early stages of drug discovery. During the past decade, a variety of techniques and algorithms have been proposed and tested with different purposes in the scope of SBVS. Although SBVS has been a common and proven technology, it still shows some challenges and problems that are needed to be addressed, where the negative influence regardless of protein flexibility and the inaccurate prediction of binding affinity are the two major challenges. Here, focusing on these difficulties, we summarize a series of combined strategies or workflows developed by our group and others. Furthermore, several representative successful applications from recent publications are also discussed to demonstrate the effectiveness of the combined SBVS strategies in drug discovery campaigns.

Year:  2020        PMID: 31995074     DOI: 10.1039/c9cp06303j

Source DB:  PubMed          Journal:  Phys Chem Chem Phys        ISSN: 1463-9076            Impact factor:   3.676


  15 in total

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Journal:  Acta Pharmacol Sin       Date:  2021-10-19       Impact factor: 7.169

Review 2.  Structural and Functional Diversity of Resistance-Nodulation-Cell Division Transporters.

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3.  Machine learning on ligand-residue interaction profiles to significantly improve binding affinity prediction.

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Journal:  Brief Bioinform       Date:  2021-09-02       Impact factor: 11.622

4.  Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays.

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6.  Improvement of Prediction Performance With Conjoint Molecular Fingerprint in Deep Learning.

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7.  Screening of β1- and β2-Adrenergic Receptor Modulators through Advanced Pharmacoinformatics and Machine Learning Approaches.

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Review 8.  Screening S protein - ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19.

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Review 9.  Automation and data-driven design of polymer therapeutics.

Authors:  Rahul Upadhya; Shashank Kosuri; Matthew Tamasi; Travis A Meyer; Supriya Atta; Michael A Webb; Adam J Gormley
Journal:  Adv Drug Deliv Rev       Date:  2020-11-24       Impact factor: 15.470

10.  Computer-Aided Drug Design (CADD) to De-Orphanize Marine Molecules: Finding Potential Therapeutic Agents for Neurodegenerative and Cardiovascular Diseases.

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Journal:  Mar Drugs       Date:  2022-01-05       Impact factor: 5.118

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