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Publications that cite Enamine REAL

  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
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