51
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Humbeck L, Weigang S, Schäfer T, Mutzel P, Koch O. CHIPMUNK: A Virtual Synthesizable Small-Molecule Library for Medicinal Chemistry, Exploitable for Protein-Protein Interaction Modulators. ChemMedChem 2018; 13:532-539. [PMID: 29392860 DOI: 10.1002/cmdc.201700689] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 01/27/2018] [Indexed: 02/05/2023]
Abstract
A common issue during drug design and development is the discovery of novel scaffolds for protein targets. On the one hand the chemical space of purchasable compounds is rather limited; on the other hand artificially generated molecules suffer from a grave lack of accessibility in practice. Therefore, we generated a novel virtual library of small molecules which are synthesizable from purchasable educts, called CHIPMUNK (CHemically feasible In silico Public Molecular UNiverse Knowledge base). Altogether, CHIPMUNK covers over 95 million compounds and encompasses regions of the chemical space that are not covered by existing databases. The coverage of CHIPMUNK exceeds the chemical space spanned by the Lipinski rule of five to foster the exploration of novel and difficult target classes. The analysis of the generated property space reveals that CHIPMUNK is well suited for the design of protein-protein interaction inhibitors (PPIIs). Furthermore, a recently developed structural clustering algorithm (StruClus) for big data was used to partition the sub-libraries into meaningful subsets and assist scientists to process the large amount of data. These clustered subsets also contain the target space based on ChEMBL data which was included during clustering.
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Affiliation(s)
- Lina Humbeck
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
| | - Sebastian Weigang
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
| | - Till Schäfer
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Straße 14, Dortmund, 44227, Germany
| | - Petra Mutzel
- Department of Computer Science, TU Dortmund University, Otto-Hahn-Straße 14, Dortmund, 44227, Germany
| | - Oliver Koch
- Faculty of Chemistry and Chemical Biology, TU Dortmund University, Otto-Hahn-Straße 6, Dortmund, 44227, Germany
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52
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Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks. ACS CENTRAL SCIENCE 2018; 4:120-131. [PMID: 29392184 PMCID: PMC5785775 DOI: 10.1021/acscentsci.7b00512] [Citation(s) in RCA: 642] [Impact Index Per Article: 107.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2017] [Indexed: 05/20/2023]
Abstract
In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
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Affiliation(s)
- Marwin H. S. Segler
- Institute of Organic
Chemistry & Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, 48149 Münster, Germany
| | - Thierry Kogej
- Hit Discovery, Discovery Sciences, AstraZeneca R&D, Gothenburg, Sweden
| | - Christian Tyrchan
- Department of Medicinal
Chemistry, IMED RIA, AstraZeneca R&D, Gothenburg, Sweden
| | - Mark P. Waller
- Department of Physics & International Centre for Quantum and
Molecular Structures, Shanghai University, Shanghai, China
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53
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Chevillard F, Rimmer H, Betti C, Pardon E, Ballet S, van Hilten N, Steyaert J, Diederich WE, Kolb P. Binding-Site Compatible Fragment Growing Applied to the Design of β 2-Adrenergic Receptor Ligands. J Med Chem 2018; 61:1118-1129. [PMID: 29364664 DOI: 10.1021/acs.jmedchem.7b01558] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Fragment-based drug discovery is intimately linked to fragment extension approaches that can be accelerated using software for de novo design. Although computers allow for the facile generation of millions of suggestions, synthetic feasibility is however often neglected. In this study we computationally extended, chemically synthesized, and experimentally assayed new ligands for the β2-adrenergic receptor (β2AR) by growing fragment-sized ligands. In order to address the synthetic tractability issue, our in silico workflow aims at derivatized products based on robust organic reactions. The study started from the predicted binding modes of five fragments. We suggested a total of eight diverse extensions that were easily synthesized, and further assays showed that four products had an improved affinity (up to 40-fold) compared to their respective initial fragment. The described workflow, which we call "growing via merging" and for which the key tools are available online, can improve early fragment-based drug discovery projects, making it a useful creative tool for medicinal chemists during structure-activity relationship (SAR) studies.
