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Kumar N, Acharya V. Advances in machine intelligence-driven virtual screening approaches for big-data. Med Res Rev 2024; 44:939-974. [PMID: 38129992 DOI: 10.1002/med.21995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 07/15/2023] [Accepted: 10/29/2023] [Indexed: 12/23/2023]
Abstract
Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.
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Affiliation(s)
- Neeraj Kumar
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
| | - Vishal Acharya
- Artificial Intelligence for Computational Biology Lab (AICoB), Biotechnology Division, CSIR-Institute of Himalayan Bioresource Technology, Palampur, Himachal Pradesh, India
- Academy of Scientific and Innovative Research, Ghaziabad, India
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2
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Garisetti V, Dhanabalan AK, Dasararaju G. Discovery of potential TAAR1 agonist targeting neurological and psychiatric disorders: An in silico approach. Int J Biol Macromol 2024; 264:130528. [PMID: 38431013 DOI: 10.1016/j.ijbiomac.2024.130528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
Trace amine-associated receptor 1 (TAAR1) is a G-protein-coupled receptor which is primarily expressed in the brain. It is activated by trace amines which play a role in regulating neurotransmitters like dopamine, serotonin and norepinephrine. TAAR1 agonists have potential applications in the treatment of neurological and psychiatric disorders, especially schizophrenia. In this study, we have used a structure-based virtual screening approach to identify potential TAAR1 agonist(s). We have modelled the structure of TAAR1 and predicted the binding pocket. Further, molecular docking of a few well-known antipsychotic drugs was carried out with TAAR1 model, which showed key interactions with the binding pocket. From screening a library of 5 million compounds from the Enamine REAL Database using structure-based virtual screening method, we shortlisted 12 compounds which showed good docking score, glide energy and interactions with the key residues. One lead compound (Z31378290) was finally selected. The lead compound showed promising binding affinity and stable interactions with TAAR1 during molecular dynamics simulations and demonstrated better van der Waals and binding energy than the known agonist, ulotaront. Our findings suggest that the lead compound may serve as a potential TAAR1 agonist, offering a promising avenue for the development of new therapies for neurological and psychiatric disorders.
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Affiliation(s)
- Vasavi Garisetti
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamil Nadu, India
| | - Anantha Krishnan Dhanabalan
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamil Nadu, India
| | - Gayathri Dasararaju
- Centre of Advanced Study in Crystallography and Biophysics, University of Madras, Guindy Campus, Chennai 600025, Tamil Nadu, India.
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3
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Naumchyk V, Andriashvili VA, Radchenko DS, Dudenko D, Moroz YS, Tolmachev AA, Zhersh S, Grygorenko OO. S NAr or Sulfonylation? Chemoselective Amination of Halo(het)arene Sulfonyl Halides for Synthetic Applications and Ultralarge Compound Library Design. J Org Chem 2024; 89:3161-3183. [PMID: 38383160 DOI: 10.1021/acs.joc.3c02636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
The chemoselectivity of halo(het)arene sulfonyl halide aminations is studied thoroughly under parallel synthesis conditions, and the scope and limitations of the method are established. It is shown that SNAr-reactive sulfonyl halides typically undergo sulfonamide synthesis during the first step; the second amination is also possible provided that the SNAr-active center is sufficiently reactive. On the contrary, sulfonyl fluorides bearing an arylating moiety undergo selective transformation at the latter reactive center under proper control. Further sulfur-fluoride exchange (SuFEx) is also possible, which can be especially valuable for some sulfonyl halide classes. The developed two-step parallel double amination protocol provides access to a 6.67-billion compound synthetically tractable REAL-type chemical space (76% expected synthesis success rate).
