Cited by 7,524

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Extended-Connectivity Fingerprints

Rogers, David, Hahn, Mathew·2010·Journal of Chemical Information and Modeling

Open this paper in CoChat — free account

Abstract

Extended-connectivity fingerprints (ECFPs) are a novel class of topological fingerprints for molecular characterization. Historically, topological fingerprints were developed for substructure and similarity searching. ECFPs were developed specifically for structure-activity modeling. ECFPs are circular fingerprints with a number of useful qualities: they can be very rapidly calculated; they are not predefined and can represent an essentially infinite number of different molecular features (including stereochemical information); their features represent the presence of particular substructures, allowing easier interpretation of analysis results; and the ECFP algorithm can be tailored to generate different types of circular fingerprints, optimized for different uses. While the use of ECFPs has been widely adopted and validated, a description of their implementation has not previously been presented in the literature.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1021/ci100050t

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Handbook of Molecular Descriptors
Todeschini, R., & Consonni, V. (2000) · Methods and Principles in Medicinal Chemistry · doi:10.1002/9783527613106 · Cited by 4,107
Predicting new molecular targets for known drugs
Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., et al. (2009) · Nature · doi:10.1038/nature08506 · Cited by 1,667

Show all 2 references

Research works printed but not linked: 97

Christie B. D., 1993, J. Chem. Inf. Comput. Sci., 33, 545, 10.1021/ci00014a004 · doi:10.1021/ci00014a004
Johnson M., 1990, Concepts and Applications of Molecular Similarity
McGregor M. J., 1997, J. Chem. Inf. Comput. Sci., 37, 443, 10.1021/ci960151e · DOI
Breiman L., 1984, Classification and Regression Trees
Xia X., 2004, J. Med. Chem., 47, 4463, 10.1021/jm0303195 · DOI · Paper page
Hassan M., 2006, Mol. Diversity, 10, 283, 10.1007/s11030-006-9041-5 · DOI
Glick M., 2006, J. Chem. Inf. Model., 46, 193, 10.1021/ci050374h · DOI
Morgan H. L., 1965, J. Chem. Doc., 5, 107, 10.1021/c160017a018 · DOI · Paper page
Weininger D., 1989, J. Chem. Inf. Comput. Sci., 29, 97, 10.1021/ci00062a008 · DOI · Paper page
Clark R. D., 1989, J. Comput. Chem., 10, 982, 10.1002/jcc.540100804 · DOI · Paper page
Ghose A. K., 1998, J. Phys. Chem. A, 102, 3762, 10.1021/jp980230o · DOI · Paper page
Greene J., 1994, J. Chem. Inf. Comput. Sci., 34, 1297, 10.1021/ci00022a012 · doi:10.1021/ci00022a012
Dubios J. E., 1973, J. Chem. Doc., 13, 8, 10.1021/c160048a004 · doi:10.1021/c160048a004
Attias R., 1983, J. Chem. Inf. Comput. Sci., 23, 102, 10.1021/ci00039a003 · doi:10.1021/ci00039a003
Durant J., 2002, J. Chem. Inf. Comput. Sci., 42, 1273, 10.1021/ci010132r · DOI · Paper page
Xing L., 2002, J. Chem. Inf. Comput. Sci., 42, 796, 10.1021/ci010315d · DOI
Bender A., 2004, J. Chem. Inf. Comput. Sci., 44, 170, 10.1021/ci034207y · DOI
Glen R. C., 2006, IDrugs, 9, 199
Li Q., 2007, J. Chem. Inf. Model., 47, 1776, 10.1021/ci700107y · DOI
