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Multi-PLI: interpretable multi-task deep learning model for unifying protein-ligand interaction datasets. J Cheminform 2021; 13:30. [PMID: 33858485 PMCID: PMC8051026 DOI: 10.1186/s13321-021-00510-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 04/08/2021] [Indexed: 11/11/2022] Open
Abstract
The assessment of protein–ligand interactions is critical at early stage of drug discovery. Computational approaches for efficiently predicting such interactions facilitate drug development. Recently, methods based on deep learning, including structure- and sequence-based models, have achieved impressive performance on several different datasets. However, their application still suffers from a generalizability issue because of insufficient data, especially for structure based models, as well as a heterogeneity problem because of different label measurements and varying proteins across datasets. Here, we present an interpretable multi-task model to evaluate protein–ligand interaction (Multi-PLI). The model can run classification (binding or not) and regression (binding affinity) tasks concurrently by unifying different datasets. The model outperforms traditional docking and machine learning on both binary classification and regression tasks and achieves competitive results compared with some structure-based deep learning methods, even with the same training set size. Furthermore, combined with the proposed occlusion algorithm, the model can predict the important amino acids of proteins that are crucial for binding, thus providing a biological interpretation.
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Podlewska S, Kurczab R. Mutual Support of Ligand- and Structure-Based Approaches-To What Extent We Can Optimize the Power of Predictive Model? Case Study of Opioid Receptors. Molecules 2021; 26:molecules26061607. [PMID: 33799356 PMCID: PMC7998793 DOI: 10.3390/molecules26061607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 03/10/2021] [Accepted: 03/11/2021] [Indexed: 11/16/2022] Open
Abstract
The process of modern drug design would not exist in the current form without computational methods. They are part of every stage of the drug design pipeline, supporting the search and optimization of new bioactive substances. Nevertheless, despite the great help that is offered by in silico strategies, the power of computational methods strongly depends on the input data supplied at the stage of the predictive model construction. The studies on the efficiency of the computational protocols most often focus on global efficiency. They use general parameters that refer to the whole dataset, such as accuracy, precision, mean squared error, etc. In the study, we examined machine learning predictions obtained for opioid receptors (mu, kappa, delta) and focused on cases for which the predictions were the most accurate and the least accurate. Moreover, by using docking, we tried to explain prediction errors. We attempted to develop a rule of thumb, which can help in the prediction of compound activity towards opioid receptors via docking, especially those that have been incorrectly predicted by machine learning. We found out that although the combination of ligand- and structure-based path can be beneficial for the prediction accuracy, there still remain cases that cannot be reliably predicted by any available modeling method. In addition to challenging ligand- and structure-based predictions, we also examined the role of the application of machine-learning methods in comparison to simple statistical methods for both standard ligand-based representations (molecular fingerprints) and interaction fingerprints. All approaches were confronted in both classification (where compounds were assigned to the group of active and inactive group constructed on the basis of Ki values) and regression (where exact Ki value was predicted) experiments.
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Affiliation(s)
- Sabina Podlewska
- Department of Technology and Biotechnology of Drugs, Jagiellonian University, Medical College, 9 Medyczna Street, 30-688 Cracow, Poland;
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland
| | - Rafał Kurczab
- Maj Institute of Pharmacology, Polish Academy of Sciences, 12 Smętna Street, 31-343 Cracow, Poland
- Correspondence: ; Tel.: +48-1266-23-301
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Grimm M, Liu Y, Yang X, Bu C, Xiao Z, Cao Y. LigMate: A Multifeature Integration Algorithm for Ligand-Similarity-Based Virtual Screening. J Chem Inf Model 2020; 60:6044-6053. [DOI: 10.1021/acs.jcim.9b01210] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Maximilian Grimm
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Liu
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Xiaocong Yang
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Chunya Bu
- College of Biological Science and Engineering, Beijing University of Agriculture, Beijing 102206, China
| | - Zhixiong Xiao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
| | - Yang Cao
- Center of Growth, Metabolism and Aging, Key Laboratory of Bio-Resource and Eco-Environment of Ministry of Education, College of Life Sciences, Sichuan University, Chengdu 610065, China
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De Vita S, Lauro G, Ruggiero D, Terracciano S, Riccio R, Bifulco G. Protein Preparation Automatic Protocol for High-Throughput Inverse Virtual Screening: Accelerating the Target Identification by Computational Methods. J Chem Inf Model 2019; 59:4678-4690. [DOI: 10.1021/acs.jcim.9b00428] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Affiliation(s)
- Simona De Vita
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Gianluigi Lauro
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Dafne Ruggiero
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Stefania Terracciano
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Raffaele Riccio
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
| | - Giuseppe Bifulco
- Department of Pharmacy, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
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Vucicevic J, Nikolic K, Mitchell JB. Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches. Curr Med Chem 2019; 26:3874-3889. [DOI: 10.2174/0929867324666170712115411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 06/06/2017] [Accepted: 06/13/2017] [Indexed: 01/07/2023]
Abstract
Background:Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation.Results:Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile.Conclusion:In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.
