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Fan N, Hirte S, Kirchmair J. Maximizing the Performance of Similarity-Based Virtual Screening Methods by Generating Synergy from the Integration of 2D and 3D Approaches. Int J Mol Sci 2022; 23:ijms23147747. [PMID: 35887097 PMCID: PMC9322642 DOI: 10.3390/ijms23147747] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/05/2022] [Accepted: 07/08/2022] [Indexed: 02/04/2023] Open
Abstract
Methods for the pairwise comparison of 2D and 3D molecular structures are established approaches in virtual screening. In this work, we explored three strategies for maximizing the virtual screening performance of these methods: (i) the merging of hit lists obtained from multi-compound screening using a single screening method, (ii) the merging of the hit lists obtained from 2D and 3D screening by parallel selection, and (iii) the combination of both of these strategies in an integrated approach. We found that any of these strategies led to a boost in virtual screening performance, with the clearest advantages observed for the integrated approach. On test sets for virtual screening, covering 50 pharmaceutically relevant proteins, the integrated approach, using sets of five query molecules, yielded, on average, an area under the receiver operating characteristic curve (AUC) of 0.84, an early enrichment among the top 1% of ranked compounds (EF1%) of 53.82 and a scaffold recovery rate among the top 1% of ranked compounds (SRR1%) of 0.50. In comparison, the 2D and 3D methods on their own (when using a single query molecule) yielded AUC values of 0.68 and 0.54, EF1% values of 19.96 and 17.52, and SRR1% values of 0.20 and 0.17, respectively. In conclusion, based on these results, the integration of 2D and 3D methods, via a (balanced) parallel selection strategy, is recommended, and, in particular, when combined with multi-query screening.
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Affiliation(s)
- Ningning Fan
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany;
| | - Steffen Hirte
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Vienna Doctoral School of Pharmaceutical, Nutritional and Sport Sciences (PhaNuSpo), University of Vienna, 1090 Vienna, Austria
| | - Johannes Kirchmair
- Center for Bioinformatics (ZBH), Department of Informatics, Faculty of Mathematics, Informatics and Natural Sciences, Universität Hamburg, 20146 Hamburg, Germany;
- Division of Pharmaceutical Chemistry, Department of Pharmaceutical Sciences, Faculty of Life Sciences, University of Vienna, 1090 Vienna, Austria;
- Correspondence: ; Tel.: +43-1-4277-55104
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2
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Zhao Y, Wang XG, Ma ZY, Xiong GL, Yang ZJ, Cheng Y, Lu AP, Huang ZJ, Cao DS. Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors. Brief Bioinform 2021; 22:6262239. [PMID: 33940596 DOI: 10.1093/bib/bbab135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 11/12/2022] Open
Abstract
The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.
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Affiliation(s)
- Yue Zhao
- Xiangya School of Pharmaceutical Sciences, Central South University, P. R. China
| | | | - Zhong-Ye Ma
- Xiangya School of Pharmaceutical Sciences, Central South University, P. R. China
| | - Guo-Li Xiong
- Xiangya School of Pharmaceutical Sciences, Central South University, P. R. China
| | - Zhi-Jiang Yang
- Xiangya School of Pharmaceutical Sciences, Central South University, P. R. China
| | - Yan Cheng
- Department of Pharmacy, The Second Xiangya Hospital, Central South University, P. R. China
| | - Ai-Ping Lu
- Institute for Advancing Translational Medicine in Bone and Joint Diseases, School of Chinese Medicine, Hong Kong Baptist University, Hong Kong, P. R. China
| | - Zhi-Jun Huang
- Center for Clinical Pharmacology, The Third Xiangya Hospital of Central South University, Hunan, P. R. China
| | - Dong-Sheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, China
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3
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Mandal SK, Munshi P. Predicting Accurate Lead Structures for Screening Molecular Libraries: A Quantum Crystallographic Approach. Molecules 2021; 26:molecules26092605. [PMID: 33946965 PMCID: PMC8124947 DOI: 10.3390/molecules26092605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 04/22/2021] [Accepted: 04/24/2021] [Indexed: 11/17/2022] Open
Abstract
Optimization of lead structures is crucial for drug discovery. However, the accuracy of such a prediction using the traditional molecular docking approach remains a major concern. Our study demonstrates that the employment of quantum crystallographic approach-counterpoise corrected kernel energy method (KEM-CP) can improve the accuracy by and large. We select human aldose reductase at 0.66 Å, cyclin dependent kinase 2 at 2.0 Å and estrogen receptor β at 2.7 Å resolutions with active site environment ranging from highly hydrophilic to moderate to highly hydrophobic and several of their known ligands. Overall, the use of KEM-CP alongside the GoldScore resulted superior prediction than the GoldScore alone. Unlike GoldScore, the KEM-CP approach is neither environment-specific nor structural resolution dependent, which highlights its versatility. Further, the ranking of the ligands based on the KEM-CP results correlated well with that of the experimental IC50 values. This computationally inexpensive yet simple approach is expected to ease the process of virtual screening of potent ligands, and it would advance the drug discovery research.