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Affiliation(s)
- Vasyl Naumchyk
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Vladyslav A Andriashvili
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | | | - Dmytro Dudenko
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
| | - Yurii S Moroz
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
- Chemspace, Winston Churchill Street 85, Kyïv 02094, Ukraine
| | - Andrey A Tolmachev
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Serhii Zhersh
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Oleksandr O Grygorenko
- Enamine Ltd., Winston Churchill Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyïv 01601, Ukraine
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4
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Kirchoff KE, Wellnitz J, Hochuli JE, Maxfield T, Popov KI, Gomez S, Tropsha A. Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search. ADVANCES IN INFORMATION RETRIEVAL : ... EUROPEAN CONFERENCE ON IR RESEARCH, ECIR ... PROCEEDINGS. EUROPEAN CONFERENCE ON IR RESEARCH 2024; 14609:34-49. [PMID: 38585224 PMCID: PMC10998712 DOI: 10.1007/978-3-031-56060-6_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Nearest neighbor-based similarity searching is a common task in chemistry, with notable use cases in drug discovery. Yet, some of the most commonly used approaches for this task still leverage a brute-force approach. In practice this can be computationally costly and overly time-consuming, due in part to the sheer size of modern chemical databases. Previous computational advancements for this task have generally relied on improvements to hardware or dataset-specific tricks that lack generalizability. Approaches that leverage lower-complexity searching algorithms remain relatively underexplored. However, many of these algorithms are approximate solutions and/or struggle with typical high-dimensional chemical embeddings. Here we evaluate whether a combination of low-dimensional chemical embeddings and a k-d tree data structure can achieve fast nearest neighbor queries while maintaining performance on standard chemical similarity search benchmarks. We examine different dimensionality reductions of standard chemical embeddings as well as a learned, structurally-aware embedding-SmallSA-for this task. With this framework, searches on over one billion chemicals execute in less than a second on a single CPU core, five orders of magnitude faster than the brute-force approach. We also demonstrate that SmallSA achieves competitive performance on chemical similarity benchmarks.
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Affiliation(s)
| | | | | | | | | | - Shawn Gomez
- Department of Pharmacology, UNC Chapel Hill
- Joint Department of Biomedical Engineering at UNC Chapel Hill and NCSU
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5
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Kotev M, Diaz Gonzalez C. Molecular Dynamics and Other HPC Simulations for Drug Discovery. Methods Mol Biol 2024; 2716:265-291. [PMID: 37702944 DOI: 10.1007/978-1-0716-3449-3_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
High performance computing (HPC) is taking an increasingly important place in drug discovery. It makes possible the simulation of complex biochemical systems with high precision in a short time, thanks to the use of sophisticated algorithms. It promotes the advancement of knowledge in fields that are inaccessible or difficult to access through experimentation and it contributes to accelerating the discovery of drugs for unmet medical needs while reducing costs. Herein, we report how computational performance has evolved over the past years, and then we detail three domains where HPC is essential. Molecular dynamics (MD) is commonly used to explore the flexibility of proteins, thus generating a better understanding of different possible approaches to modulate their activity. Modeling and simulation of biopolymer complexes enables the study of protein-protein interactions (PPI) in healthy and disease states, thus helping the identification of targets of pharmacological interest. Virtual screening (VS) also benefits from HPC to predict in a short time, among millions or billions of virtual chemical compounds, the best potential ligands that will be tested in relevant assays to start a rational drug design process.
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Affiliation(s)
- Martin Kotev
- Evotec SE, Integrated Drug Discovery, Molecular Architects, Campus Curie, Toulouse, France
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Kunnakkattu IR, Choudhary P, Pravda L, Nadzirin N, Smart OS, Yuan Q, Anyango S, Nair S, Varadi M, Velankar S. PDBe CCDUtils: an RDKit-based toolkit for handling and analysing small molecules in the Protein Data Bank. J Cheminform 2023; 15:117. [PMID: 38042830 PMCID: PMC10693035 DOI: 10.1186/s13321-023-00786-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 11/17/2023] [Indexed: 12/04/2023] Open
Abstract
While the Protein Data Bank (PDB) contains a wealth of structural information on ligands bound to macromolecules, their analysis can be challenging due to the large amount and diversity of data. Here, we present PDBe CCDUtils, a versatile toolkit for processing and analysing small molecules from the PDB in PDBx/mmCIF format. PDBe CCDUtils provides streamlined access to all the metadata for small molecules in the PDB and offers a set of convenient methods to compute various properties using RDKit, such as 2D depictions, 3D conformers, physicochemical properties, scaffolds, common fragments, and cross-references to small molecule databases using UniChem. The toolkit also provides methods for identifying all the covalently attached chemical components in a macromolecular structure and calculating similarity among small molecules. By providing a broad range of functionality, PDBe CCDUtils caters to the needs of researchers in cheminformatics, structural biology, bioinformatics and computational chemistry.
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Affiliation(s)
- Ibrahim Roshan Kunnakkattu
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Preeti Choudhary
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Lukas Pravda
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Nurul Nadzirin
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Oliver S Smart
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Qi Yuan
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Stephen Anyango
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sreenath Nair
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Mihaly Varadi
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Sameer Velankar
- Protein Data Bank in Europe, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK.