Faulon J. L., 1994, J. Chem. Inf. Comput. Sci., 34, 1204, 10.1021/ci00021a031 · DOI
Visco D. P., 2002, J. Mol. Graphics Modell., 20, 429, 10.1016/S1093-3263(01)00144-9 · DOI
Faulon J. L., 2003, J. Chem. Inf. Comput. Sci., 43, 707, 10.1021/ci020345w · DOI
Faulon J. L., 2003, J. Chem. Inf. Comput. Sci., 43, 721, 10.1021/ci020346o · DOI
Lowis, D. R.HQSAR. A New, Highly Predictive QSAR Technique;Tripos Technical Notes, Tripos:St. Louis, MO, 1998; Vol. 1, no. 5, p3.
Sheridan R. P., 1989, J. Chem. Inf. Comput. Sci., 29, 255, 10.1021/ci00064a005 · doi:10.1021/ci00064a005
Sprague P. W., 1995, Perspect. Drug Discovery Des., 3, 1, 10.1007/BF02174464 · DOI
Norinder U., 1995, QSAR and Molecular Modeling: Concepts, Computational Tools and Biological Applications, 433
Weinstein J. N., 1992, Science, 258, 447, 10.1126/science.1411538 · DOI · Paper page
Briem H., 1996, J. Med. Chem., 39, 3401, 10.1021/jm950800y · DOI
Briem H., 2000, Perspect. Drug Discovery Des., 20, 231, 10.1023/A:1008793325522 · DOI
Rhodes N., 2006, J. Chem. Inf. Model., 46, 615, 10.1021/ci0503863 · DOI
Zhang Q., 2006, J. Med. Chem., 49, 1536, 10.1021/jm050468i · DOI
Nettles J. H., 2006, J. Med. Chem., 49, 6802, 10.1021/jm060902w · DOI
Sheridan R., 2007, Expert Opin. Drug Discovery, 2, 423, 10.1517/17460441.2.4.423 · DOI
Rogers D., 2005, J. Biomol. Screening, 10, 682, 10.1177/1087057105281365 · DOI · Paper page
Hert J., 2006, J. Chem. Inf. Model., 46, 462, 10.1021/ci050348j · DOI
Carhart R. E., 1985, J. Chem. Inf. Comput. Sci., 25, 64, 10.1021/ci00046a002 · DOI · Paper page
Freedman D., 1991, Statistics
Herman, W.InPartial Least Squares;Kotz, S.; Johnson, N. L., Eds.Wiley:New York, NY, 1985; pp581−591.
Rumelhart D. E., 1986, Parallel Distributed Processing: Explorations in the Microstructure of Cognition: Foundations, 10.7551/mitpress/5236.001.0001 · DOI · Paper page
Yoon S., 2005, J. Comput.-Aided Mol. Des., 19, 483, 10.1007/s10822-005-9002-6 · DOI
Steindl T. M., 2006, J. Chem. Inf. Model., 46, 2146, 10.1021/ci6002043 · DOI
Yang H., 2003, J. Biol. Chem., 278, 35079, 10.1074/jbc.M303098200 · DOI
Klon A. E., 2009, Comb. Chem. High Throughput Screening, 12, 469, 10.2174/138620709788489046 · doi:10.2174/138620709788489046
Hert J., 2005, J. Med. Chem., 48, 7049, 10.1021/jm050316n · DOI
Cloutier L. M., 2008, Drug Discovery Today, 13, 536, 10.1016/j.drudis.2008.03.022 · doi:10.1016/j.drudis.2008.03.022
Metz J. T., 2007, J. Comput.-Aided Mol. Des., 21, 139, 10.1007/s10822-007-9109-z · DOI
Glick M., 2003, Mol. Phys., 101, 1325, 10.1080/0026897031000099862 · DOI
Glick M., 2004, J. Biomol. Screening, 9, 32, 10.1177/1087057103260590 · DOI
Chen B., 2007, J. Comput.-Aided Mol. Des., 21, 51, 10.1007/s10822-006-9096-5 · DOI
Hert J., 2004, Org. Biomol. Chem., 2, 3256, 10.1039/b409865j · DOI · Paper page
Costache A. D., 2007, Xenobiotica, 37, 221, 10.1080/00498250601089162 · DOI
Good A. C., 2007, J. Chem. Inf. Model., 47, 110, 10.1021/ci6003493 · DOI
Sun H., 2005, J. Med. Chem., 48, 4031, 10.1021/jm050180t · DOI
Flahive E., 2007, QSAR Comb. Sci., 26, 679, 10.1002/qsar.200610124 · doi:10.1002/qsar.200610124
Nidhi; Glick M., 2006, J. Chem. Inf. Model., 46, 1124, 10.1021/ci060003g · DOI · Paper page