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4
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Mirabello C, Wallner B. InterLig: improved ligand-based virtual screening using topologically independent structural alignments. Bioinformatics 2020; 36:3266-3267. [PMID: 32049311 PMCID: PMC7214017 DOI: 10.1093/bioinformatics/btaa089] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 01/29/2020] [Accepted: 02/04/2020] [Indexed: 11/27/2022] Open
Abstract
Motivation In the past few years, drug discovery processes have been relying more and more on computational methods to sift out the most promising molecules before time and resources are spent to test them in experimental settings. Whenever the protein target of a given disease is not known, it becomes fundamental to have accurate methods for ligand-based virtual screening, which compares known active molecules against vast libraries of candidate compounds. Recently, 3D-based similarity methods have been developed that are capable of scaffold hopping and to superimpose matching molecules. Results Here, we present InterLig, a new method for the comparison and superposition of small molecules using topologically independent alignments of atoms. We test InterLig on a standard benchmark and show that it compares favorably to the best currently available 3D methods. Availability and implementation The program is available from http://wallnerlab.org/InterLig. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Claudio Mirabello
- Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
| | - Björn Wallner
- Division of Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden
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5
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Tran-Nguyen VK, Jacquemard C, Rognan D. LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening. J Chem Inf Model 2020; 60:4263-4273. [DOI: 10.1021/acs.jcim.0c00155] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Viet-Khoa Tran-Nguyen
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Célien Jacquemard
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
| | - Didier Rognan
- Laboratoire d’Innovation Thérapeutique, UMR 7200 CNRS-Université de Strasbourg, 67400 Illkirch, France
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6
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Pérez-Sianes J, Pérez-Sánchez H, Díaz F. Virtual Screening Meets Deep Learning. Curr Comput Aided Drug Des 2019; 15:6-28. [PMID: 30338743 DOI: 10.2174/1573409914666181018141602] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2017] [Revised: 10/08/2018] [Accepted: 10/11/2018] [Indexed: 12/27/2022]
Abstract
BACKGROUND Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. OBJECTIVE This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.
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Affiliation(s)
| | - Horacio Pérez-Sánchez
- Bioinformatics and High Performance Computing Research Group (BIO-HPC), Computer Engineering Department, Universidad Católica San Antonio de Murcia (UCAM), Murcia, Spain
| | - Fernando Díaz
- Departamento de Informática, Escuela de Ingeniería Informática, University of Valladolid, Segovia, Spain
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7
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Costa G, Rocca R, Corona A, Grandi N, Moraca F, Romeo I, Talarico C, Gagliardi MG, Ambrosio FA, Ortuso F, Alcaro S, Distinto S, Maccioni E, Tramontano E, Artese A. Novel natural non-nucleoside inhibitors of HIV-1 reverse transcriptase identified by shape- and structure-based virtual screening techniques. Eur J Med Chem 2018; 161:1-10. [PMID: 30342421 DOI: 10.1016/j.ejmech.2018.10.029] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Revised: 10/10/2018] [Accepted: 10/11/2018] [Indexed: 10/28/2022]
Abstract
In this work we report a parallel application of both docking- and shape-based virtual screening (VS) methods, followed by Molecular Dynamics simulations (MDs), for discovering new compounds able to inhibit the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT) RNA-dependent DNA polymerase activity. Specifically, we screened more than 143000 natural compounds commercially available in the ZINC database against the best five RT crystallographic models, taking into account the five approved NNRTIs as query compounds. As a result, 20 hit molecules were selected and tested on biochemical assays for the inhibition of the RNA dependent DNA polymerase RT function and, among them, an indoline pyrrolidine (hit1), an indonyl piperazine (hit2) and an indolyl indolinone (hit3) derivatives were identified as novel non-nucleoside RT inhibitors in the low micromolar range.
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Affiliation(s)
- Giosuè Costa
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Roberta Rocca
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Angela Corona
- Dipartimento di Scienze della Vita e dell'Ambiente, Università di Cagliari, Cittadella Universitaria di Monserrato, SS554, 09042, Monserrato, Cagliari, Italy
| | - Nicole Grandi
- Dipartimento di Scienze della Vita e dell'Ambiente, Università di Cagliari, Cittadella Universitaria di Monserrato, SS554, 09042, Monserrato, Cagliari, Italy
| | - Federica Moraca
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy; Department of Chemical Sciences, University of Napoli Federico II, Via Cinthia 4, I-80126, Napoli, Italy.