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7
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Xie L, Xie L. Elucidation of genome-wide understudied proteins targeted by PROTAC-induced degradation using interpretable machine learning. PLoS Comput Biol 2023; 19:e1010974. [PMID: 37590332 PMCID: PMC10464998 DOI: 10.1371/journal.pcbi.1010974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 08/29/2023] [Accepted: 07/27/2023] [Indexed: 08/19/2023] Open
Abstract
Proteolysis-targeting chimeras (PROTACs) are hetero-bifunctional molecules that induce the degradation of target proteins by recruiting an E3 ligase. PROTACs have the potential to inactivate disease-related genes that are considered undruggable by small molecules, making them a promising therapy for the treatment of incurable diseases. However, only a few hundred proteins have been experimentally tested for their amenability to PROTACs, and it remains unclear which other proteins in the entire human genome can be targeted by PROTACs. In this study, we have developed PrePROTAC, an interpretable machine learning model based on a transformer-based protein sequence descriptor and random forest classification. PrePROTAC predicts genome-wide targets that can be degraded by CRBN, one of the E3 ligases. In the benchmark studies, PrePROTAC achieved a ROC-AUC of 0.81, an average precision of 0.84, and over 40% sensitivity at a false positive rate of 0.05. When evaluated by an external test set which comprised proteins from different structural folds than those in the training set, the performance of PrePROTAC did not drop significantly, indicating its generalizability. Furthermore, we developed an embedding SHapley Additive exPlanations (eSHAP) method, which extends conventional SHAP analysis for original features to an embedding space through in silico mutagenesis. This method allowed us to identify key residues in the protein structure that play critical roles in PROTAC activity. The identified key residues were consistent with existing knowledge. Using PrePROTAC, we identified over 600 novel understudied proteins that are potentially degradable by CRBN and proposed PROTAC compounds for three novel drug targets associated with Alzheimer's disease.
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Affiliation(s)
- Li Xie
- Department of Computer Science, Hunter College, The City University of New York, New York City, New York, United States of America
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York City, New York, United States of America
- Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York City, New York, United States of America
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Feil Family Brain & Mind Research Institute, Weill Cornell Medicine, Cornell University, New York City, New York, United States of America
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8
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Torng W, Biancofiore I, Oehler S, Xu J, Xu J, Watson I, Masina B, Prati L, Favalli N, Bassi G, Neri D, Cazzamalli S, Feng JA. Deep Learning Approach for the Discovery of Tumor-Targeting Small Organic Ligands from DNA-Encoded Chemical Libraries. ACS OMEGA 2023; 8:25090-25100. [PMID: 37483198 PMCID: PMC10357458 DOI: 10.1021/acsomega.3c01775] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023]
Abstract
DNA-Encoded Chemical Libraries (DELs) have emerged as efficient and cost-effective ligand discovery tools, which enable the generation of protein-ligand interaction data of unprecedented size. In this article, we present an approach that combines DEL screening and instance-level deep learning modeling to identify tumor-targeting ligands against carbonic anhydrase IX (CAIX), a clinically validated marker of hypoxia and clear cell renal cell carcinoma. We present a new ligand identification and hit-to-lead strategy driven by machine learning models trained on DELs, which expand the scope of DEL-derived chemical motifs. CAIX-screening datasets obtained from three different DELs were used to train machine learning models for generating novel hits, dissimilar to elements present in the original DELs. Out of the 152 novel potential hits that were identified with our approach and screened in an in vitro enzymatic inhibition assay, 70% displayed submicromolar activities (IC50 < 1 μM). To generate lead compounds that are functionalized with anticancer payloads, analogues of top hits were prioritized for synthesis based on the predicted CAIX affinity and synthetic feasibility. Three lead candidates showed accumulation on the surface of CAIX-expressing tumor cells in cellular binding assays. The best compound displayed an in vitro KD of 5.7 nM and selectively targeted tumors in mice bearing human renal cell carcinoma lesions. Our results demonstrate the synergy between DEL and machine learning for the identification of novel hits and for the successful translation of lead candidates for in vivo targeting applications.
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Affiliation(s)
- Wen Torng
- Google
Research, 1600 Amphitheatre
Parkway, Mountain View, California 94043, United States
| | | | - Sebastian Oehler
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
| | - Jin Xu
- Google
Research, 1600 Amphitheatre
Parkway, Mountain View, California 94043, United States
| | - Jessica Xu
- Google
Research, 1600 Amphitheatre
Parkway, Mountain View, California 94043, United States
| | - Ian Watson
- Google
Research, 1600 Amphitheatre
Parkway, Mountain View, California 94043, United States
| | - Brenno Masina
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
| | - Luca Prati
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
| | - Nicholas Favalli
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
| | - Gabriele Bassi
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
| | - Dario Neri
- R&D
Department, Philochem AG, Otelfingen, Zürich 8112, Switzerland
- Philogen
S.p.A., Siena 53100, Italy
- Department
of Chemistry and Applied Biosciences, Swiss
Federal Institute of Technology (ETH Zürich), Zürich 8092, Switzerland
| | | | - Jianwen A. Feng
- Google
Research, 1600 Amphitheatre
Parkway, Mountain View, California 94043, United States
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9
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Holovach S, Melnykov KP, Poroshyn I, Iminov RT, Dudenko D, Kondratov I, Levin M, Grygorenko OO. C-C Coupling through Nitrogen Deletion: Application to Library Synthesis. Chemistry 2023; 29:e202203470. [PMID: 36445790 DOI: 10.1002/chem.202203470] [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: 11/08/2022] [Revised: 11/26/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022]
Abstract
A protocol for parallel C(sp3 )-C(sp3 ) coupling of (hetero)aromatic aldehydes and (hetero)arylmethyl amines based on a reductive amination - "nitrogen deletion" reaction sequence has been developed. After preliminary validation experiments, an illustrative compound library of 25 members was prepared with 76 % synthetic efficiency. The estimated chemical space accessible by the proposed approach covers almost 600 000 representatives that are scarcely represented in current compound databases.
