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      Advances in virtual screening

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      Author(s): * ,
      Publication date (Electronic):
      Journal: Drug Discovery Today. Technologies
      Publisher: Elsevier Ltd.

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          Abstract

          Although the term virtual screening as the in silico analog of high throughput screening has been coined only a decade ago, virtual screening is now a widespread lead identification method in the pharmaceutical industry. A myriad of different methods have been developed exploiting the growing library of target structures and assay data as a basis for finding new lead structures. Exploiting synergies between different methods best utilizes the information available and is at the center of recent developments.

          Section editors:

          Tudor Oprea – University of New Mexico, School of Medicine, Albuquerque, USA

          Alex Tropsha – University of North Carolina, Chapel Hill, USA

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          Similarity-based virtual screening using 2D fingerprints.

          Peter Willett (2006)
          This paper summarizes recent work at the University of Sheffield on virtual screening methods that use 2D fingerprint measures of structural similarity. A detailed comparison of a large number of similarity coefficients demonstrates that the well-known Tanimoto coefficient remains the method of choice for the computation of fingerprint-based similarity, despite possessing some inherent biases related to the sizes of the molecules that are being sought. Group fusion involves combining the results of similarity searches based on multiple reference structures and a single similarity measure. We demonstrate the effectiveness of this approach to screening, and also describe an approximate form of group fusion, turbo similarity searching, that can be used when just a single reference structure is available.
          Article 0 comments Cited 196 times – based on 0 reviews     Review now
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            Virtual and biomolecular screening converge on a selective agonist for GPR30.

            Cristian Bologa, Chetana M Revankar, Susan M. Young … (2006)
            Estrogen is a hormone critical in the development, normal physiology and pathophysiology of numerous human tissues. The effects of estrogen have traditionally been solely ascribed to estrogen receptor alpha (ERalpha) and more recently ERbeta, members of the soluble, nuclear ligand-activated family of transcription factors. We have recently shown that the seven-transmembrane G protein-coupled receptor GPR30 binds estrogen with high affinity and resides in the endoplasmic reticulum, where it activates multiple intracellular signaling pathways. To differentiate between the functions of ERalpha or ERbeta and GPR30, we used a combination of virtual and biomolecular screening to isolate compounds that selectively bind to GPR30. Here we describe the identification of the first GPR30-specific agonist, G-1 (1), capable of activating GPR30 in a complex environment of classical and new estrogen receptors. The development of compounds specific to estrogen receptor family members provides the opportunity to increase our understanding of these receptors and their contribution to estrogen biology.
            Article 0 comments Cited 190 times – based on 0 reviews     Review now
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              A general and fast scoring function for protein-ligand interactions: a simplified potential approach.

              I Muegge, Y Martin (1999)
              A fast, simplified potential-based approach is presented that estimates the protein-ligand binding affinity based on the given 3D structure of a protein-ligand complex. This general, knowledge-based approach exploits structural information of known protein-ligand complexes extracted from the Brookhaven Protein Data Bank and converts it into distance-dependent Helmholtz free interaction energies of protein-ligand atom pairs (potentials of mean force, PMF). The definition of an appropriate reference state and the introduction of a correction term accounting for the volume taken by the ligand were found to be crucial for deriving the relevant interaction potentials that treat solvation and entropic contributions implicitly. A significant correlation between experimental binding affinities and computed score was found for sets of diverse protein-ligand complexes and for sets of different ligands bound to the same target. For 77 protein-ligand complexes taken from the Brookhaven Protein Data Bank, the calculated score showed a standard deviation from observed binding affinities of 1.8 log Ki units and an R2 value of 0.61. The best results were obtained for the subset of 16 serine protease complexes with a standard deviation of 1.0 log Ki unit and an R2 value of 0.86. A set of 33 inhibitors modeled into a crystal structure of HIV-1 protease yielded a standard deviation of 0.8 log Ki units from measured inhibition constants and an R2 value of 0.74. In contrast to empirical scoring functions that show similar or sometimes better correlation with observed binding affinities, our method does not involve deriving specific parameters that fit the observed binding affinities of protein-ligand complexes of a given training set. We compared the performance of the PMF score, Böhm's score (LUDI), and the SMOG score for eight different test sets of protein-ligand complexes. It was found that for the majority of test sets the PMF score performs best. The strength of the new approach presented here lies in its generality as no knowledge about measured binding affinities is needed to derive atomic interaction potentials. The use of the new scoring function in docking studies is outlined.
              Article 0 comments Cited 134 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Ingo Muegge
                Journal
                Journal ID (nlm-ta): Drug Discov Today Technol
                Journal ID (iso-abbrev): Drug Discov Today Technol
                Title: Drug Discovery Today. Technologies
                Publisher: Elsevier Ltd.
                ISSN (Electronic): 1740-6749
                Publication date PMC-release: 12 January 2007
                Publication date (Print): Winter 2006
                Publication date (Electronic): 12 January 2007
                Volume: 3
                Issue: 4
                Pages: 405-411
                Affiliations
                Boehringer Ingelheim Pharmaceuticals Inc., 900 Ridgebury Road, Ridgefield, P.O. Box 368, CT 06877-368, USA
                Author notes
                Article
                Publisher ID: S1740-6749(06)00075-8
                DOI: 10.1016/j.ddtec.2006.12.002
                PMC ID: 7105922
                SO-VID: 7fd87e9a-3899-4c84-bc5e-f2c6f69e5fac
                Copyright © Copyright © 2006 Elsevier Ltd. All rights reserved.
                License:

                Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.

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