| | - Isabella Romeo
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Carmine Talarico
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Maria Giovanna Gagliardi
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Francesca Alessandra Ambrosio
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Francesco Ortuso
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Stefano Alcaro
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
| | - Simona Distinto
- Dipartimento di Scienze della Vita e dell'Ambiente, Università degli Studi di Cagliari, Via Ospedale 72, 09124, Cagliari, Italy
| | - Elias Maccioni
- Dipartimento di Scienze della Vita e dell'Ambiente, Università degli Studi di Cagliari, Via Ospedale 72, 09124, Cagliari, Italy
| | - Enzo Tramontano
- Dipartimento di Scienze della Vita e dell'Ambiente, Università di Cagliari, Cittadella Universitaria di Monserrato, SS554, 09042, Monserrato, Cagliari, Italy
| | - Anna Artese
- Dipartimento di Scienze della Salute, Università degli Studi "Magna Græcia" di Catanzaro, Campus "S. Venuta", Viale Europa, Germaneto, 88100, Catanzaro, Italy
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8
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Imrie F, Bradley AR, van der Schaar M, Deane CM. Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data. J Chem Inf Model 2018; 58:2319-2330. [DOI: 10.1021/acs.jcim.8b00350] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Fergus Imrie
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, U.K
| | - Anthony R. Bradley
- Structural Genomics Consortium, University of Oxford, Oxford OX3 7DQ, U.K
- Department of Chemistry, University of Oxford, Oxford OX1 3TA, U.K
- Diamond Light Source Ltd., Didcot OX11 0DE, U.K
| | - Mihaela van der Schaar
- Department of Engineering, University of Oxford, Oxford OX1 3PJ, U.K
- Alan Turing Institute, London NW1 2DB, U.K
| | - Charlotte M. Deane
- Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, U.K
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9
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Rodríguez-Pérez R, Miyao T, Jasial S, Vogt M, Bajorath J. Prediction of Compound Profiling Matrices Using Machine Learning. ACS OMEGA 2018; 3:4713-4723. [PMID: 30023899 PMCID: PMC6045364 DOI: 10.1021/acsomega.8b00462] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2018] [Accepted: 04/20/2018] [Indexed: 05/25/2023]
Abstract
Screening of compound libraries against panels of targets yields profiling matrices. Such matrices typically contain structurally diverse screening compounds, large numbers of inactives, and small numbers of hits per assay. As such, they represent interesting and challenging test cases for computational screening and activity predictions. In this work, modeling of large compound profiling matrices was attempted that were extracted from publicly available screening data. Different machine learning methods including deep learning were compared and different prediction strategies explored. Prediction accuracy varied for assays with different numbers of active compounds, and alternative machine learning approaches often produced comparable results. Deep learning did not further increase the prediction accuracy of standard methods such as random forests or support vector machines. Target-based random forest models were prioritized and yielded successful predictions of active compounds for many assays.
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10
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Réau M, Langenfeld F, Zagury JF, Lagarde N, Montes M. Decoys Selection in Benchmarking Datasets: Overview and Perspectives. Front Pharmacol 2018; 9:11. [PMID: 29416509 PMCID: PMC5787549 DOI: 10.3389/fphar.2018.00011] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2017] [Accepted: 01/05/2018] [Indexed: 11/24/2022] Open
Abstract
Virtual Screening (VS) is designed to prospectively help identifying potential hits, i.e., compounds capable of interacting with a given target and potentially modulate its activity, out of large compound collections. Among the variety of methodologies, it is crucial to select the protocol that is the most adapted to the query/target system under study and that yields the most reliable output. To this aim, the performance of VS methods is commonly evaluated and compared by computing their ability to retrieve active compounds in benchmarking datasets. The benchmarking datasets contain a subset of known active compounds together with a subset of decoys, i.e., assumed non-active molecules. The composition of both the active and the decoy compounds subsets is critical to limit the biases in the evaluation of the VS methods. In this review, we focus on the selection of decoy compounds that has considerably changed over the years, from randomly selected compounds to highly customized or experimentally validated negative compounds. We first outline the evolution of decoys selection in benchmarking databases as well as current benchmarking databases that tend to minimize the introduction of biases, and secondly, we propose recommendations for the selection and the design of benchmarking datasets.
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Affiliation(s)
- Manon Réau
- Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France
| | - Florent Langenfeld
- Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France
| | - Jean-François Zagury
- Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France
| | - Nathalie Lagarde
- Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France
| | - Matthieu Montes
- Laboratoire GBA, EA4627, Conservatoire National des Arts et Métiers, Paris, France
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11
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Cross JB. Methods for Virtual Screening of GPCR Targets: Approaches and Challenges. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2017; 1705:233-264. [PMID: 29188566 DOI: 10.1007/978-1-4939-7465-8_11] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Virtual screening (VS) has become an integral part of the drug discovery process and is a valuable tool for finding novel chemical starting points for GPCR targets. Ligand-based VS makes use of biochemical data for known, active compounds and has been applied successfully to many diverse GPCRs. Recent progress in GPCR X-ray crystallography has made it possible to incorporate detailed structural information into the VS process. This chapter outlines the latest VS techniques along with examples that highlight successful applications of these methods. Best practices for increasing the likelihood of VS success, as well as ongoing challenges, are also discussed.
