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A New Chapter in Scientific Collaboration


Enamine Scientific Research Institute (Enamine SRI) is a newly established non-profit organization founded by Enamine in 2025. Its mission is to foster scientific research and innovation in the fields of chemistry, biology, computer science, and early-stage drug discovery in Ukraine. The institute also aims to support the professional growth of students and young scientists.

Enamine Scientific Research Institute logo

Enamine SRI functions as an independent structure within the Enamine ecosystem, serving as a hub for collaboration and knowledge exchange with research institutions worldwide. The Institute is fully supported by Enamine, which provides access to laboratory space, modern equipment, and all necessary infrastructure.

The Institute is led by Prof. Serhii Ryabukhin, Dr., a renowned Ukrainian chemist and expert in organic and medicinal chemistry.

Enamine is a scientifically driven company. Through the scientific approach, we have been routinely solving the research goals of our clients and collaborators. Today, we are happy to launch a separate non-profit institution that will be fully focused on the scientific aspects of modern science. We at Enamine are proud that after more than three decades of our history, we could step further and create a new research institution. We are sure it will help solve global scientific challenges, – commented Dr. Andrey Tolmachev, Founder and Owner of Enamine.

In the era of open science and global collaboration, Enamine SRI is designed to serve as a bridge between commercial innovation and academic research. The Institute welcomes partnerships and diverse scientific initiatives from around the world.

 

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  1. SpaceGrow: efficient shape-based virtual screening of billion-sized combinatorial fragment spaces Hönig S.M.N.; Flachsenberg F.; Ehrt C.; Neumann A.; Schmidt R.; Lemmen C.; Rarey M. J Comput Aided Mol Des 2024, 38 (1), 13. DOI: 10.1007/s10822-024-00551-7
  2. The HZB F2X-Facility—An Efficient Crystallographic Fragment Screening Platform Barthel T.; Benz L.; Basler Y.; Crosskey T.; Dillmann A.; Förster R.; Fröling P.; Dieguez C. G.; Gless C.; Hauß T.; Hellmig M.; Jänisch L.; James D.; Lennartz F.; Mijatovic J.; Oelker M.; Scanlan J. W.; Weber G.; Wollenhaupt J.; Mueller U.; Dobbek H.; Wahl M. C.; Weiss M. S. Applied Research 2024, in press. DOI: 10.1002/appl.202400110
  3. Navigating Chemical Space Tarcsay A.; Volford A.; Buttrick J.; Christopherson J.-C.; Erdos M.; Szabo Z.B. Computational Drug Discovery: Methods and Applications 2024, 337-363. URL: 10.1002/9783527840748.ch15
  4. Generative artificial intelligence for small molecule drug design Kanakala G.C.; Devata S.; Chatterjee P.; Priyakumar U.D. Curr Opin Biotechnol 2024, 89, 103175. DOI: 10.1016/j.copbio.2024.103175
  5. Application scenario-oriented molecule generation platform developed for drug discovery Zheng L.; Shi F.; Peng C.; Xu M.; Fan F.; Li Y.; Zhang L.; Du J.; Wang Z.; Lin Z.; Sun Y.; Deng C.; Duan X.; Wei L.; Zhao C.; Fang L.; Zhang P.; Ma S.; Lai L.; Yang M. Methods 2024, 222, 112-121. DOI: 10.1016/j.ymeth.2023.12.009
  6. A data science roadmap for open science organizations engaged in early-stage drug discovery Edfeldt K.; Edwards A. M.; Engkvist O.; Gunther J.; Hartley M.; Hulcoop D. G.; Leach A. R.; Marsden B. D.; Menge A.; Misquitta L.; Muller S.; Owen D. R.; Schutt K. T.; Skelton N.; Steffen A.; Tropsha A.; Vernet E.; Wang Y.; Wellnitz J.; Willson T. M.; Clevert D. A.; Haibe-Kains B.; Schiavone L. H.; Schapira M. Nat Commun 2024, 15 (1), 5640. DOI: 10.1038/s41467-024-49777-x
  7. Identification of Novel Potent NSD2-PWWP1 Ligands Using Structure-Based Design and Computational Approaches Carlino L.; Astles P. C.; Ackroyd B.; Ahmed A.; Chan C.; Collie G. W.; Dale I. L.; O'Donovan D. H.; Fawcett C.; di Fruscia P.; Gohlke A.; Guo X.; Hao-Ru Hsu J.; Kaplan B.; Milbradt A. G.; Northall S.; Petrovic D.; Rivers E. L.; Underwood E.; Webb A. J Med Chem 2024, 67 (11), 8962-8987. DOI: 10.1021/acs.jmedchem.4c00215
  8. Hit me with your best shot: Integrated hit discovery for the next generation of drug targets Ashraf S.N.; Blackwell J.H.; Holdgate G.A.; Lucas S.C.C.; Solovyeva A.; Storer R.I.; Whitehurst B.C. Drug Discov Today 2024, 29 (10), 104143. DOI: 10.1016/j.drudis.2024.104143
  9. E-pharmacophore and deep learning based high throughput virtual screening for identification of CDPK1 inhibitors of Cryptosporidium parvum Asmare M.M.; Yun S.-I. Comput Biol Chem 2024, 112, 108172. DOI: 10.1016/j.compbiolchem.2024.108172
  10. Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery Manen-Freixa L.; Antolin A.A. Expert Opin Drug Discov 2024, 19 (9), 1043-1069. DOI: 10.1080/17460441.2024.2376643
  11. Enumerable Libraries and Accessible Chemical Space in Drug Discovery Knehans T.; Boyles N.A.; Bos P.H. Computational Drug Discovery: Methods and Applications 2024, 315-336. DOI: 10.1002/9783527840748.ch14
  12. Target-Aware Drug Activity Model: A Deep Learning Approach to Virtual HTS Czaplak S.; Frączek T.; Ambrogi F.; Kmicikiewicz M.; Wichard J.; Karawajczyk A. Artificial Neural Networks and Machine Learning (ICANN 2024) 2024, 15025, 73-87. DOI: 10.1007/978-3-031-72359-9_6
  13. Streamlining Large Chemical Library Docking with Artificial Intelligence: the PyRMD2Dock Approach Roggia M.; Natale B.; Amendola G.; Di Maro S.; Cosconati S. J Chem Inf Model 2024, 64 (7), 2143-2149. DOI: 10.1021/acs.jcim.3c00647