Gedeck P., 2006, J. Chem. Inf. Model., 46, 1924, 10.1021/ci050413p · DOI
Zhou D., 2008, J. Chem. Inf. Model., 48, 981, 10.1021/ci800024c · DOI
Klon A. E., 2006, J. Chem. Inf. Model., 46, 1945, 10.1021/ci0601315 · DOI
O’Brien S. E., 2005, J. Med. Chem., 48, 1287, 10.1021/jm049254b · DOI
Sun H., 2006, ChemMedChem, 1, 315, 10.1002/cmdc.200500047 · doi:10.1002/cmdc.200500047
Sciabola S., 2007, J. Chem. Inf. Model., 47, 76, 10.1021/ci060143q · DOI
Zhou D., 2007, Lett. Drug Des. Discovery, 4, 192, 10.2174/157018007780077462 · doi:10.2174/157018007780077462
Jensen B. F., 2007, J. Med. Chem., 50, 501, 10.1021/jm060333s · DOI
McIntyre T. A., 2009, Xenobiotica, 39, 1, 10.1080/00498250902926906 · DOI
Pelletier D. J., 2007, J. Chem. Inf. Model., 47, 1196, 10.1021/ci6004542 · doi:10.1021/ci6004542
Scheiber J., 2009, J. Med. Chem., 52, 3103, 10.1021/jm801546k · DOI
Lee P. H., 2007, J. Comput.-Aided Mol. Des., 21, 665, 10.1007/s10822-007-9124-0 · DOI
Kellenberger E., 2007, J. Med. Chem., 50, 1294, 10.1021/jm061389p · DOI
Bender A., 2009, J. Chem. Inf. Model., 49, 108, 10.1021/ci800249s · DOI · Paper page
Bender A., 2006, J. Chem. Inf. Model., 46, 2445, 10.1021/ci600197y · DOI · Paper page
Martin Y., 2009, QSAR Comb. Sci., 28, 797, 10.1002/qsar.200810176 · DOI
Selzer P., 2005, Curr. Opin. Chem. Biol., 9, 310, 10.1016/j.cbpa.2005.04.001 · DOI
Krier M., 2006, J. Chem. Inf. Model., 46, 512, 10.1021/ci050352v · DOI
Steindl T. M., 2007, J. Chem. Inf. Model., 47, 563, 10.1021/ci600321m · doi:10.1021/ci600321m
Schuffenhauer A., 2006, J. Chem. Inf. Model., 46, 525, 10.1021/ci0503558 · DOI
Schuffenhauer A., 2007, J. Chem. Inf. Model., 47, 325, 10.1021/ci6004004 · DOI
Marcou G., 2007, J. Chem. Inf. Model., 47, 195, 10.1021/ci600342e · DOI · Paper page
Klon A. E., 2004, J. Med. Chem., 47, 2743, 10.1021/jm030363k · DOI
Klon A. E., 2004, J. Chem. Inf. Comput. Sci., 44, 2216, 10.1021/ci0497861 · doi:10.1021/ci0497861
Klon A. E., 2004, J. Med. Chem., 47, 4356, 10.1021/jm049970d · doi:10.1021/jm049970d
de Graaf C., 2008, J. Med. Chem., 51, 4978, 10.1021/jm800710x · DOI
Whittle M., 2006, J. Chem. Inf. Model., 46, 2206, 10.1021/ci0496144 · DOI
Nigsch F., 2008, J. Chem. Inf. Model., 48, 2313, 10.1021/ci800079x · DOI
Hert J., 2008, J. Chem. Inf. Model., 48, 755, 10.1021/ci8000259 · DOI
Nisius B., 2009, J. Chem. Inf. Model., 49, 1347, 10.1021/ci900087y · DOI
van Hoorn W. P., 2009, J. Chem. Inf. Model., 49, 2211, 10.1021/ci900072g · DOI
Prathipati P., 2008, J. Chem. Inf. Model., 48, 2362, 10.1021/ci800143n · DOI
Papadatos G., 2009, J. Chem. Inf. Model., 49, 195, 10.1021/ci800302g · DOI
Muchmore S. W., 2008, J. Chem. Inf. Model., 48, 941, 10.1021/ci7004498 · DOI
Liu R., 2008, J. Chem. Inf. Model., 48, 542, 10.1021/ci700372s · DOI
Wale N., 2008, J. Chem. Inf. Model., 48, 730, 10.1021/ci700369e · DOI
Sullivan D. C., 2008, J. Chem. Inf. Model., 48, 817, 10.1021/ci700439z · DOI
Wale N., 2009, J. Chem. Inf. Model., 49, 2190, 10.1021/ci9000376 · DOI
Lounkine E., 2009, J. Chem. Inf. Model., 49, 561, 10.1021/ci800377n · doi:10.1021/ci800377n
Baldi P., 2007, J. Chem. Inf. Model., 47, 2098, 10.1021/ci700200n · DOI
Hattori K., 2008, J. Chem. Inf. Model., 48, 135, 10.1021/ci7002686 · doi:10.1021/ci7002686

Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science · doi:10.1039/c7sc02664a · Cited by 3,107
ZINC 15 – Ligand Discovery for Everyone
Sterling, T., & Irwin, J. J. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00559 · Cited by 3,224
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00572 · Cited by 3,106
A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell · doi:10.1016/j.cell.2020.01.021 · Cited by 2,463
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310
QSAR Modeling: Where have you been? Where are you going to?
Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., Dearden, J., Gramatica, P., Martin, Y. C., Todeschini, R., et al. (2014) · Journal of Medicinal Chemistry · doi:10.1021/jm4004285 · Cited by 2,164
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., et al. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00237 · Cited by 1,923
Planning chemical syntheses with deep neural networks and symbolic AI
Segler, M. H. S., Preuss, M., & Waller, M. P. (2018) · Nature · doi:10.1038/nature25978 · Cited by 1,847
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., & Riley, P. (2016) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-016-9938-8 · Cited by 1,631
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
Segler, M. H. S., Kogej, T., Tyrchan, C., & Waller, M. P. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00512 · Cited by 1,537
DeepTox: toxicity prediction using deep learning
Mayr, A., Klambauer, G., Unterthiner, T., & Hochreiter, S. (2016) · Frontiers in Environmental Science · doi:10.3389/fenvs.2015.00080 · Cited by 1,081
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach
Gómez-Bombarelli, R., Aguilera-Iparraguirre, J., Hirzel, T. D., Duvenaud, D., Maclaurin, D., Blood-Forsythe, M. A., Chae, H. S., Einzinger, M., Ha, D.-G., Wu, T., et al. (2016) · Nature Materials · doi:10.1038/nmat4717 · Cited by 1,060
Drug discovery with explainable artificial intelligence
Jiménez-Luna, J., Grisoni, F., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00236-4 · Cited by 1,063
Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra
Dührkop, K., Nothias, L.-F., Fleischauer, M., Reher, R., Ludwig, M., Hoffmann, M. A., Petras, D., Gerwick, W. H., Rousu, J., Dorrestein, P. C., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-020-0740-8 · Cited by 860
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849
Automated design of ligands to polypharmacological profiles
Besnard, J., Ruda, G. F., Setola, V., Abecassis, K., Rodriguiz, R. M., Huang, X.-P., Norval, S., Sassano, M. F., Shin, A. I., Webster, L. A., et al. (2012) · Nature · doi:10.1038/nature11691 · Cited by 832
Deep learning for computational chemistry
Goh, G. B., Hodas, N. O., & Vishnu, A. (2017) · Journal of Computational Chemistry · doi:10.1002/jcc.24764 · Cited by 809
Low Data Drug Discovery with One-Shot Learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.6b00367 · Cited by 799
Augmenting large language models with chemistry tools
M. Bran, A., Cox, S., Schilter, O., Baldassari, C., White, A. D., & Schwaller, P. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00832-8 · Cited by 794
The Chemical Space Project
Reymond, J.-L. (2015) · Accounts of Chemical Research · doi:10.1021/ar500432k · Cited by 720
Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions
Rodríguez-Pérez, R., & Bajorath, J. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00314-0 · Cited by 718
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Machine Learning in Computer-Aided Synthesis Planning
Coley, C. W., Green, W. H., & Jensen, K. F. (2018) · Accounts of Chemical Research · doi:10.1021/acs.accounts.8b00087 · Cited by 704
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017) · eLife · doi:10.7554/elife.26726 · Cited by 666
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error