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Affiliation(s)
- Jason B Cross
- University of Texas MD Anderson Cancer Center, Houston, TX, 77054, USA.
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12
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Skinnider MA, Dejong CA, Franczak BC, McNicholas PD, Magarvey NA. Comparative analysis of chemical similarity methods for modular natural products with a hypothetical structure enumeration algorithm. J Cheminform 2017; 9:46. [PMID: 29086195 PMCID: PMC5559407 DOI: 10.1186/s13321-017-0234-y] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2017] [Accepted: 08/08/2017] [Indexed: 12/25/2022] Open
Abstract
Natural products represent a prominent source of pharmaceutically and industrially important agents. Calculating the chemical similarity of two molecules is a central task in cheminformatics, with applications at multiple stages of the drug discovery pipeline. Quantifying the similarity of natural products is a particularly important problem, as the biological activities of these molecules have been extensively optimized by natural selection. The large and structurally complex scaffolds of natural products distinguish their physical and chemical properties from those of synthetic compounds. However, no analysis of the performance of existing methods for molecular similarity calculation specific to natural products has been reported to date. Here, we present LEMONS, an algorithm for the enumeration of hypothetical modular natural product structures. We leverage this algorithm to conduct a comparative analysis of molecular similarity methods within the unique chemical space occupied by modular natural products using controlled synthetic data, and comprehensively investigate the impact of diverse biosynthetic parameters on similarity search. We additionally investigate a recently described algorithm for natural product retrobiosynthesis and alignment, and find that when rule-based retrobiosynthesis can be applied, this approach outperforms conventional two-dimensional fingerprints, suggesting it may represent a valuable approach for the targeted exploration of natural product chemical space and microbial genome mining. Our open-source algorithm is an extensible method of enumerating hypothetical natural product structures with diverse potential applications in bioinformatics.
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Affiliation(s)
- Michael A Skinnider
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Chemistry and Chemical Biology, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
| | - Chris A Dejong
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.,Department of Chemistry and Chemical Biology, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada
| | - Brian C Franczak
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada.,Department of Mathematics and Statistics, MacEwan University, Edmonton, AB, Canada
| | - Paul D McNicholas
- Department of Mathematics and Statistics, McMaster University, Hamilton, ON, Canada
| | - Nathan A Magarvey
- Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada. .,Department of Chemistry and Chemical Biology, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, Hamilton, ON, Canada.
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13
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Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR. Protein-Ligand Scoring with Convolutional Neural Networks. J Chem Inf Model 2017; 57:942-957. [PMID: 28368587 PMCID: PMC5479431 DOI: 10.1021/acs.jcim.6b00740] [Citation(s) in RCA: 438] [Impact Index Per Article: 62.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive three-dimensional (3D) representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and nonbinders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening.
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Affiliation(s)
| | | | - Elisa Idrobo
- Department of Computer Science, The College of New Jersey , Ewing, New Jersey 08628, United States
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14
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Evaluation of selected 3D virtual screening tools for the prospective identification of peroxisome proliferator-activated receptor (PPAR) γ partial agonists. Eur J Med Chem 2016; 124:49-62. [DOI: 10.1016/j.ejmech.2016.07.072] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Revised: 07/14/2016] [Accepted: 07/28/2016] [Indexed: 11/20/2022]
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15
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O'Boyle NM, Sayle RA. Comparing structural fingerprints using a literature-based similarity benchmark. J Cheminform 2016; 8:36. [PMID: 27382417 PMCID: PMC4932683 DOI: 10.1186/s13321-016-0148-0] [Citation(s) in RCA: 116] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2016] [Accepted: 06/26/2016] [Indexed: 01/17/2023] Open
Abstract
Background The concept of molecular similarity is one of the central ideas in cheminformatics, despite the fact that it is ill-defined and rather difficult to assess objectively. Here we propose a practical definition of molecular similarity in the context of drug discovery: molecules A and B are similar if a medicinal chemist would be likely to synthesise and test them around the same time as part of the same medicinal chemistry program. The attraction of such a definition is that it matches one of the key uses of similarity measures in early-stage drug discovery. If we make the assumption that molecules in the same compound activity table in a medicinal chemistry paper were considered similar by the authors of the paper, we can create a dataset of similar molecules from the medicinal chemistry literature. Furthermore, molecules with decreasing levels of similarity to a reference can be found by either ordering molecules in an activity table by their activity, or by considering activity tables in different papers which have at least one molecule in common. Results Using this procedure with activity data from ChEMBL, we have created two benchmark datasets for structural similarity that can be used to guide the development of improved measures. Compared to similar results from a virtual screen, these benchmarks are