  14. Transformers for Molecular Property Prediction: Lessons Learned from the Past Five Years Sultan A.; Sieg J.; Mathea M.; Volkamer A. J Chem Inf Model 2024, 64 (16), 6259-6280. DOI: 10.1021/acs.jcim.4c00747
  15. Exploring Chemical Spaces in the Billion Range: Is Docking a Computational Alternative to DNA-Encoded Libraries? Mihalovits L.M.; Szalai T.V.; Bajusz D.; Keserű G.M. J Chem Inf Model 2024, in press. DOI: 10.1021/acs.jcim.4c00803
  16. High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model Vasan A.; Gokdemir O.; Brace A.; Ramanathan A.; Brettin T.; Stevens R.; Vishwanath V. 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) 2024, 571-580. DOI: 10.1109/IPDPSW63119.2024.00114
  17. Sulfamide instead of urea in Biginelli reaction: from black box to reality Lyapunov A.Y.; Tarnovskiy A.V.; Boron S.Y.; Rusanov E.B.; Grabchuk G.P.; Volochnyuk D.M.; Ryabukhin S.V. Org Chem Front 2024, 11 (8), 2155-2160. DOI: 10.1039/d3qo01926h
  18. Ultra-large scale virtual screening identifies a small molecule inhibitor of the Wnt transporter Wntless Yu J.; Liao P.-J.; Keller T.H.; Cherian J.; Virshup D.M.; Xu W. iScience 2024, 27 (8), 110454. DOI: 10.1016/j.isci.2024.110454
  19. Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening Cao Z.; Sciabola S.; Wang Y. J Chem Inf Model 2024, 64 (6), 1882-1891. DOI: 10.1021/acs.jcim.3c01938
  20. Structure-based virtual screening of vast chemical space as a starting point for drug discovery Carlsson J.; Luttens A. Curr Opin Struct Biol 2024, 87, 102829. DOI: 10.1016/j.sbi.2024.102829
  21. In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot An Y.; Lim J.; Glavatskikh M.; Wang X.; Norris-Drouin J.; Hardy P.B.; Leisner T.M.; Pearce K.H.; Kireev D. Nat Commun 2024, 15 (1), 5564. DOI: 10.1038/s41467-024-49892-9
  22. Discovery of the small molecular inhibitors against sclerostin loop3 as potential anti-osteoporosis agents by structural based virtual screening and molecular design Yu S.; Huang W.; Zhang H.; Guo Y.; Zhang B.; Zhang G.; Lei J. Eur J Med Chem 2024, 271, 116414. DOI: 10.1016/j.ejmech.2024.116414
  23. Perspectives on current approaches to virtual screening in drug discovery Muegge I.; Bentzien J.; Ge Y. Expert Opin Drug Discov 2024, 19 (10), 1173-1183. DOI: 10.1080/17460441.2024.2390511
  24. Docking and other computing tools in drug design against SARS-CoV-2 Sulimov A.V.; Ilin I.S.; Tashchilova A.S.; Kondakova O.A.; Kutov D.C.; Sulimov V.B. SAR QSAR Environ Res 2024, 35 (2), 91-136. DOI: 10.1080/1062936X.2024.2306336
  25. The Pan-Canadian Chemical Library: A Mechanism to Open Academic Chemistry to High-Throughput Virtual Screening Bedart C.; Shimokura G.; West F.G.; Wood T.E.; Batey R.A.; Irwin J.J.; Schapira M. Sci Data 2024, 11 (1), 597. DOI: 10.1038/s41597-024-03443-5
  26. The IMS Library: from IN-Stock to Virtual Djikic-Stojsic T.; Bret G.; Blond G.; Girard N.; Le Guen C.; Marsol C.; Schmitt M.; Schneider S.; Bihel F.; Bonnet D.; Gulea M.; Kellenberger E. ChemMedChem 2024, 19 (20), e202400381. DOI: 10.1002/cmdc.202400381
  27. Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery Shen L.; Fang J.; Liu L.; Yang F.; Jenkins J.L.; Kutchukian P.S.; Wang H. J Cheminform 2024, 16 (1), 33. DOI: 10.1186/s13321-024-00829-w
  28. Structure-Based Ultra-Large Virtual Screenings Gorgulla C. Computational Drug Discovery: Methods and Applications: Volumes 1-2 2024, 441-470. URL: 10.1002/9783527840748.ch19
  29. ACEGEN: Reinforcement Learning of Generative Chemical Agents for Drug Discovery Bou A.; Thomas M.; Dittert S.; Navarro C.; Majewski M.; Wang Y.; Patel S.; Tresadern G.; Ahmad M.; Moens V.; Sherman W.; Sciabola S.; De Fabritiis G. J Chem Inf Model 2024, 64 (15), 5900-5911. DOI: 10.1021/acs.jcim.4c00895
  30. Shape-Aware Synthon Search (SASS) for Virtual Screening of Synthon-Based Chemical Spaces Cheng C.; Beroza P. J Chem Inf Model 2024, 64 (4), 1251-1260. DOI: 10.1021/acs.jcim.3c01865
  31. An artificial intelligence accelerated virtual screening platform for drug discovery Zhou G.; Rusnac D. V.; Park H.; Canzani D.; Nguyen H. M.; Stewart L.; Bush M. F.; Nguyen P. T.; Wulff H.; Yarov-Yarovoy V.; Zheng N.; DiMaio F. Nat Commun 2024, 15 (1), 7761. DOI: 10.1038/s41467-024-52061-7
  32. In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations Gutkin E.; Gusev F.; Gentile F.; Ban F.; Koby S.B.; Narangoda C.; Isayev O.; Cherkasov A.; Kurnikova M.G. Chem Sci 2024, 15 (23), 8800-8812. DOI: 10.1039/d3sc06880c
  33. Enhanced Calculation of Property Distributions in Chemical Fragment Spaces Lübbers J.; Lessel U.; Rarey M. J Chem Inf Model 2024, 64 (6), 2008-2020. DOI: 10.1021/acs.jcim.4c00147
  34. A time-efficient computational binding affinity estimation protocol with utilization of limited experimental data: A case study for adenosine receptor Cho I.; Moon S.; Cho K.-H. Bulletin of the Korean Chemical Society 2024, 45 (9), 778-787. DOI: 10.1002/bkcs.12890
  35. Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS) Pal S.; Nare Z.; Rao V.A.; Smith B.O.; Morrison I.; Fitzgerald E.A.; Scott A.; Bingham M.J.; Pesnot T. ChemMedChem 2024, 19 (6), e202300590. DOI: 10.1002/cmdc.202300590
  36. DFRscore: Deep Learning-Based Scoring of Synthetic Complexity with Drug-Focused Retrosynthetic Analysis for High-Throughput Virtual Screening Kim H.; Lee K.; Kim C.; Lim J.; Kim W.Y. J Chem Inf Model 2024, 64 (7), 2432-2444. DOI: 10.1021/acs.jcim.3c01134
  37. Subpocket Similarity-Based Hit Identification for Challenging Targets: Application to the WDR Domain of LRRK2 Eguida M.; Bret G.; Sindt F.; Li F.; Chau I.; Ackloo S.; Arrowsmith C.; Bolotokova A.; Ghiabi P.; Gibson E.; Halabelian L.; Houliston S.; Harding R.J.; Hutchinson A.; Loppnau P.; Perveen S.; Seitova A.; Zeng H.; Schapira M.; Rognan D. J Chem Inf Model 2024, 64 (13), 5344-5355. DOI: 10.1021/acs.jcim.4c00601
