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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
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  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
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  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
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