an order of magnitude more sensitive to differences between fingerprints both because of their size and because they avoid loss of statistical power due to the use of mean scores or ranks. We measure the performance of 28 different fingerprints on the benchmark sets and compare the results to those from the Riniker and Landrum (J Cheminf 5:26, 2013. doi:10.1186/1758-2946-5-26) ligand-based virtual screening benchmark. Conclusions Extended-connectivity fingerprints of diameter 4 and 6 are among the best performing fingerprints when ranking diverse structures by similarity, as is the topological torsion fingerprint. However, when ranking very close analogues, the atom pair fingerprint outperforms the others tested. When ranking diverse structures or carrying out a virtual screen, we find that the performance of the ECFP fingerprints significantly improves if the bit-vector length is increased from 1024 to 16,384.An example series from one of the benchmark datasets. Each fingerprint is assessed on its ability to reproduce a specific series order. ![]() Electronic supplementary material The online version of this article (doi:10.1186/s13321-016-0148-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Noel M O'Boyle
- NextMove Software, Innovation Centre, Cambridge Science Park, Milton Road, Cambridge, CB4 0EY UK
| | - Roger A Sayle
- NextMove Software, Innovation Centre, Cambridge Science Park, Milton Road, Cambridge, CB4 0EY UK
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16
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Fragment oriented molecular shapes. J Mol Graph Model 2016; 66:143-54. [PMID: 27085751 DOI: 10.1016/j.jmgm.2016.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2016] [Revised: 03/23/2016] [Accepted: 03/29/2016] [Indexed: 11/23/2022]
Abstract
Molecular shape is an important concept in drug design and virtual screening. Shape similarity typically uses either alignment methods, which dynamically optimize molecular poses with respect to the query molecular shape, or feature vector methods, which are computationally less demanding but less accurate. The computational cost of alignment can be reduced by pre-aligning shapes, as is done with the Volumetric-Aligned Molecular Shapes (VAMS) method. Here, we introduce and evaluate fragment oriented molecular shapes (FOMS), where shapes are aligned based on molecular fragments. FOMS enables the use of shape constraints, a novel method for precisely specifying molecular shape queries that provides the ability to perform partial shape matching and supports search algorithms that function on an interactive time scale. When evaluated using the challenging Maximum Unbiased Validation dataset, shape constraints were able to extract significantly enriched subsets of compounds for the majority of targets, and FOMS matched or exceeded the performance of both VAMS and an optimizing alignment method of shape similarity search.
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17
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Rivera-Borroto OM, García-de la Vega JM, Marrero-Ponce Y, Grau R. Relational Agreement Measures for Similarity Searching of Cheminformatic Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2016; 13:158-67. [PMID: 26886740 DOI: 10.1109/tcbb.2015.2424435] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
Research on similarity searching of cheminformatic data sets has been focused on similarity measures using fingerprints. However, nominal scales are the least informative of all metric scales, increasing the tied similarity scores, and decreasing the effectivity of the retrieval engines. Tanimoto's coefficient has been claimed to be the most prominent measure for this task. Nevertheless, this field is far from being exhausted since the computer science no free lunch theorem predicts that "no similarity measure has overall superiority over the population of data sets". We introduce 12 relational agreement (RA) coefficients for seven metric scales, which are integrated within a group fusion-based similarity searching algorithm. These similarity measures are compared to a reference panel of 21 proximity quantifiers over 17 benchmark data sets (MUV), by using informative descriptors, a feature selection stage, a suitable performance metric, and powerful comparison tests. In this stage, RA coefficients perform favourably with repect to the state-of-the-art proximity measures. Afterward, the RA-based method outperform another four nearest neighbor searching algorithms over the same data domains. In a third validation stage, RA measures are successfully applied to the virtual screening of the NCI data set. Finally, we discuss a possible molecular interpretation for these similarity variants.
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18
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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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Xia J, Tilahun EL, Reid TE, Zhang L, Wang XS. Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods 2015; 71:146-57. [PMID: 25481478 PMCID: PMC4278665 DOI: 10.1016/j.ymeth.2014.11.015] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 11/22/2014] [Accepted: 11/24/2014] [Indexed: 11/21/2022] Open
Abstract
Retrospective small-scale virtual screening (VS) based on benchmarking data sets has been widely used to estimate ligand enrichments of VS approaches in the prospective (i.e. real-world) efforts. However, the intrinsic differences of benchmarking sets to the real screening chemical libraries can cause biased assessment. Herein, we summarize the history of benchmarking methods as well as data sets and highlight three main types of biases found in benchmarking sets, i.e. "analogue bias", "artificial enrichment" and "false negative". In addition, we introduce our recent algorithm to build maximum-unbiased benchmarking sets applicable to both ligand-based and structure-based VS approaches, and its implementations to three important human histone deacetylases (HDACs) isoforms, i.e. HDAC1, HDAC6 and HDAC8. The leave-one-out cross-validation (LOO CV) demonstrates that the benchmarking sets built by our algorithm are maximum-unbiased as measured by property matching, ROC curves and AUCs.