  38. A Sober Look at LLMs for Material Discovery: Are They Actually Good for Bayesian Optimization Over Molecules? Kristiadi A.; Strieth-Kalthoff F.; Skreta M.; Poupart P.; Aspuru-Guzik A.; Pleiss G. Proceedings of the 41st International Conference on Machine Learning 2024, 235, 25603-25622. URL: PMLR
  39. Quantum-Informed Molecular Representation Learning Enhancing ADMET Property Prediction Kim J.; Chang W.; Ji H.; Joung I. J Chem Inf Model 2024, 64 (13), 5028-5040. DOI: 10.1021/acs.jcim.4c00772
  40. Fragment-based discovery of new potential DNMT1 inhibitors integrating multiple pharmacophore modeling, 3D-QSAR, virtual screening, molecular docking, ADME, and molecular dynamics simulation approaches Lanka G.; Banerjee S.; Adhikari N.; Ghosh B. Mol Divers 2024, in press. DOI: 10.1007/s11030-024-10837-5
  41. AIDDISON: Empowering Drug Discovery with AI/ML and CADD Tools in a Secure, Web-Based SaaS Platform Rusinko A.; Rezaei M.; Friedrich L.; Buchstaller H.-P.; Kuhn D.; Ghogare A. J Chem Inf Model 2024, 64 (1), 3-8. DOI: 10.1021/acs.jcim.3c01016
  42. AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection Clyde A.; Liu X.; Brettin T.; Yoo H.; Partin A.; Babuji Y.; Blaiszik B.; Mohd-Yusof J.; Merzky A.; Turilli M.; Jha S.; Ramanathan A.; Stevens R. Sci Rep 2023, 13 (1), 2105. DOI: 10.1038/s41598-023-28785-9
  43. Integrative Drug Discovery Platform: A Modular Approach for Efficient and Automated Virtual Screening Li T.; Xie L.; Zhang Z.; Li X.; Duan B.; Niu G.; Sun S.; Zhang F.; Zhang R.; Tan G.; Zhang C. 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2023, 365-372. DOI: 10.1109/BIBM58861.2023.10385402
  44. Targeting ROS production through inhibition of NADPH oxidases Reis J.; Gorgulla C.; Massari M.; Marchese S.; Valente S.; Noce B.; Basile L.; Torner R.; Cox H., 3rd; Viennet T.; Yang M. H.; Ronan M. M.; Rees M. G.; Roth J. A.; Capasso L.; Nebbioso A.; Altucci L.; Mai A.; Arthanari H.; Mattevi A. Nat Chem Biol 2023, 19 (12), 1540-1550. DOI: 10.1038/s41589-023-01457-5
  45. Discovery of novel A2AR antagonists through deep learning-based virtual screening Tang M.; Wen C.; Lin J.; Chen H.; Ran T. AI in Life Science Research 2023, 3, 100058. DOI: 10.1016/j.ailsci.2023.100058
  46. Targeting ion channels with ultra-large library screening for hit discovery Melancon K.; Pliushcheuskaya P.; Meiler J.; Künze G. Front Mol Neurosci 2023, 16, 1336004. DOI: 10.3389/fnmol.2023.1336004
  47. Integrated molecular modeling and dynamics approaches revealed potential natural inhibitors of NF-κB transcription factor as breast cancer therapeutics Zubair M.; Khalil S.; Rasul I.; Nadeem H.; Noor F.; Ahmad S.; Alrumaihi F.; Allemailem K. S.; Almatroudi A.; Alshehri F. F.; Alshehri Z. S. J Biomol Struct Dyn 2023, 41 (24), 14715-14729. DOI: 10.1080/07391102.2023.2214209
  48. Therapeutic disruption of RAD52–ssDNA complexation via novel drug-like inhibitors Bhat D. S.; Malacaria E.; Biagi L. D.; Razzaghi M.; Honda M.; Hobbs K. F.; Hengel S. R.; Pichierri P.; Spies M. A.; Spies M. NAR Cancer 2023, 5 (2), zcad018. DOI: 10.1093/narcan/zcad018
  49. Transferable Graph Neural Fingerprint Models for Quick Response to Future Bio-Threats Chen W.; Ren Y.; Kagawa A.; Carbone M.R.; Chen S.Y.-C.; Qu X.; Yoo S.; Clyde A.; Ramanathan A.; Stevens R.L.; Van Dam H.J.J.; Lu D. 2023 International Conference on Machine Learning and Applications (ICMLA) 2023, 800-807. DOI: 10.1109/ICMLA58977.2023.00117
  50. Improving drug discovery with a hybrid deep generative model using reinforcement learning trained on a Bayesian docking approximation Xiong Y.; Wang Y.; Wang Y.; Li C.; Yusong P.; Wu J.; Gu L.; Butch C.J. J Comput Aided Mol Des 2023, 37 (11), 507-517. DOI: 10.1007/s10822-023-00523-3
  51. De novo generated combinatorial library design Johansson S.V.; Haghir Chehreghani M.; Engkvist O.; Schliep A. Digital Discovery 2023, 3 (1), 122-135. DOI: 10.1039/d3dd00095h
  52. Discovery and development of small-molecule heparanase inhibitors Zhang Y.; Cui L. Bioorg Med Chem 2023, 90, 117335. DOI: 10.1016/j.bmc.2023.117335
  53. Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores Meyenburg C.; Dolfus U.; Briem H.; Rarey M. J Comput Aided Mol Des 2023, 37 (1), 1-16. DOI: 10.1007/s10822-022-00485-y
  54. The Impact of Supervised Learning Methods in Ultralarge High-Throughput Docking Cavasotto C.N.; Di Filippo J.I. J Chem Inf Model 2023, 63 (8), 2267-2280. DOI: 10.1021/acs.jcim.2c01471
  55. Deep learning workflow for the inverse design of molecules with specific optoelectronic properties Yoo P.; Bhowmik D.; Mehta K.; Zhang P.; Liu F.; Lupo Pasini M.; Irle S. Sci Rep 2023, 13 (1), 20031. DOI: 10.1038/s41598-023-45385-9
  56. Epik: pKa and Protonation State Prediction through Machine Learning Johnston R. C.; Yao K.; Kaplan Z.; Chelliah M.; Leswing K.; Seekins S.; Watts S.; Calkins D.; Chief Elk J.; Jerome S. V.; Repasky M. P.; Shelley J. C. J Chem Theory Comput 2023, 19 (8), 2380-2388. DOI: 10.1021/acs.jctc.3c00044
  57. Keeping pace with the explosive growth of chemical libraries with structure-based virtual screening Kuan J.; Radaeva M.; Avenido A.; Cherkasov A.; Gentile F. WIREs Computational Molecular Science 2023, 13 (6), e1678. DOI: 10.1002/wcms.1678
  58. Surely you are joking, Mr Docking! Gentile F.; Oprea T.I.; Tropsha A.; Cherkasov A. Chem Soc Rev 2023, 52 (3), 872-878. DOI: 10.1039/d2cs00948j
  59. Creation of targeted compound libraries based on 3D shape recognition Kyrylchuk A.; Kravets I.; Cherednichenko A.; Tararina V.; Kapeliukha A.; Dudenko D.; Protopopov M. Mol Divers 2023, 27 (2), 939-949. DOI: 10.1007/s11030-022-10447-z