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Affiliation(s)
- Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade, Vojvode Stepe 450, 11000 Belgrade, Serbia
| | - John B.O. Mitchell
- EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, St Andrews KY16 9ST, United Kingdom
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Insights into an alternative benzofuran binding mode and novel scaffolds of polyketide synthase 13 inhibitors. J Mol Model 2019; 25:130. [DOI: 10.1007/s00894-019-4010-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 03/29/2019] [Indexed: 01/01/2023]
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Mavrogeni ME, Pronios F, Zareifi D, Vasilakaki S, Lozach O, Alexopoulos L, Meijer L, Myrianthopoulos V, Mikros E. A facile consensus ranking approach enhances virtual screening robustness and identifies a cell-active DYRK1α inhibitor. Future Med Chem 2018; 10:2411-2430. [PMID: 30325204 PMCID: PMC6479281 DOI: 10.4155/fmc-2018-0198] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2018] [Accepted: 08/16/2018] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Virtual screening is vital for contemporary drug discovery but striking performance fluctuations are commonly encountered, thus hampering error-free use. Results and Methodology: A conceptual framework is suggested for combining screening algorithms characterized by orthogonality (docking-scoring calculations, 3D shape similarity, 2D fingerprint similarity) into a simple, efficient and expansible python-based consensus ranking scheme. An original experimental dataset is created for comparing individual screening methods versus the novel approach. Its utilization leads to identification and phosphoproteomic evaluation of a cell-active DYRK1α inhibitor. CONCLUSION Consensus ranking considerably stabilizes screening performance at reasonable computational cost, whereas individual screens are heavily dependent on calculation settings. Results indicate that the novel approach, currently available as a free online tool, is highly suitable for prospective screening by nonexperts.
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Affiliation(s)
- Maria E Mavrogeni
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
| | - Filippos Pronios
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
| | - Danae Zareifi
- ProtATonce Ltd, Dimokritos Science Park, Agia Paraskevi, 153 43 Athens, Greece
| | - Sofia Vasilakaki
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
| | - Olivier Lozach
- Laboratoire Chimie Electrochimie Moléculaires et Chimie Analytique, University of Brest, 29238 Brest, France
| | - Leonidas Alexopoulos
- School of Mechanical Engineering, National Technical University of Athens, 157 80 Athens, Greece
| | - Laurent Meijer
- ManRos Therapeutics, Perharidy Research Center, 29680 Roscoff, Bretagne, France
| | - Vassilios Myrianthopoulos
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
- ‘Athena’ Research & Innovation Center, 151 25 Athens, Greece
| | - Emmanuel Mikros
- Department of Pharmaceutical Chemistry, School of Pharmacy, University of Athens, Panepistimiopolis Zografou, 157 71 Athens, Greece
- ‘Athena’ Research & Innovation Center, 151 25 Athens, Greece
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Passeri GI, Trisciuzzi D, Alberga D, Siragusa L, Leonetti F, Mangiatordi GF, Nicolotti O. Strategies of Virtual Screening in Medicinal Chemistry. ACTA ACUST UNITED AC 2018. [DOI: 10.4018/ijqspr.2018010108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Virtual screening represents an effective computational strategy to rise-up the chances of finding new bioactive compounds by accelerating the time needed to move from an initial intuition to market. Classically, the most pursued approaches rely on ligand- and structure-based studies, the former employed when structural data information about the target is missing while the latter employed when X-ray/NMR solved or homology models are instead available for the target. The authors will focus on the most advanced techniques applied in this area. In particular, they will survey the key concepts of virtual screening by discussing how to properly select chemical libraries, how to make database curation, how to applying and- and structure-based techniques, how to wisely use post-processing methods. Emphasis will be also given to the most meaningful databases used in VS protocols. For the ease of discussion several examples will be presented.
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Affiliation(s)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Lydia Siragusa
- Molecular Discovery Ltd., Pinner, Middlesex, London, United Kingdom
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
| | - Giuseppe F. Mangiatordi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, Bari, Italy
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Sommer T, Hübner H, El Kerdawy A, Gmeiner P, Pischetsrieder M, Clark T. Identification of the Beer Component Hordenine as Food-Derived Dopamine D2 Receptor Agonist by Virtual Screening a 3D Compound Database. Sci Rep 2017; 7:44201. [PMID: 28281694 PMCID: PMC5345022 DOI: 10.1038/srep44201] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2016] [Accepted: 02/06/2017] [Indexed: 01/11/2023] Open
Abstract
The dopamine D2 receptor (D2R) is involved in food reward and compulsive food intake. The present study developed a virtual screening (VS) method to identify food components, which may modulate D2R signalling. In contrast to their common applications in drug discovery, VS methods are rarely applied for the discovery of bioactive food compounds. Here, databases were created that exclusively contain substances occurring in food and natural sources (about 13,000 different compounds in total) as the basis for combined pharmacophore searching, hit-list clustering and molecular docking into D2R homology models. From 17 compounds finally tested in radioligand assays to determine their binding affinities, seven were classified as hits (hit rate = 41%). Functional properties of the five most active compounds were further examined in β-arrestin recruitment and cAMP inhibition experiments. D2R-promoted G-protein activation was observed for hordenine, a constituent of barley and beer, with approximately identical ligand efficacy as dopamine (76%) and a Ki value of 13 μM. Moreover, hordenine antagonised D2-mediated β-arrestin recruitment indicating functional selectivity. Application of our databases provides new perspectives for the discovery of bioactive food constituents using VS methods. Based on its presence in beer, we suggest that hordenine significantly contributes to mood-elevating effects of beer.