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Affiliation(s)
- Jie Xia
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China; Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Ermias Lemma Tilahun
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Terry-Elinor Reid
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing 100191, PR China.
| | - Xiang Simon Wang
- Molecular Modeling and Drug Discovery Core for District of Columbia Developmental Center for AIDS Research (DC D-CFAR), Laboratory of Cheminformatics and Drug Design, Department of Pharmaceutical Sciences, College of Pharmacy, Howard University, Washington, DC 20059, USA.
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20
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Schuster D. 3D pharmacophores as tools for activity profiling. DRUG DISCOVERY TODAY. TECHNOLOGIES 2013; 7:e203-70. [PMID: 24103796 DOI: 10.1016/j.ddtec.2010.11.006] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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21
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Vainio MJ, Kogej T, Raubacher F, Sadowski J. Scaffold Hopping by Fragment Replacement. J Chem Inf Model 2013; 53:1825-35. [DOI: 10.1021/ci4001019] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Affiliation(s)
- Mikko J. Vainio
- Discovery Sciences Chemistry Innovation Centre, AstraZeneca R&D, Pepparedsleden 1, 43186 Mölndal, Sweden
| | - Thierry Kogej
- Discovery Sciences Chemistry Innovation Centre, AstraZeneca R&D, Pepparedsleden 1, 43186 Mölndal, Sweden
| | - Florian Raubacher
- Discovery Sciences Chemistry Innovation Centre, AstraZeneca R&D, Pepparedsleden 1, 43186 Mölndal, Sweden
| | - Jens Sadowski
- Discovery Sciences Chemistry Innovation Centre, AstraZeneca R&D, Pepparedsleden 1, 43186 Mölndal, Sweden
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22
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Riniker S, Landrum GA. Open-source platform to benchmark fingerprints for ligand-based virtual screening. J Cheminform 2013; 5:26. [PMID: 23721588 PMCID: PMC3686626 DOI: 10.1186/1758-2946-5-26] [Citation(s) in RCA: 207] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2013] [Accepted: 05/20/2013] [Indexed: 01/21/2023] Open
Abstract
Similarity-search methods using molecular fingerprints are an important tool for ligand-based virtual screening. A huge variety of fingerprints exist and their performance, usually assessed in retrospective benchmarking studies using data sets with known actives and known or assumed inactives, depends largely on the validation data sets used and the similarity measure used. Comparing new methods to existing ones in any systematic way is rather difficult due to the lack of standard data sets and evaluation procedures. Here, we present a standard platform for the benchmarking of 2D fingerprints. The open-source platform contains all source code, structural data for the actives and inactives used (drawn from three publicly available collections of data sets), and lists of randomly selected query molecules to be used for statistically valid comparisons of methods. This allows the exact reproduction and comparison of results for future studies. The results for 12 standard fingerprints together with two simple baseline fingerprints assessed by seven evaluation methods are shown together with the correlations between methods. High correlations were found between the 12 fingerprints and a careful statistical analysis showed that only the two baseline fingerprints were different from the others in a statistically significant way. High correlations were also found between six of the seven evaluation methods, indicating that despite their seeming differences, many of these methods are similar to each other.
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Affiliation(s)
- Sereina Riniker
- Novartis Institutes for BioMedical Research, Basel, Switzerland.
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23
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Willett P. Fusing similarity rankings in ligand-based virtual screening. Comput Struct Biotechnol J 2013; 5:e201302002. [PMID: 24688695 PMCID: PMC3962232 DOI: 10.5936/csbj.201302002] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2012] [Revised: 10/04/2012] [Accepted: 10/12/2012] [Indexed: 11/22/2022] Open
Abstract
Data fusion is the name given to a range of methods for combining multiple sources of evidence. This mini-review summarizes the use of one such class of methods for combining the rankings obtained when similarity searching is used for ligand-based virtual screening. Two main approaches are described: similarity fusion involves combining rankings from single searches based on multiple similarity measures; and group fusion involves combining rankings from multiple searches based on a single similarity measure. The review then focuses on the rules that are available for combining similarity rankings, and on the evidence that exists for the superiority of fusion-based methods over conventional similarity searching.
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Affiliation(s)
- Peter Willett
- Information School, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, United Kingdom
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24
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Alcaro S, Musetti C, Distinto S, Casatti M, Zagotto G, Artese A, Parrotta L, Moraca F, Costa G, Ortuso F, Maccioni E, Sissi C. Identification and characterization of new DNA G-quadruplex binders selected by a combination of ligand and structure-based virtual screening approaches. J Med Chem 2013; 56:843-55. [PMID: 23294188 DOI: 10.1021/jm3013486] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Nowadays, it has been demonstrated that DNA G-quadruplex arrangements are involved in cellular aging and cancer, thus boosting the discovery of selective binders for these DNA secondary structures. By taking advantage of available structural and biological information on these structures, we performed a high throughput in silico screening of commercially available molecules databases by merging ligand- and structure-based approaches by means of docking experiments. Compounds selected by the virtual screening procedure were then tested for their ability to interact with the human telomeric G-quadruplex folding by circular dichroism, fluorescence spectroscopy, and photodynamic techniques. Interestingly, our screening succeeded in retrieving a new promising scaffold for G-quadruplex binders characterized by a psoralen moiety.