  60. Pharmacophore-based virtual screening, 3D QSAR, Docking, ADMET, and MD simulation studies: An in silico perspective for the identification of new potential HDAC3 inhibitors Lanka G.; Begum D.; Banerjee S.; Adhikari N.; P Y.; Ghosh B. Comput Biol Med 2023, 166, 107481. DOI: 10.1016/j.compbiomed.2023.107481
  61. Targeting in silico GPCR conformations with ultra-large library screening for hit discovery D.; Batebi H.; Ledwitch K.; Hildebrand P.W.; Meiler J. Trends Pharmacol Sci 2023, 44 (3), 150-161. DOI: 10.1016/j.tips.2022.12.006
  62. Design of the Global Health chemical diversity library v2 for screening against infectious diseases Wilson C.; Gardner J. M. F.; Gray D. W.; Baragana B.; Wyatt P. G.; Cookson A.; Thompson S.; Mendoza-Martinez C.; Bodkin M. J.; Gilbert I. H.; Tarver G. J. PLoS Negl Trop Dis 2023, 17 (12), e0011799. DOI: 10.1371/journal.pntd.0011799
  63. Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries Sivula T.; Yetukuri L.; Kalliokoski T.; Käsnänen H.; Poso A.; Pöhner I. J Chem Inf Model 2023, 63 (18), 5773-5783. DOI: 10.1021/acs.jcim.3c01239
  64. Overlap of On-demand Ultra-large Combinatorial Spaces with On-the-shelf Drug-like Libraries Perebyinis M.; Rognan D. Mol Inform 2023, 42 (1), e2200163. DOI: 10.1002/minf.202200163
  65. Evaluating Scalable Supervised Learning for Synthesize-on-Demand Chemical Libraries Alnammi M.; Liu S.; Ericksen S. S.; Ananiev G. E.; Voter A. F.; Guo S.; Keck J. L.; Hoffmann F. M.; Wildman S. A.; Gitter A. J Chem Inf Model 2023, 63 (17), 5513-5528. DOI: 10.1021/acs.jcim.3c00912
  66. Navigating large chemical spaces in early-phase drug discovery Korn M.; Ehrt C.; Ruggiu F.; Gastreich M.; Rarey M. Curr Opin Struct Biol 2023, 80, 102578. DOI: 10.1016/j.sbi.2023.102578
  67. Uni-Dock: GPU-Accelerated Docking Enables Ultralarge Virtual Screening Yu Y.; Cai C.; Wang J.; Bo Z.; Zhu Z.; Zheng H. J Chem Theory Comput 2023, 19 (11), 3336-3345. DOI: 10.1021/acs.jctc.2c01145
  68. Transferring a Molecular Foundation Model for Polymer Property Predictions Zhang P.; Kearney L.; Bhowmik D.; Fox Z.; Naskar A.K.; Gounley J. J Chem Inf Model 2023, 63 (24), 7689-7698. DOI: 10.1021/acs.jcim.3c01650
  69. Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease Varikoti R.A.; Schultz K.J.; Kombala C.J.; Kruel A.; Brandvold K.R.; Zhou M.; Kumar N. J Comput Aided Mol Des 2023, 37 (8), 339-355. DOI: 10.1007/s10822-023-00509-1
  70. Predicting the Likelihood of Molecules to Act as Modulators of Protein-Protein Interactions Wolk O.; Goldblum A. J Chem Inf Model 2023, 63 (1), 126-137. DOI: 10.1021/acs.jcim.2c00920
  71. Adaptive language model training for molecular design Blanchard A.E.; Bhowmik D.; Fox Z.; Gounley J.; Glaser J.; Akpa B.S.; Irle S. J Cheminform 2023, 15 (1), 59. DOI: 10.1186/s13321-023-00719-7
  72. CNN Use and Performance in Virtual Screening Chandegra B.; Prajapati P.; Prajapati J. Artificial Intelligence in Bioinformatics and Chemoinformatics 2023, 189-207. DOI: 10.1201/9781003353768-10
  73. Computational approaches streamlining drug discovery Sadybekov A.V.; Katritch V. Nature 2023, 616 (7958), 673-685. DOI: 10.1038/s41586-023-05905-z
  74. Managing the Drug Discovery Process (Second Edition) Miller S.; Moos W.; Munk B.; Munk S.; Hart C.; Spellmeyer D. 2023, 1-639. DOI: 10.1016/C2020-0-01031-5
  75. Inhibiting a promiscuous GPCR: iterative discovery of bitter taste receptor ligands Fierro F.; Peri L.; Hübner H.; Tabor-Schkade A.; Waterloo L.; Löber S.; Pfeiffer T.; Weikert D.; Dingjan T.; Margulis E.; Gmeiner P.; Niv M.Y. Cellular and Molecular Life Sciences 2023, 80 (4), 114. DOI: 10.1007/s00018-023-04765-0
  76. Fast Substructure Search in Combinatorial Library Spaces Liphardt T.; Sander T. J Chem Inf Model 2023, 63 (16), 5133-5141. DOI: 10.1021/acs.jcim.3c00290
  77. Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches Gorgulla C. Annual Review of Biomedical Data Science 2023, 6, 229-258. DOI: 10.1146/annurev-biodatasci-020222-025013
  78. Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development Hsieh C.-J.; Giannakoulias S.; Petersson E.J.; Mach R.H. Pharmaceuticals 2023, 16 (2), 317. DOI: 10.3390/ph16020317
  79. Shape-Based Virtual Screening of a Billion-Compound Library Identifies Mycobacterial Lipoamide Dehydrogenase Inhibitors Michino M.; Beautrait A.; Boyles N.A.; Nadupalli A.; Dementiev A.; Sun S.; Ginn J.; Baxt L.; Suto R.; Bryk R.; Jerome S.V.; Huggins D.J.; Vendome J. ACS Bio and Med Chem Au 2023, 3 (6), 507-515. DOI: 10.1021/acsbiomedchemau.3c00046
  80. Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system Gautam V.; Gaurav A.; Masand N.; Lee V.S.; Patil V.M. Mol Divers 2023, 27 (2), 959-985. DOI: 10.1007/s11030-022-10489-3
  81. Improved decision making with similarity based machine learning: applications in chemistry Lemm D.; Falk von Rudorff G.; Anatole von Lilienfeld O. Machine Learning: Science and Technology 2023, 4 (4), 045043. DOI: 10.1088/2632-2153/ad0fa3
  82. DIY Virtual Chemical Libraries - Novel Starting Points for Drug Discovery Takács G.; Havasi D.; Sándor M.; Dohánics Z.; Balogh G.T.; Kiss R. ACS Med Chem Lett 2023, 14 (9), 1188-1197. DOI: 10.1021/acsmedchemlett.3c00146
  83. Chemspace Atlas: Multiscale Chemography of Ultralarge Libraries for Drug Discovery Zabolotna Y.; Bonachera F.; Horvath D.; Lin A.; Marcou G.; Klimchuk O.; Varnek A. J Chem Inf Model 2022, 62 (18), 4537-4548. DOI: 10.1021/acs.jcim.2c00509
  84. Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits Rossetti G.G.; Ossorio M.A.; Rempel S.; Kratzel A.; Dionellis V.S.; Barriot S.; Tropia L.; Gorgulla C.; Arthanari H.; Thiel V.; Mohr P.; Gamboni R.; Halazonetis T.D. Sci Rep 2022, 12 (1), 2505. DOI: 10.1038/s41598-022-06306-4