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Affiliation(s)
- Florent Chevillard
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
| | - Helena Rimmer
- Department of Pharmaceutical Chemistry and Center for Tumor Biology and Immunology, Philipps-University Marburg , Hans-Meerwein-Straße 3, 35032 Marburg, Germany
| | - Cecilia Betti
- Research Group of Organic Chemistry, Departments of Chemistry and Bio-Engineering Sciences, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium
| | - Els Pardon
- VIB-VUB Center for Structural Biology, VIB , 1050 Brussels, Belgium.,Structural Biology Brussels, Vrije Universiteit Brussel , 1050 Brussels, Belgium
| | - Steven Ballet
- Research Group of Organic Chemistry, Departments of Chemistry and Bio-Engineering Sciences, Vrije Universiteit Brussel , Pleinlaan 2, 1050 Brussels, Belgium
| | - Niek van Hilten
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
| | - Jan Steyaert
- VIB-VUB Center for Structural Biology, VIB , 1050 Brussels, Belgium.,Structural Biology Brussels, Vrije Universiteit Brussel , 1050 Brussels, Belgium
| | - Wibke E Diederich
- Department of Pharmaceutical Chemistry and Center for Tumor Biology and Immunology, Philipps-University Marburg , Hans-Meerwein-Straße 3, 35032 Marburg, Germany
| | - Peter Kolb
- Department of Pharmaceutical Chemistry, Philipps-University Marburg , Marbacher Weg 6, 35032 Marburg, Germany
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54
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Visini R, Arús-Pous J, Awale M, Reymond JL. Virtual Exploration of the Ring Systems Chemical Universe. J Chem Inf Model 2017; 57:2707-2718. [PMID: 29019686 DOI: 10.1021/acs.jcim.7b00457] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Here, we explore the chemical space of all virtually possible organic molecules focusing on ring systems, which represent the cyclic cores of organic molecules obtained by removing all acyclic bonds and converting all remaining atoms to carbon. This approach circumvents the combinatorial explosion encountered when enumerating the molecules themselves. We report the chemical universe database GDB4c containing 916 130 ring systems up to four saturated or aromatic rings and maximum ring size of 14 atoms and GDB4c3D containing the corresponding 6 555 929 stereoisomers. Almost all (98.6%) of these ring systems are unknown and represent chiral 3D-shaped macrocycles containing small rings and quaternary centers reminiscent of polycyclic natural products. We envision that GDB4c can serve to select new ring systems from which to design analogs of such natural products. The database is available for download at www.gdb.unibe.ch together with interactive visualization and search tools as a resource for molecular design.
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Affiliation(s)
- Ricardo Visini
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Josep Arús-Pous
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Mahendra Awale
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
| | - Jean-Louis Reymond
- Department of Chemistry and Biochemistry, University of Berne , Freiestrasse 3, 3012 Berne, Switzerland
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55
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Monteleone S, Fuchs JE, Liedl KR. Molecular Connectivity Predefines Polypharmacology: Aliphatic Rings, Chirality, and sp 3 Centers Enhance Target Selectivity. Front Pharmacol 2017; 8:552. [PMID: 28894419 PMCID: PMC5581349 DOI: 10.3389/fphar.2017.00552] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 08/07/2017] [Indexed: 12/31/2022] Open
Abstract
Dark chemical matter compounds are small molecules that have been recently identified as highly potent and selective hits. For this reason, they constitute a promising class of possible candidates in the process of drug discovery and raise the interest of the scientific community. To this purpose, Wassermann et al. (2015) have described the application of 2D descriptors to characterize dark chemical matter. However, their definition was based on the number of reported positive assays rather than the number of known targets. As there might be multiple assays for one single target, the number of assays does not fully describe target selectivity. Here, we propose an alternative classification of active molecules that is based on the number of known targets. We cluster molecules in four classes: black, gray, and white compounds are active on one, two to four, and more than four targets respectively, whilst inactive compounds are found to be inactive in the considered assays. In this study, black and inactive compounds are found to have not only higher solubility, but also a higher number of chiral centers, sp3 carbon atoms and aliphatic rings. On the contrary, white compounds contain a higher number of double bonds and fused aromatic rings. Therefore, the design of a screening compound library should consider these molecular properties in order to achieve target selectivity or polypharmacology. Furthermore, analysis of four main target classes (GPCRs, kinases, proteases, and ion channels) shows that GPCR ligands are more selective than the other classes, as the number of black compounds is higher in this target superfamily. On the other side, ligands that hit kinases, proteases, and ion channels bind to GPCRs more likely than to other target classes. Consequently, depending on the target protein family, appropriate screening libraries can be designed in order to minimize the likelihood of unwanted side effects early in the drug discovery process. Additionally, synergistic effects may be obtained by library design toward polypharmacology.