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Affiliation(s)
- Stefano Alcaro
- Dipartimento di Scienze della Salute, Università di Catanzaro, Campus "Salvatore Venuta", Viale Europa, 88100 Catanzaro, Italy
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25
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Affiliation(s)
- Peter Willett
- Information School, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, United Kingdom.
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26
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Scior T, Bender A, Tresadern G, Medina-Franco JL, Martínez-Mayorga K, Langer T, Cuanalo-Contreras K, Agrafiotis DK. Recognizing Pitfalls in Virtual Screening: A Critical Review. J Chem Inf Model 2012; 52:867-81. [DOI: 10.1021/ci200528d] [Citation(s) in RCA: 294] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Thomas Scior
- Pharmacy Department, Facultad de Ciencias Químicas, Universidad Autónoma de Puebla, Puebla, Pue, México
| | - Andreas Bender
- Unilever Centre for Molecular
Science Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Gary Tresadern
- Research Informatics & Integrative Genomics, Janssen Research & Development, Calle Jarama 75, Poligono Industrial, Toledo 45007, Spain
| | - José L. Medina-Franco
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port
St. Lucie, Florida 34987, United States
| | - Karina Martínez-Mayorga
- Torrey Pines Institute for Molecular Studies, 11350 SW Village Parkway, Port
St. Lucie, Florida 34987, United States
| | - Thierry Langer
- Prestwick Chemical, Blvd Gonthier dʼAndernach, F-67400 Illkirch, France
| | - Karina Cuanalo-Contreras
- Pharmacy Department, Facultad de Ciencias Químicas, Universidad Autónoma de Puebla, Puebla, Pue, México
| | - Dimitris K. Agrafiotis
- Johnson & Johnson Pharmaceutical Research & Development, LLC, Welsh & McKean Roads, Spring House, Pennsylvania 19477, United States
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27
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McKay PB, Fayne D, Horn HW, James T, Peters MB, Carta G, Caboni L, Nevin DK, Price T, Bradley G, Williams DC, Rice JE, Lloyd DG. Consensus Computational Ligand-Based Design for the Identification of Novel Modulators of Human Estrogen Receptor Alpha. Mol Inform 2012; 31:246-58. [DOI: 10.1002/minf.201100127] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2011] [Accepted: 02/10/2012] [Indexed: 01/30/2023]
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Distinto S, Esposito F, Kirchmair J, Cardia MC, Gaspari M, Maccioni E, Alcaro S, Markt P, Wolber G, Zinzula L, Tramontano E. Identification of HIV-1 reverse transcriptase dual inhibitors by a combined shape-, 2D-fingerprint- and pharmacophore-based virtual screening approach. Eur J Med Chem 2012; 50:216-29. [PMID: 22361685 DOI: 10.1016/j.ejmech.2012.01.056] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2011] [Revised: 12/26/2011] [Accepted: 01/27/2012] [Indexed: 01/23/2023]
Abstract
We report the first application of ligand-based virtual screening (VS) methods for discovering new compounds able to inhibit both human immunodeficiency virus type 1 (HIV-1) reverse transcriptase (RT)-associated functions, DNA polymerase and ribonuclease H (RNase H) activities. The overall VS campaign consisted of two consecutive screening processes. In the first, the VS platform Rapid Overlay of Chemical Structures (ROCS) was used to perform in silico shape-based similarity screening on the NCI compounds database in which a hydrazone derivative, previously shown to inhibit the HIV-1 RT, was chosen. As a result, 34 hit molecules were selected and assayed on both RT-associated functions. In the second, the 4 most potent RT inhibitors identified were selected as queries for parallel VS performed by combining shape-based, 2D-fingerprint and 3D-pharmacophore VS methods. Overall, a set of molecules characterized by new different scaffolds were identified as novel inhibitors of both HIV-1 RT-associated activities in the low micromolar range.