  85. Small molecules and their impact in drug discovery: A perspective on the occasion of the 125th anniversary of the Bayer Chemical Research Laboratory Beck H.; Härter M.; Haß B.; Schmeck C.; Baerfacker L. Drug Discov Today 2022, 27 (6), 1560-1574. DOI: 10.1016/j.drudis.2022.02.015
  86. A multi-reference poly-conformational method for in silico design, optimization, and repositioning of pharmaceutical compounds illustrated for selected SARSCoV-2 ligands Alexandrov V.; Kirpich A.; Kantidze O.; Gankin Y. PeerJ 2022, 10, e14252. DOI: 10.7717/peerj.14252
  87. Targeting Echinococcus multilocularis PIM kinase for improving anti-parasitic chemotherapy Koike A.; Becker F.; Sennhenn P.; Kim J.; Zhang J.; Hannus S.; Brehm K. PLoS Negl Trop Dis 2022, 16 (10), e0010483. DOI: 10.1371/journal.pntd.0010483
  88. Automating Genetic Algorithm Mutations for Molecules Using a Masked Language Model Blanchard A.E.; Shekar M.C.; Gao S.; Gounley J.; Lyngaas I.; Glaser J.; Bhowmik D. IEEE Transactions on Evolutionary Computation 2022, 26 (4), 793-799. DOI: 10.1109/TEVC.2022.3144045
  89. Exploration of Ultralarge Compound Collections for Drug Discovery Warr W.A.; Nicklaus M.C.; Nicolaou C.A.; Rarey M. J Chem Inf Model 2022, 62 (9), 2021-2034. DOI: 10.1021/acs.jcim.2c00224
  90. Virtual Screening in the Cloud Identifies Potent and Selective ROS1 Kinase Inhibitors Petrović D.; Scott J.S.; Bodnarchuk M.S.; Lorthioir O.; Boyd S.; Hughes G.M.; Lane J.; Wu A.; Hargreaves D.; Robinson J.; Sadowski J. J Chem Inf Model 2022, 62 (16), 3832-3843. DOI: 10.1021/acs.jcim.2c00644
  91. Identifying Novel Inhibitors for Hepatic Organic Anion Transporting Polypeptides by Machine Learning-Based Virtual Screening Tuerkova A.; Bongers B.J.; Norinder U.; Ungvári O.; Székely V.; Tarnovskiy A.; Szakács G.; Özvegy-Laczka C.; Van Westen G.J.P.; Zdrazil B. J Chem Inf Model 2022, 62 (24), 6323-6335. DOI: 10.1021/acs.jcim.1c01460
  92. Fully Automated Creation of Virtual Chemical Fragment Spaces Using the Open-Source Library OpenChemLib Wahl J.; Sander T. J Chem Inf Model 2022, 62 (9), 2202-2211. DOI: 10.1021/acs.jcim.1c01041
  93. The Ukrainian Factor in Early-Stage Drug Discovery in the Context of Russian Invasion: The Case of Enamine Ltd Kondratov I.S.; Moroz Y.S.; Grygorenko O.O.; Tolmachev A.A. ACS Med Chem Lett 2022, 13 (7), 992-996. DOI: 10.1021/acsmedchemlett.2c00211
  94. Accelerators for Classical Molecular Dynamics Simulations of Biomolecules Jones D.; Allen J.E.; Yang Y.; Drew Bennett W.F.; Gokhale M.; Moshiri N.; Rosing T.S. J Chem Theory Comput 2022, 18 (7), 4047-4069. DOI: 10.1021/acs.jctc.1c01214
  95. Challenges for chemistry in Ukraine after the war: Ukrainian science requires rebuilding and support Kondratov I.S.; Moroz Y.S.; Gorgulla C.; Grygorenko O.O.; Komarov I.V.; Wagner G.; Tolmachev A.A. Proc Natl Acad Sci U S A 2022, 119 (50), e2210686119. DOI: 10.1073/pnas.2210686119
  96. A multilevel generative framework with hierarchical self-contrasting for bias control and transparency in structure-based ligand design Chan L.; Kumar R.; Verdonk M.; Poelking C. Nature Machine Intelligence 2022, 4 (12), 1130-1142. DOI: 10.1038/s42256-022-00564-7
  97. Calculating and Optimizing Physicochemical Property Distributions of Large Combinatorial Fragment Spaces Bellmann L.; Klein R.; Rarey M. J Chem Inf Model 2022, 62 (11), 2800-2810. DOI: 10.1021/acs.jcim.2c00334
  98. Self-Focusing Virtual Screening with Active Design Space Pruning Graff D.E.; Aldeghi M.; Morrone J.A.; Jordan K.E.; Pyzer-Knapp E.O.; Coley C.W. J Chem Inf Model 2022, 62 (16), 3854-3862. DOI: 10.1021/acs.jcim.2c00554
  99. Rings in Clinical Trials and Drugs: Present and Future Shearer J.; Castro J.L.; Lawson A.D.G.; MacCoss M.; Taylor R.D. J Med Chem 2022, 65 (13), 8699-8712. DOI: 10.1021/acs.jmedchem.2c00473
  100. Magnet for the Needle in Haystack: "crystal Structure First" Fragment Hits Unlock Active Chemical Matter Using Targeted Exploration of Vast Chemical Spaces Muller J.; Klein R.; Tarkhanova O.; Gryniukova A.; Borysko P.; Merkl S.; Ruf M.; Neumann A.; Gastreich M.; Moroz Y. S.; Klebe G.; Glinca S. J Med Chem 2022, 65 (23), 15663-15678. DOI: 10.1021/acs.jmedchem.2c00813
  101. SynthI: A New Open-Source Tool for Synthon-Based Library Design Zabolotna Y.; Volochnyuk D.M.; Ryabukhin S.V.; Gavrylenko K.; Horvath D.; Klimchuk O.; Oksiuta O.; Marcou G.; Varnek A. J Chem Inf Model 2022, 62 (9), 2151-2163. DOI: 10.1021/acs.jcim.1c00754
  102. Artificial Intelligence in Compound Design Grebner C.; Matter H.; Hessler G. Artificial Intelligence in Drug Design 2022, 2390, 349-382. DOI: 10.1007/978-1-0716-1787-8_15
  103. Maximizing the integration of virtual and experimental screening in hit discovery Bajusz D.; Keserű G.M. Expert Opin Drug Discov 2022, 17 (6), 629-640. DOI: 10.1080/17460441.2022.2085685
  104. One-pot parallel synthesis of 1,3,5-trisubstituted 1,2,4-triazoles Radchenko D.S.; Naumchyk V.S.; Dziuba I.; Kyrylchuk A.A.; Gubina K.E.; Moroz Y.S.; Grygorenko O.O. Mol Divers 2022, 26 (2), 993-1004. DOI: 10.1007/s11030-021-10218-2
  105. Towards rational computational peptide design Chang L.; Mondal A.; Perez A. Front Bioinform 2022, 2 (), 1046493. DOI: 10.3389/fbinf.2022.1046493
  106. Hit Expansion of a Noncovalent SARS-CoV-2 Main Protease Inhibitor Glaser J.; Sedova A.; Galanie S.; Kneller D. W.; Davidson R. B.; Maradzike E.; Del Galdo S.; Labbe A.; Hsu D. J.; Agarwal R.; Bykov D.; Tharrington A.; Parks J. M.; Smith D. M. A.; Daidone I.; Coates L.; Kovalevsky A.; Smith J. C. ACS Pharmacol Transl Sci 2022, 5 (4), 255-265. DOI: 10.1021/acsptsci.2c00026