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Affiliation(s)
- Thomas Sommer
- Computer Chemistry Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, Germany
- Food Chemistry Unit, Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schuhstr. 19, 91052 Erlangen, Germany
| | - Harald Hübner
- Department of Chemistry and Pharmacy, Emil Fischer Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schuhstr. 19, 91052 Erlangen, Germany
| | - Ahmed El Kerdawy
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Cairo University, Kasr-el-Aini Street, Cairo, P. O. Box 11562, Egypt
- Molecular Modeling Unit, Faculty of Pharmacy, Cairo University, Kasr-el-Aini Street, Cairo, P. O. Box 11562, Egypt
| | - Peter Gmeiner
- Department of Chemistry and Pharmacy, Emil Fischer Center, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schuhstr. 19, 91052 Erlangen, Germany
| | - Monika Pischetsrieder
- Food Chemistry Unit, Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Schuhstr. 19, 91052 Erlangen, Germany
| | - Timothy Clark
- Computer Chemistry Center, Department of Chemistry and Pharmacy, Friedrich-Alexander-Universität Erlangen-Nürnberg, Nägelsbachstr. 25, 91052 Erlangen, Germany
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Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K, Mitchell JBO. Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies. Front Neurosci 2016; 10:265. [PMID: 27375423 PMCID: PMC4901078 DOI: 10.3389/fnins.2016.00265] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2016] [Accepted: 05/25/2016] [Indexed: 11/13/2022] Open
Abstract
HIGHLIGHTSMany CNS targets are being explored for multi-target drug design New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds QSAR, virtual screening and docking methods increase the potential of rational drug design
The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D1-R/D2-R/5-HT2A-R/H3-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.
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Affiliation(s)
- Katarina Nikolic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Lazaros Mavridis
- School of Biological and Chemical Sciences, Queen Mary University of London London, UK
| | - Teodora Djikic
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - Jelica Vucicevic
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Danica Agbaba
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Belgrade Belgrade, Serbia
| | - Kemal Yelekci
- Department of Bioinformatics and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University Istanbul, Turkey
| | - John B O Mitchell
- EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews St Andrews, UK
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Schultes S, Kooistra AJ, Vischer HF, Nijmeijer S, Haaksma EEJ, Leurs R, de Esch IJP, de Graaf C. Combinatorial Consensus Scoring for Ligand-Based Virtual Fragment Screening: A Comparative Case Study for Serotonin 5-HT(3)A, Histamine H(1), and Histamine H(4) Receptors. J Chem Inf Model 2015; 55:1030-44. [PMID: 25815783 DOI: 10.1021/ci500694c] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
In the current study we have evaluated the applicability of ligand-based virtual screening (LBVS) methods for the identification of small fragment-like biologically active molecules using different similarity descriptors and different consensus scoring approaches. For this purpose, we have evaluated the performance of 14 chemical similarity descriptors in retrospective virtual screening studies to discriminate fragment-like ligands of three membrane-bound receptors from fragments that are experimentally determined to have no affinity for these proteins (true inactives). We used a complete fragment affinity data set of experimentally determined ligands and inactives for two G protein-coupled receptors (GPCRs), the histamine H1 receptor (H1R) and the histamine H4 receptor (H4R), and one ligand-gated ion channel (LGIC), the serotonin receptor (5-HT3AR), to validate our retrospective virtual screening studies. We have exhaustively tested consensus scoring strategies that combine the results of multiple actives (group fusion) or combine different similarity descriptors (similarity fusion), and for the first time systematically evaluated different combinations of group fusion and similarity fusion approaches. Our studies show that for these three case study protein targets both consensus scoring approaches can increase virtual screening enrichments compared to single chemical similarity search methods. Our cheminformatics analyses recommend to use a combination of both group fusion and similarity fusion for prospective ligand-based virtual fragment screening.
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Affiliation(s)
- Sabine Schultes
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Albert J Kooistra
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Henry F Vischer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Saskia Nijmeijer
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Eric E J Haaksma
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Rob Leurs
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Iwan J P de Esch
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
| | - Chris de Graaf
- †Division of Medicinal Chemistry, Faculty of Sciences, Amsterdam Institute for Molecules, Medicines and Systems (AIMMS), VU University Amsterdam, De Boelelaan 1083, 1081 HV Amsterdam, The Netherlands
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Exploring conformational search protocols for ligand-based virtual screening and 3-D QSAR modeling. J Comput Aided Mol Des 2014; 29:165-82. [DOI: 10.1007/s10822-014-9813-4] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2014] [Accepted: 11/06/2014] [Indexed: 10/24/2022]
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