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Affiliation(s)
| | | | - Klaus R. Liedl
- Institute of General, Inorganic and Theoretical Chemistry, Center of Molecular Biosciences, University of InnsbruckInnsbruck, Austria
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56
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Israels R, Maaß A, Hamaekers J. The octet rule in chemical space: generating virtual molecules. Mol Divers 2017; 21:769-778. [PMID: 28776208 DOI: 10.1007/s11030-017-9775-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 07/24/2017] [Indexed: 10/19/2022]
Abstract
We present a generator of virtual molecules that selects valid chemistry on the basis of the octet rule. Also, we introduce a mesomer group key that allows a fast detection of duplicates in the generated structures. Compared to existing approaches, our model is simpler and faster, generates new chemistry and avoids invalid chemistry. Its versatility is illustrated by the correct generation of molecules containing third-row elements and a surprisingly adept handling of complex boron chemistry. Without any empirical parameters, our model is designed to be valid also in unexplored regions of chemical space. One first unexpected finding is the high prevalence of dipolar structures among generated molecules.
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Affiliation(s)
- Rafel Israels
- Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
| | - Astrid Maaß
- Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI, Schloss Birlinghoven, 53754, Sankt Augustin, Germany
| | - Jan Hamaekers
- Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI, Schloss Birlinghoven, 53754, Sankt Augustin, Germany.
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57
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Affiliation(s)
- Xavier Barril
- a Facultat de Farmacia and Institut de Biomedicina (IBUB) , Universitat de Barcelona , Barcelona , Spain.,b Catalan Institution for Research and Advanced Studies (ICREA) , Barcelona , Spain
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58
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Pottel J, Moitessier N. Customizable Generation of Synthetically Accessible, Local Chemical Subspaces. J Chem Inf Model 2017; 57:454-467. [DOI: 10.1021/acs.jcim.6b00648] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Joshua Pottel
- Department of Chemistry, McGill University, 801
Sherbrooke Street W., Montréal, Québec, Canada H3A 0B8
| | - Nicolas Moitessier
- Department of Chemistry, McGill University, 801
Sherbrooke Street W., Montréal, Québec, Canada H3A 0B8
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59
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Segler MHS, Waller MP. Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction. Chemistry 2017; 23:5966-5971. [PMID: 28134452 DOI: 10.1002/chem.201605499] [Citation(s) in RCA: 227] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Indexed: 12/24/2022]
Abstract
Reaction prediction and retrosynthesis are the cornerstones of organic chemistry. Rule-based expert systems have been the most widespread approach to computationally solve these two related challenges to date. However, reaction rules often fail because they ignore the molecular context, which leads to reactivity conflicts. Herein, we report that deep neural networks can learn to resolve reactivity conflicts and to prioritize the most suitable transformation rules. We show that by training our model on 3.5 million reactions taken from the collective published knowledge of the entire discipline of chemistry, our model exhibits a top10-accuracy of 95 % in retrosynthesis and 97 % for reaction prediction on a validation set of almost 1 million reactions.
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Affiliation(s)
- Marwin H S Segler
- Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstr. 40, 48149, Münster, Germany
| | - Mark P Waller
- Organisch-Chemisches Institut and Center for Multiscale Theory and Computation, Westfälische Wilhelms-Universität Münster, Corrensstr. 40, 48149, Münster, Germany
- Department of Physics and International Center for Quantum and Molecular Structures, Shanghai University, Shangda Road 99, 200444, Shanghai, China
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60
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Podlewska S, Czarnecki WM, Kafel R, Bojarski AJ. Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization. J Chem Inf Model 2017; 57:133-147. [DOI: 10.1021/acs.jcim.6b00426] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Sabina Podlewska
- Department of Medicinal
Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, 31-343 Kraków, Poland
| | - Wojciech M. Czarnecki
- Faculty
of Mathematics and Computer Science, Jagiellonian University, 30-348 Kraków, Poland
| | - Rafał Kafel
- Department of Medicinal
Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, 31-343 Kraków, Poland
| | - Andrzej J. Bojarski
- Department of Medicinal
Chemistry, Institute of Pharmacology, Polish Academy of Sciences, Smętna 12, 31-343 Kraków, Poland