  107. Identification of potential SARS-CoV-2 Mpro inhibitors integrating molecular docking and water thermodynamics Sobhia M.E.; Ghosh K.; Sivangula S.; Kumar S.; Singh H. J Biomol Struct Dyn 2022, 40 (11), 5079-5089. DOI: 10.1080/07391102.2020.1867642
  108. Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking Gentile F.; Yaacoub J.C.; Gleave J.; Fernandez M.; Ton A.-T.; Ban F.; Stern A.; Cherkasov A. Nature Protocols 2022, 17 (3), 672-697. DOI: 10.1038/s41596-021-00659-2
  109. Comparison of Combinatorial Fragment Spaces and Its Application to Ultralarge Make-on-Demand Compound Catalogs Bellmann L.; Penner P.; Gastreich M.; Rarey M. J Chem Inf Model 2022, 62 (3), 553-566. DOI: 10.1021/acs.jcim.1c01378
  110. Benchmarking ensemble docking methods in D3R Grand Challenge 4 Gan J.L.; Kumar D.; Chen C.; Taylor B.C.; Jagger B.R.; Amaro R.E.; Lee C.T. J Comput Aided Mol Des 2022, 36 (2), 87-99. DOI: 10.1007/s10822-021-00433-2
  111. Ultrahigh Throughput Protein–Ligand Docking with Deep Learning Clyde A. Artificial Intelligence in Drug Design 2022, 2390, 301-319. DOI: 10.1007/978-1-0716-1787-8_13
  112. Synthon-based ligand discovery in virtual libraries of over 11 billion compounds Sadybekov A. A.; Sadybekov A. V.; Liu Y.; Iliopoulos-Tsoutsouvas C.; Huang X. P.; Pickett J.; Houser B.; Patel N.; Tran N. K.; Tong F.; Zvonok N.; Jain M. K.; Savych O.; Radchenko D. S.; Nikas S. P.; Petasis N. A.; Moroz Y. S.; Roth B. L.; Makriyannis A.; Katritch V. Nature 2022, 601 (7893), 452-459. DOI: 10.1038/s41586-021-04220-9
  113. A Review on Parallel Virtual Screening Softwares for High-Performance Computers Murugan N.A.; Podobas A.; Gadioli D.; Vitali E.; Palermo G.; Markidis S. Pharmaceuticals 2022, 15 (1), 63. DOI: 10.3390/ph15010063
  114. Identification of novel protein kinase C-βII inhibitors: virtual screening, molecular docking and molecular dynamics simulation studies Sanapalli B.K.R.; Yele V.; Baldaniya L.; Karri V.V.S.R. J Mol Model 2022, 28 (5), 117. DOI: 10.1007/s00894-022-05104-z
  115. Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models Blanchard A.E.; Zhang P.; Bhowmik D.; Mehta K.; Gounley J.; Reeve S.T.; Irle S.; Pasini M.L. Smoky Mountains Computational Sciences and Engineering Conferenc, SMC 2022 2022, 3-19. DOI: 10.1007/978-3-031-23606-8_1
  116. Molecular docking-based computational platform for high-throughput virtual screening Zhang B.; Li H.; Yu K.; Jin Z. CCF Trans High Perform Comput 2022, 4 (1), 63-74. DOI: 10.1007/s42514-021-00086-5
  117. Call for Papers for the Special Issue: From Reaction Informatics to Chemical Space Rarey M.; Nicklaus M.C.; Warr W. J Chem Inf Model 2021, 61 (4), 1531-1532. DOI: 10.1021/acs.jcim.1c00321
  118. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data Bender A.; Cortes-Ciriano I. Drug Discov Today 2021, 26 (4), 1040-1052. DOI: 10.1016/j.drudis.2020.11.037
  119. Drug discovery for mycobacterium tuberculosis using structure-based computer-aided drug design approach Ejalonibu M.A.; Ogundare S.A.; Elrashedy A.A.; Ejalonibu M.A.; Lawal M.M.; Mhlongo N.N.; Kumalo H.M. Int J Mol Sci 2021, 22 (24), 13259. DOI: 10.3390/ijms222413259
  120. Enabling rapid COVID-19 small molecule drug design through scalable deep learning of generative models Jacobs S. A.; Moon T.; McLoughlin K.; Jones D.; Hysom D.; Ahn D. H.; Gyllenhaal J.; Watson P.; Lightstone F. C.; Allen J. E.; Karlin I.; Van Essen B. IJHPCA 2021, 35 (5), 469-482. DOI: 10.1177/10943420211010930
  121. Enamine Ltd.: The Science and Business of Organic Chemistry and Beyond Grygorenko O.O. Eur J Org Chem 2021, 2021 (47), 6474-6477. DOI: 10.1002/ejoc.202101210
  122. Discovery of Small-Molecule Inhibitors of SARS-CoV-2 Proteins Using a Computational and Experimental Pipeline Lau E. Y.; Negrete O. A.; Bennett W. F. D.; Bennion B. J.; Borucki M.; Bourguet F.; Epstein A.; Franco M.; Harmon B.; He S.; Jones D.; Kim H.; Kirshner D.; Lao V.; Lo J.; McLoughlin K.; Mosesso R.; Murugesh D. K.; Saada E. A.; Segelke B.; Stefan M. A.; Stevenson G. A.; Torres M. W.; Weilhammer D. R.; Wong S.; Yang Y.; Zemla A.; Zhang X.; Zhu F.; Allen J. E.; Lightstone F. C. Front Mol Biosci 2021, 8, 678701. DOI: 10.3389/fmolb.2021.678701
  123. Best Practices for Docking-Based Virtual Screening Neves B.J.; Mottin M.; Moreira-Filho J.T.; Sousa B.K.P.; Mendonca S.S.; Andrade C.H. Molecular Docking for Computer-Aided Drug Design 2021, 75-98. DOI: 10.1016/B978-0-12-822312-3.00001-1
  124. Deep learning in virtual screening: Recent applications and developments Kimber T.B.; Chen Y.; Volkamer A. Int J Mol Sci 2021, 22 (9), 4435. DOI: 10.3390/ijms22094435
  125. C@PA: Computer-Aided Pattern Analysis to Predict Multitarget ABC Transporter Inhibitors Namasivayam V.; Silbermann K.; Wiese M.; Pahnke J.; Stefan S.M. J Med Chem 2021, 64 (6), 3350-3366. DOI: 10.1021/acs.jmedchem.0c02199
  126. Accelerating high-throughput virtual screening through molecular pool-based active learning Graff D.E.; Shakhnovich E.I.; Coley C.W. Chem Sci 2021, 12 (22), 7866-7881. DOI: 10.1039/d0sc06805e
  127. Web-Based Quantitative Structure–Activity Relationship Resources Facilitate Effective Drug Discovery Wang Y.-L.; Li J.-Y.; Shi X.-X.; Wang Z.; Hao G.-F.; Yang G.-F. Top Curr Chem (Cham) 2021, 379 (6), 37. DOI: 10.1007/s41061-021-00349-3
  128. Challenges Encountered Applying Equilibrium and Nonequilibrium Binding Free Energy Calculations Baumann H.M.; Gapsys V.; De Groot B.L.; Mobley D.L. J Phys Chem B 2021, 125 (17), 4241-4261. DOI: 10.1021/acs.jpcb.0c10263
  129. From computer-aided drug discovery to computer-driven drug discovery Frye L.; Bhat S.; Akinsanya K.; Abel R. Drug Discov Today Technol 2021, 39, 111-117. DOI: 10.1016/j.ddtec.2021.08.001
