Targeted scoring functions for virtual screening

Seifert, Markus H. J.

doi:10.1016/j.drudis.2009.03.013

Cited by 69 publications

(58 citation statements)

References 50 publications

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“…The use of target-specific scoring models was comprehensively reviewed recently by Seifert. [16] The target-specific scoring models and pharmacophore constraints derived from the ISAC approach were validated on various data sets taking external test data into account. We performed structure-based virtual screening experiments for the targets thrombin, angiotensin-converting enzyme (ACE), protein tyrosine phosphatase 1B (PTP-1B) and checkpoint kinase 1 (CHK1).…”

Section: Introductionmentioning

confidence: 99%

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

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The role of activity cliffs in drug discovery projects is certainly two-edged: on the one hand, they often lead to the failure of QSAR modeling techniques; on the other, they are highly valuable for identifying key aspects of SARs. In the presence of activity cliffs the results of purely ligand-based QSAR approaches often remain puzzling, and the resulting models have limited predictive power. Herein we present a new approach for the identification of structure-based activity cliffs (ISAC). It uses the valuable information of activity cliffs in a structure-based design scenario by analyzing interaction energies of protein-ligand complexes. Using the relative frequency at which a protein atom is involved in activity cliff events, we introduce a novel visualization of hot spots in the active site of a protein. The ISAC approach supports the medicinal chemist in elucidating the key interacting atoms of the binding site and facilitates the development of pharmacophore hypotheses. The hot spot visualization can be applied to small data sets in early project phases as well as in the lead optimization process. Based on the ISAC approach, we developed a method to derive target-specific scoring functions and pharmacophore constraints, which were validated on independent external data sets in virtual screening experiments. The activity-cliff-based approach shows an improved enrichment over the generic empirical scoring function for various protein targets in the validation set.

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Section: Introductionmentioning

confidence: 99%

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

2011

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“…One of the key strategies of the virtual screening was filtering the Protein-Ligand Interaction Fingerprint (PLIF) (Marcou and Rognan, 2007; ): Only docking poses that form a hydrogen bond (H-bond) and an ionic interaction with D107 were considered (de . This strategy can be recognized as "using prior knowledge" in SBVS campaigns (Seifert, 2009;Yuniarti et al, 2011). The customization of the SBVS protocols by filtering on key interactions has increased the SBVS quality significantly (de Sirci et al, 2012;Yuniarti et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Construction and Retrospective Validation of Structure-Based Virtual Screening Protocols to Identify Potent Ligands for Human Adrenergic Beta-2 Receptor

Istyastono

Setyaningsih

2015

Indonesian J. Pharm.

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Adrenergic Beta-2 Receptor (ADRB2) is a member of Gprotein coupled receptors family, which has served as targets for more than 30% of top-selling drugs in the market. Recently, an enhanced dataset of ligands and decoys for ADRB2 has publicly available. However, the original retrospective structure-based virtual screening campaign accompanying the dataset showed relatively poor quality with enrichment factor of true positives at 1% false positives (EF 1% ) value of 3.9. In this article, the construction and retrospective validation of a structure-based virtual screening protocol by employing PLANTS1.2 as the molecular docking software and PyPLIF as an alternative post docking scoring functions are presented. The results show that the developed protocols have better quality compared the original structure-based virtual screening with EF 1% values of 24.24 and 8.22 by using ChemPLP from PLANTS1.2 and by using Tc-PLIF from PyPLIF, respectively. Further investigation by performing systematic filtering resulted in the identification of D113, S203, and N293 as molecular determinants in ADRB2-ligand binding.

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“…As a consequence, it is not possible to know a priori if a docking procedure is more suitable than another one for a particular task. Several attempts to increase docking accuracy and reliability, thus improving its VS performance, reported in literature concerned the development of optimized SFs, rescoring procedures and pose filtering methods 13,[25][26][27][28][29][30][31] . Among these strategies, the combination of different scoring functions (consenus scoring) to improve VS hit rates is a well known practice introduced by Charifson et al in 1999 31 that is still commonly applied [33][34][35][36][37][38] .…”

Section: Introductionmentioning

confidence: 99%

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

Poli

Martinelli

Tuccinardi

2016

Journal of Enzyme Inhibition and Medicinal Chemistry

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Ligand-protein docking is one of the most common techniques used in virtual screening campaigns. Despite the large number of docking software available, there is still the need of improving the efficacy of docking-based virtual screenings. To date, only very few studies evaluated the possibility of combining the results of different docking methods to achieve higher success rates in virtual screening studies (consensus docking). In order to better understand the range of applicability of this approach, we carried out an extensive enriched database analysis using the DUD dataset. The consensus docking protocol was then refined by applying modifications concerning the calculation of pose consensus and the combination of docking methods included in the procedure. The results obtained suggest that this approach performs as well as the best available methods found in literature, confirming the idea that this procedure can be profitably used for the identification of new hit compounds.

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Targeted scoring functions for virtual screening

Cited by 69 publications

References 50 publications

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

From Activity Cliffs to Target‐Specific Scoring Models and Pharmacophore Hypotheses

Construction and Retrospective Validation of Structure-Based Virtual Screening Protocols to Identify Potent Ligands for Human Adrenergic Beta-2 Receptor

Reliability analysis and optimization of the consensus docking approach for the development of virtual screening studies

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