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61
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Segler MHS, Waller MP. Modelling Chemical Reasoning to Predict and Invent Reactions. Chemistry 2017; 23:6118-6128. [DOI: 10.1002/chem.201604556] [Citation(s) in RCA: 109] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2016] [Indexed: 01/30/2023]
Affiliation(s)
- Marwin H. S. Segler
- Institute of Organic Chemistry and Center for Multiscale Theory and Computation; Westfälische Wilhelms-Universität Münster; Corrensstraße 40 48149 Münster Germany
| | - Mark P. Waller
- Institute of Organic Chemistry and Center for Multiscale Theory and Computation; Westfälische Wilhelms-Universität Münster; Corrensstraße 40 48149 Münster Germany
- Department of Physics and International Centre for Quantum and Molecular Structures; Shanghai University; Shangda Road 99 200444 Shanghai P.R. China
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62
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Tolmachev A, Bogolubsky AV, Pipko SE, Grishchenko AV, Ushakov DV, Zhemera AV, Viniychuk OO, Konovets AI, Zaporozhets OA, Mykhailiuk PK, Moroz YS. Expanding Synthesizable Space of Disubstituted 1,2,4-Oxadiazoles. ACS COMBINATORIAL SCIENCE 2016; 18:616-624. [PMID: 27548754 DOI: 10.1021/acscombsci.6b00103] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
One-pot synthesis of 3,5-disubstituted 1,2,4-oxadiazoles from carboxylic acids and nitriles was optimized to parallel chemistry. The method was validated on a 141 member library; the desired products were recovered with a high success rate and in moderate yields. Practical application of the approach was demonstrated in the synthesis of bioactive compound pifexole and agonists of free fatty acid receptor 1. A library of 4 948 100 synthesizable drug-like 3,5-disubstituted 1,2,4-oxadiazoles was enumerated based on the method and available validated reagents.
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Affiliation(s)
- Andrey Tolmachev
- Enamine Ltd., 78 Chervonotkatska
Street, Kyiv, 02094, Ukraine
- ChemBioCenter, Kyiv National Taras Shevchenko University, 61 Chervonotkatska Street, Kyiv, 02094, Ukraine
| | | | - Sergey E. Pipko
- ChemBioCenter, Kyiv National Taras Shevchenko University, 61 Chervonotkatska Street, Kyiv, 02094, Ukraine
- UkrOrgSyntez Ltd. (UORSY), 29 Schorsa
Street, Kyiv, 01133, Ukraine
| | | | | | | | | | - Anzhelika I. Konovets
- Enamine Ltd., 78 Chervonotkatska
Street, Kyiv, 02094, Ukraine
- The
Institute of High Technologies, Kyiv National Taras Shevchenko University, 4 Glushkov Street, Building 5, Kyiv, 03187, Ukraine
| | - Olga A. Zaporozhets
- Department
of Chemistry, Kyiv National Taras Shevchenko University, 64 Volodymyrska
Street, Kyiv, 01601, Ukraine
| | - Pavel K. Mykhailiuk
- Enamine Ltd., 78 Chervonotkatska
Street, Kyiv, 02094, Ukraine
- Department
of Chemistry, Kyiv National Taras Shevchenko University, 64 Volodymyrska
Street, Kyiv, 01601, Ukraine
| | - Yurii S. Moroz
- ChemBioCenter, Kyiv National Taras Shevchenko University, 61 Chervonotkatska Street, Kyiv, 02094, Ukraine
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63
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Zoete V, Daina A, Bovigny C, Michielin O. SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening. J Chem Inf Model 2016; 56:1399-404. [PMID: 27391578 DOI: 10.1021/acs.jcim.6b00174] [Citation(s) in RCA: 182] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small molecules. Screenable compounds include drugs, bioactive and commercial molecules, as well as 205 million of virtual compounds readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field. SwissSimilarity is accessible free of charge or login at http://www.swisssimilarity.ch .
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Affiliation(s)
- Vincent Zoete
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Antoine Daina
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Christophe Bovigny
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland
| | - Olivier Michielin
- SIB Swiss Institute of Bioinformatics, Quartier Sorge, Bâtiment Génopode , CH-1015 Lausanne, Switzerland.,Ludwig Institute for Cancer Research, Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland.,Department of Oncology, University of Lausanne and Centre Hospitalier Universitaire Vaudois , CH-1011 Lausanne, Switzerland
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64
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Nicolaou CA, Watson IA, Hu H, Wang J. The Proximal Lilly Collection: Mapping, Exploring and Exploiting Feasible Chemical Space. J Chem Inf Model 2016; 56:1253-66. [DOI: 10.1021/acs.jcim.6b00173] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Christos A. Nicolaou
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Ian A. Watson
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Hong Hu
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
| | - Jibo Wang
- Discovery Chemistry, Lilly
Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana 46285, United States
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