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Affiliation(s)
- Serhii Holovach
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Kostiantyn P Melnykov
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine.,Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine
| | - Illia Poroshyn
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Rustam T Iminov
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine
| | - Dmytro Dudenko
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine.,Chemspace, Chervonotkatska Street 85, Kyiv, 02094, Ukraine
| | - Ivan Kondratov
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine.,V. P. Kukhar Institute of Bioorganic Chemistry and Petrochemistry, National Academy of Sciences of Ukraine, Akademik Kukhar Street 1, Kyiv, 02094, Ukraine
| | - Mark Levin
- Department of Chemistry, University of Chicago, 5735 S Ellis Ave, Chicago, IL 60637, USA
| | - Oleksandr O Grygorenko
- Enamine Ltd., Chervonotkatska Street 78, Kyiv, 02094, Ukraine.,Taras Shevchenko National University of Kyiv, Volodymyrska Street 60, Kyiv, 01601, Ukraine
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10
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McMillan AE, Wu WWX, Nichols PL, Wanner BM, Bode JW. A vending machine for drug-like molecules - automated synthesis of virtual screening hits. Chem Sci 2022; 13:14292-14299. [PMID: 36545137 PMCID: PMC9749103 DOI: 10.1039/d2sc05182f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022] Open
Abstract
As a result of high false positive rates in virtual screening campaigns, prospective hits must be synthesised for validation. When done manually, this is a time consuming and laborious process. Large "on-demand" virtual libraries (>7 × 1012 members), suitable for preparation using capsule-based automated synthesis and commercial building blocks, were evaluated to determine their structural novelty. One sub-library, constructed from iSnAP capsules, aldehydes and amines, contains unique scaffolds with drug-like physicochemical properties. Virtual screening hits from this iSnAP library were prepared in an automated fashion for evaluation against Aedes aegypti and Phytophthora infestans. In comparison to manual workflows, this approach provided a 10-fold improvement in user efficiency. A streamlined method of relative stereochemical assignment was also devised to augment the rapid synthesis. User efficiency was further improved to 100-fold by downscaling and parallelising capsule-based chemistry on 96-well plates equipped with filter bases. This work demonstrates that automated synthesis consoles can enable the rapid and reliable preparation of attractive virtual screening hits from large virtual libraries.
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Affiliation(s)
- Angus E. McMillan
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Wilson W. X. Wu
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
| | - Paula L. Nichols
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland,Synple Chem AGKemptpark 18Kemptthal 8310Switzerland
| | | | - Jeffrey W. Bode
- Laboratory for Organic Chemistry, Department of Chemistry and Applied Biosciences, ETH ZürichZürich 8093Switzerland
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11
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Kondratov IS, Moroz YS, Grygorenko OO, Tolmachev AA. The Ukrainian Factor in Early-Stage Drug Discovery in the Context of Russian Invasion: The Case of Enamine Ltd. ACS Med Chem Lett 2022; 13:992-996. [DOI: 10.1021/acsmedchemlett.2c00211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Affiliation(s)
- Ivan S. Kondratov
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- V.P. Kukhar Institute of Bioorganic Chemistry & Petrochemistry, National Academy of Sciences of Ukraine, Murmanska Street 1, Kyïv 02660, Ukraine
| | - Yurii S. Moroz
- Chemspace (www.chem-space.com), Chervonotkatska Street 85, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Oleksandr O. Grygorenko
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
| | - Andrey A. Tolmachev
- Enamine Ltd (www.enamine.net), Chervonotkatska Street 78, Kyïv 02094, Ukraine
- Taras Shevchenko National University of Kyïv, Volodymyrska Street 60, Kyïv 01601, Ukraine
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