  130. Structure-based drug repurposing against COVID-19 and emerging infectious diseases: Methods, resources and discoveries Masoudi-Sobhanzadeh Y.; Salemi A.; Pourseif M.M.; Jafari B.; Omidi Y.; Masoudi-Nejad A. Briefings in Bioinformatics 2021, 22 (6), bbab113. DOI: 10.1093/bib/bbab113
  131. Design concepts for DNA-encoded library synthesis Zhang Y.; Clark M.A. Bioorg Med Chem 2021, 41, 116189. DOI: 10.1016/j.bmc.2021.116189
  132. Prediction of chemical compounds properties using a deep learning model Galushka M.; Swain C.; Browne F.; Mulvenna M.D.; Bond R.; Gray D. Neural Comput & Applic 2021, 33 (20), 13345-13366. DOI: 10.1007/s00521-021-05961-4
  133. Discovery of Novel BRD4 Ligand Scaffolds by Automated Navigation of the Fragment Chemical Space Piticchio S. G.; Martinez-Cartro M.; Scaffidi S.; Rachman M.; Rodriguez-Arevalo S.; Sanchez-Arfelis A.; Escolano C.; Picaud S.; Krojer T.; Filippakopoulos P.; von Delft F.; Galdeano C.; Barril X. J Med Chem 2021, 64 (24), 17887-17900. DOI: 10.1021/acs.jmedchem.1c01108
  134. Topological Similarity Search in Large Combinatorial Fragment Spaces Bellmann L.; Penner P.; Rarey M. J Chem Inf Model 2021, 61 (1), 238-251. DOI: 10.1021/acs.jcim.0c00850
  135. Monitoring Large Scale Supercomputers: A Case Study with the Lassen Supercomputer Patki T.; Bertsch A.; Karlin I.; Ahn D.H.; Van Essen B.; Rountree B.; De Supinski B.R.; Besaw N. 2021 IEEE International Conference on Cluster Computing (CLUSTER) 2021, 468-480. DOI: 10.1109/Cluster48925.2021.00057
  136. Generative Deep Learning for Targeted Compound Design Sousa T.; Correia J.; Pereira V.; Rocha M. J Chem Inf Model 2021, 61 (11), 5343-5361. DOI: 10.1021/acs.jcim.0c01496
  137. Generative Models for de Novo Drug Design Tong X.; Liu X.; Tan X.; Li X.; Jiang J.; Xiong Z.; Xu T.; Jiang H.; Qiao N.; Zheng M. J Med Chem 2021, 64 (19), 14011-14027. DOI: 10.1021/acs.jmedchem.1c00927
  138. Chemical Topic Modeling - An Unsupervised Approach Originating from Text-mining to Organize Chemical Data Schneider N.; Fechner N.; Stiefl N.; Landrum G.A. Artificial Intelligence in Drug Discovery 2021, 17-44. DOI: 10.1039/9781788016841-00015
  139. Structure-based virtual screening of ultra-large library yields potent antagonists for a lipid gpcr Sadybekov A. A.; Brouillette R. L.; Marin E.; Sadybekov A. V.; Luginina A.; Gusach A.; Mishin A.; Besserer-Offroy E.; Longpre J. M.; Borshchevskiy V.; Cherezov V.; Sarret P.; Katritch V. Biomolecules 2020, 10 (12), 1-15. DOI: 10.3390/biom10121634
  140. BRADSHAW: a system for automated molecular design Green D. V. S.; Pickett S.; Luscombe C.; Senger S.; Marcus D.; Meslamani J.; Brett D.; Powell A.; Masson J. J Comput Aided Mol Des 2020, 34 (7), 747-765. DOI: 10.1007/s10822-019-00234-8
  141. Adapting CHMTRN (CHeMistry TRaNslator) for a New Use Judson P.N.; Ihlenfeldt W.-D.; Patel H.; Delannée V.; Tarasova N.; Nicklaus M.C. J Chem Inf Model 2020, 60 (7), 3336-3341. DOI: 10.1021/acs.jcim.0c00448
  142. Computational Approaches to Identify a Hidden Pharmacological Potential in Large Chemical Libraries Druzhilovskiy D.S.; Stolbov L.A.; Savosina P.I.; Pogodin P.V.; Filimonov D.A.; Veselovsky A.V.; Stefanisko K.; Tarasova N.I.; Nicklaus M.C.; Poroikov V.V. Supercomputing Frontiers and Innovations 2020, 7 (3), 57-76. DOI: 10.14529/jsfi200306
  143. Generating Multibillion Chemical Space of Readily Accessible Screening Compounds Grygorenko O.O.; Radchenko D.S.; Dziuba I.; Chuprina A.; Gubina K.E.; Moroz Y.S. iScience 2020, 23 (11), 101681. DOI: 10.1016/j.isci.2020.101681
  144. Computer-aided estimation of biological activity profiles of drug-like compounds taking into account their metabolism in human body Filimonov D.A.; Rudik A.V.; Dmitriev A.V.; Poroikov V.V. Int J Mol Sci 2020, 21 (20), 1-13. DOI: 10.3390/ijms21207492
  145. Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19 Acharya A.; Agarwal R.; Baker M. B.; Baudry J.; Bhowmik D.; Boehm S.; Byler K. G.; Chen S. Y.; Coates L.; Cooper C. J.; Demerdash O.; Daidone I.; Eblen J. D.; Ellingson S.; Forli S.; Glaser J.; Gumbart J. C.; Gunnels J.; Hernandez O.; Irle S.; Kneller D. W.; Kovalevsky A.; Larkin J.; Lawrence T. J.; LeGrand S.; Liu S. H.; Mitchell J. C.; Park G.; Parks J. M.; Pavlova A.; Petridis L.; Poole D.; Pouchard L.; Ramanathan A.; Rogers D. M.; Santos-Martins D.; Scheinberg A.; Sedova A.; Shen Y.; Smith J. C.; Smith M. D.; Soto C.; Tsaris A.; Thavappiragasam M.; Tillack A. F.; Vermaas J. V.; Vuong V. Q.; Yin J.; Yoo S.; Zahran M.; Zanetti-Polzi L. J Chem Inf Model 2020, 60 (12), 5832-5852. DOI: 10.1021/acs.jcim.0c01010
  146. Characterizing human odorant signals: Insights from insect semiochemistry and in silico modelling Radadiya A.; Pickett J.A. Philos Trans R Soc Lond B Biol Sci 2020, 375 (1800), 20190263. DOI: 10.1098/rstb.2019.0263
  147. Chemoinformatics-based enumeration of chemical libraries: a tutorial Saldívar-González F.I.; Huerta-García C.S.; Medina-Franco J.L. J Cheminform 2020, 12 (1), 64. DOI: 10.1186/s13321-020-00466-z
  148. In silico strategies in tuberculosis drug discovery Macalino S.J.Y.; Billones J.B.; Organo V.G.; Carrillo M.C.O. Molecules 2020, 25 (3), 665. DOI: 10.3390/molecules25030665
  149. A Fragment Library of Natural Products and its Comparative Chemoinformatic Characterization Chávez-Hernández A.L.; Sánchez-Cruz N.; Medina-Franco J.L. Mol Inform 2020, 39 (11), e2000050. DOI: 10.1002/minf.202000050
  150. Autonomous Discovery in the Chem Scis Part II: Outlook Coley C.W.; Eyke N.S.; Jensen K.F. Angew Chem Int Ed Engl 2020, 59 (52), 23414-23436. DOI: 10.1002/anie.201909989