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Affiliation(s)
- Simona Distinto
- Dipartimento di Scienze della Salute, Università degli Studi Magna Grecia di Catanzaro, Campus Salvatore Venuta, 88100, Catanzaro, Italy
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29
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Vogel SM, Bauer MR, Boeckler FM. DEKOIS: Demanding Evaluation Kits for Objective in Silico Screening — A Versatile Tool for Benchmarking Docking Programs and Scoring Functions. J Chem Inf Model 2011; 51:2650-65. [DOI: 10.1021/ci2001549] [Citation(s) in RCA: 54] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Simon M. Vogel
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
| | - Matthias R. Bauer
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
| | - Frank M. Boeckler
- Laboratory for Molecular Design and Pharmaceutical Biophysics, Department of Pharmaceutical and Medicinal Chemistry, Institute of Pharmacy, Eberhard Karls University Tuebingen, Auf der Morgenstelle 8, 72076 Tuebingen, Germany
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Heikamp K, Bajorath J. Large-scale similarity search profiling of ChEMBL compound data sets. J Chem Inf Model 2011; 51:1831-9. [PMID: 21728295 DOI: 10.1021/ci200199u] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
A large-scale similarity search investigation has been carried out on 266 well-defined compound activity classes extracted from the ChEMBL database. The analysis was performed using two widely applied two-dimensional (2D) fingerprints that mark opposite ends of the current performance spectrum of these types of fingerprints, i.e., MACCS structural keys and the extended connectivity fingerprint with bond diameter four (ECFP4). For each fingerprint, three nearest neighbor search strategies were applied. On the basis of these search calculations, a similarity search profile of the ChEMBL database was generated. Overall, the fingerprint search campaign was surprisingly successful. In 203 of 266 test cases (∼76%), a compound recovery rate of at least 50% was observed with at least the better performing fingerprint and one search strategy. The similarity search profile also revealed several general trends. For example, fingerprint searching was often characterized by an early enrichment of active compounds in database selection sets. In addition, compound activity classes have been categorized according to different similarity search performance levels, which helps to put the results of benchmark calculations into perspective. Therefore, a compendium of activity classes falling into different search performance categories is provided. On the basis of our large-scale investigation, the performance range of state-of-the-art 2D fingerprinting has been delineated for compound data sets directed against a wide spectrum of pharmaceutical targets.
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Affiliation(s)
- Kathrin Heikamp
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
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Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC, Bender A. From in silico target prediction to multi-target drug design: current databases, methods and applications. J Proteomics 2011; 74:2554-74. [PMID: 21621023 DOI: 10.1016/j.jprot.2011.05.011] [Citation(s) in RCA: 186] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2011] [Revised: 04/10/2011] [Accepted: 05/06/2011] [Indexed: 01/31/2023]
Abstract
Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.
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Affiliation(s)
- Alexios Koutsoukas
- Unilever Centre for Molecular Sciences Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
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Markt P, Schuster D, Langer T. Pharmacophore Models for Virtual Screening. METHODS AND PRINCIPLES IN MEDICINAL CHEMISTRY 2011. [DOI: 10.1002/9783527633326.ch5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
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33
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Pharmacophore alignment search tool: Influence of the third dimension on text-based similarity searching. J Comput Chem 2011; 32:1618-34. [DOI: 10.1002/jcc.21742] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Revised: 11/30/2010] [Accepted: 12/03/2010] [Indexed: 11/07/2022]
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Venkatraman V, Pérez-Nueno VI, Mavridis L, Ritchie DW. Comprehensive Comparison of Ligand-Based Virtual Screening Tools Against the DUD Data set Reveals Limitations of Current 3D Methods. J Chem Inf Model 2010; 50:2079-93. [DOI: 10.1021/ci100263p] [Citation(s) in RCA: 107] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
| | | | - Lazaros Mavridis
- INRIA Nancy Grand Est, LORIA, 54506, Vandoeuvre-lès-Nancy, France
| | - David W. Ritchie
- INRIA Nancy Grand Est, LORIA, 54506, Vandoeuvre-lès-Nancy, France
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Bender A. How similar are those molecules after all? Use two descriptors and you will have three different answers. Expert Opin Drug Discov 2010; 5:1141-51. [DOI: 10.1517/17460441.2010.517832] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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López-Ramos M, Perruccio F. HPPD: ligand- and target-based virtual screening on a herbicide target. J Chem Inf Model 2010; 50:801-14. [PMID: 20359237 DOI: 10.1021/ci900498n] [Citation(s) in RCA: 26] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Hydroxyphenylpyruvate dioxygenase (HPPD) has proven to be a very successful target for the development of herbicides with bleaching properties, and today HPPD inhibitors are well established in the agrochemical market. Syngenta has a long history of HPPD-inhibitor research, and HPPD was chosen as a case study for the validation of diverse ligand- and target-based virtual screening approaches to identify compounds with inhibitory properties. Two-dimensional extended connectivity fingerprints, three-dimensional shape-based tools (ROCS, EON, and Phase-shape) and a pharmacophore approach (Phase) were used as ligand-based methods; Glide and Gold were used as target-based. Both the virtual screening utility and the scaffold-hopping ability of the screening tools were assessed. Particular emphasis was put on the specific pitfalls to take into account for the design of a virtual screening campaign in an agrochemical context, as compared to a pharmaceutical environment.
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Affiliation(s)
- Miriam López-Ramos
- Syngenta Crop Protection, Muenchwilen AG, WST-820.1.15, Schaffhauserstrasse, CH-4332 Stein, Switzerland.
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Geppert H, Vogt M, Bajorath J. Current trends in ligand-based virtual screening: molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 2010; 50:205-16. [PMID: 20088575 DOI: 10.1021/ci900419k] [Citation(s) in RCA: 231] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Hanna Geppert
- Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universitat, Dahlmannstrasse 2, D-53113 Bonn, Germany
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