  151. Electrochemical Scaled-up Synthesis of Cyclic Enecarbamates as Starting Materials for Medicinal Chemistry Relevant Building Bocks Tereshchenko O. D.; Perebiynis M. Y.; Knysh I. V.; Vasylets O. V.; Sorochenko A. A.; Slobodyanyuk E. Y.; Rusanov E. B.; Borysov O. V.; Kolotilov S. V.; Ryabukhin S. V.; Volochnyuk D. M. Advanced Synthesis & Catalysis 2020, 362 (15), 3229-3242. DOI: 10.1002/adsc.202000450
  152. ZINC20 - A Free Ultralarge-Scale Chemical Database for Ligand Discovery Irwin J. J.; Tang K. G.; Young J.; Dandarchuluun C.; Wong B. R.; Khurelbaatar M.; Moroz Y. S.; Mayfield J.; Sayle R. A. J Chem Inf Model 2020, 60 (12), 6065-6073. DOI: 10.1021/acs.jcim.0c00675
  153. SAVI, in silico generation of billions of easily synthesizable compounds through expert-system type rules Patel H.; Ihlenfeldt W. D.; Judson P. N.; Moroz Y. S.; Pevzner Y.; Peach M. L.; Delannee V.; Tarasova N. I.; Nicklaus M. C. Sci Data 2020, 7 (1), 384. DOI: 10.1038/s41597-020-00727-4
  154. Automated de Novo Design in Medicinal Chemistry: Which Types of Chemistry Does a Generative Neural Network Learn? Grebner C.; Matter H.; Plowright A.T.; Hessler G. J Med Chem 2020, 63 (16), 8809-8823. DOI: 10.1021/acs.jmedchem.9b02044
  155. Can easy chemistry produce complex, diverse, and novel molecules? Tomberg A.; Boström J. Drug Discov Today 2020, 25 (12), 2174-2181. DOI: 10.1016/j.drudis.2020.09.027
  156. Autonomous Discovery in the Chem Scis Part I: Progress Coley C.W.; Eyke N.S.; Jensen K.F. Angew Chem Int Ed Engl 2020, 59 (51), 22858-22893. DOI: 10.1002/anie.201909987
  157. Focused Library Generator: case of Mdmx inhibitors Xia Z.; Karpov P.; Popowicz G.; Tetko I.V. J Comput Aided Mol Des 2020, 34 (7), 769-782. DOI: 10.1007/s10822-019-00242-8
  158. Multi-objective biofilm algorithm (MOBifi) for de novo drug design with special focus to anti-diabetic drugs Devi R.V.; Siva Sathya S.; Coumar M.S. Applied Soft Computing 2020, 96, 106655. DOI: 10.1016/j.asoc.2020.106655
  159. Screening of metal ions and organocatalysts on solid support-coupled DNA oligonucleotides guides design of DNA-encoded reactions Potowski M.; Losch F.; Wünnemann E.; Dahmen J.K.; Chines S.; Brunschweiger A. Chem Sci 2019, 10 (45), 10481-10492. DOI: 10.1039/c9sc04708e
  160. SAR by space: Enriching hit sets from the chemical space Klingler F. M.; Gastreich M.; Grygorenko O. O.; Savych O.; Borysko P.; Griniukova A.; Gubina K. E.; Lemmen C.; Moroz Y. S. Molecules 2019, 24 (17), 3096. DOI: 10.3390/molecules24173096
  161. Comparison of Large Chemical Spaces Lessel U.; Lemmen C. ACS Med Chem Lett 2019, 10 (10), 1504-1510. DOI: 10.1021/acsmedchemlett.9b00331
  162. Using Machine Learning to Inform Decisions in Drug Discovery: An Industry Perspective Green D.V.S. ACS Symposium Series 2019, 1326, 81-101. DOI: 10.1021/bk-2019-1326.ch005
  163. Prediction and Experimental Confirmation of Novel Peripheral Cannabinoid-1 Receptor Antagonists El-Atawneh S.; Hirsch S.; Hadar R.; Tam J.; Goldblum A. J Chem Inf Model 2019, 59 (9), 3996-4006. DOI: 10.1021/acs.jcim.9b00577
  164. Recent applications of machine learning in medicinal chemistry Panteleev J.; Gao H.; Jia L. Bioorg Med Chem Letters 2018, 28 (17), 2807-2815. DOI: 10.1016/j.bmcl.2018.06.046
  165. Practical aspects of machine learning for the design-synthesis-purify-assay workflow Sirockin F.; Stiefl N. Chimia 2018, 72 (9), 648-649. DOI: 10.2533/chimia.2018.648
  166. (Chlorosulfonyl)benzenesulfonyl Fluorides - Versatile Building Blocks for Combinatorial Chemistry: Design, Synthesis and Evaluation of a Covalent Inhibitor Library Tolmachova K. A.; Moroz Y. S.; Konovets A.; Platonov M. O.; Vasylchenko O. V.; Borysko P.; Zozulya S.; Gryniukova A.; Bogolubsky A. V.; Pipko S.; Mykhailiuk P. K.; Brovarets V. S.; Grygorenko O. O. ACS Comb Sci 2018, 20 (11), 672-680. DOI: 10.1021/acscombsci.8b00130
  167. Facile One-Pot Parallel Synthesis of 3-Amino-1,2,4-triazoles Bogolyubsky A. V.; Savych O.; Zhemera A. V.; Pipko S. E.; Grishchenko A. V.; Konovets A. I.; Doroshchuk R. O.; Khomenko D. N.; Brovarets V. S.; Moroz Y. S.; Vybornyi M. ACS Comb Sci 2018, 20 (7), 461-466. DOI: 10.1021/acscombsci.8b00060
  168. Straightforward hit identification approach in fragment-based discovery of bromodomain-containing protein 4 (BRD4) inhibitors Borysko P.; Moroz Y. S.; Vasylchenko O. V.; Hurmach V. V.; Starodubtseva A.; Stefanishena N.; Nesteruk K.; Zozulya S.; Kondratov I. S.; Grygorenko O. O. Bioorg Med Chem 2018, 26 (12), 3399-3405. DOI: 10.1016/j.bmc.2018.05.010
  169. An Old Story in the Parallel Synthesis World: An Approach to Hydantoin Libraries Bogolubsky A. V.; Moroz Y. S.; Savych O.; Pipko S.; Konovets A.; Platonov M. O.; Vasylchenko O. V.; Hurmach V. V.; Grygorenko O. O. ACS Comb Sci 2018, 20 (1), 35-43. DOI: 10.1021/acscombsci.7b00163
  170. Design, Virtual Screening, and Synthesis of Antagonists of αiIbβ3 as Antiplatelet Agents Polishchuk P. G.; Samoylenko G. V.; Khristova T. M.; Krysko O. L.; Kabanova T. A.; Kabanov V. M.; Kornylov A. Y.; Klimchuk O.; Langer T.; Andronati S. A.; Kuz'min V. E.; Krysko A. A.; Varnek A. J Med Chem 2015, 58 (19), 7681-7694. DOI: 10.1021/acs.jmedchem.5b00865
  171. Exploring virtual scaffold spaces Pitt W.R.; Kroeplien B. Scaffold Hopping in Medicinal Chemistry 2013, 83-104. DOI: 10.1002/9783527665143.ch05
  172. Approach to the library of fused pyridine-4-carboxylic acids by combes-type reaction of acyl pyruvates and electron-rich amino heterocycles Volochnyuk D. M.; Ryabukhin S. V.; Plaskon A. S.; Dmytriv Y. V.; Grygorenko O. O.; Mykhailiuk P. K.; Krotko D. G.; Pushechnikov A.; Tolmachev A. A. J Comb Chem 2010, 12 (4), 510-517. DOI: 10.1021/cc100040q
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