Faber, F. A., Hutchison, L., Huang, B., Gilmer, J., Schoenholz, S. S., Dahl, G. E., Vinyals, O., Kearnes, S., Riley, P. F., & von Lilienfeld, O. A. (2017) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.7b00577 · Cited by 651
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
Large-scale chemical language representations capture molecular structure and properties
Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., & Das, P. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00580-7 · Cited by 590
Molecular representations in AI-driven drug discovery: a review and practical guide
David, L., Thakkar, A., Mercado, R., & Engkvist, O. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00460-5 · Cited by 587
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S. (2018) · Chemical Science · doi:10.1039/c8sc00148k · Cited by 584
Deep Learning in Chemistry
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565
Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction
Segler, M. H. S., & Waller, M. P. (2017) · Chemistry – A European Journal · doi:10.1002/chem.201605499 · Cited by 548
NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products
Kim, H. W., Wang, M., Leber, C. A., Nothias, L.-F., Reher, R., Kang, K. B., van der Hooft, J. J. J., Dorrestein, P. C., Gerwick, W. H., & Cottrell, G. W. (2021) · Journal of Natural Products · doi:10.1021/acs.jnatprod.1c00399 · Cited by 551
Predicting drug response and synergy using a deep learning model of human cancer cells
Kuenzi, B. M., Park, J., Fong, S. H., Sanchez, K. S., Lee, J., Kreisberg, J. F., Ma, J., & Ideker, T. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.09.014 · Cited by 544
A practical guide to large-scale docking
Bender, B. J., Gahbauer, S., Luttens, A., Lyu, J., Webb, C. M., Stein, R. M., Fink, E. A., Balius, T. E., Carlsson, J., Irwin, J. J., et al. (2021) · Nature Protocols · doi:10.1038/s41596-021-00597-z · Cited by 546
Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds
Proschak, E., Stark, H., & Merk, D. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b00760 · Cited by 498
REINVENT 2.0: An AI Tool for De Novo Drug Design
Blaschke, T., Arús-Pous, J., Chen, H., Margreitter, C., Tyrchan, C., Engkvist, O., Papadopoulos, K., & Patronov, A. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c00915 · Cited by 496
Convolutional embedding of attributed molecular graphs for physical property prediction
Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., & Jensen, K. F. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00601 · Cited by 470
Accelerating materials discovery using artificial intelligence, high performance computing and robotics
Pyzer-Knapp, E. O., Pitera, J. W., Staar, P. W. J., Takeda, S., Laino, T., Sanders, D. P., Sexton, J., Smith, J. R., & Curioni, A. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00765-z · Cited by 465
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. (2022) · Nature Protocols · doi:10.1038/s41596-021-00659-2 · Cited by 397
NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty543 · Cited by 381
SCScore: Synthetic Complexity Learned from a Reaction Corpus
Coley, C. W., Rogers, L., Green, W. H., & Jensen, K. F. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00622 · Cited by 381
Application of Generative Autoencoder in De Novo Molecular Design
Blaschke, T., Olivecrona, M., Engkvist, O., Bajorath, J., & Chen, H. (2018) · Molecular Informatics · doi:10.1002/minf.201700123 · Cited by 411
Drug–target affinity prediction using graph neural network and contact maps
Jiang, M., Li, Z., Zhang, S., Wang, S., Wang, X., Yuan, Q., & Wei, Z. (2020) · RSC Advances · doi:10.1039/d0ra02297g · Cited by 372
Graph Convolutional Neural Networks for Predicting Drug-Target Interactions
Torng, W., & Altman, R. B. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00628 · Cited by 371
Machine learning–assisted molecular design and efficiency prediction for high-performance organic photovoltaic materials
Sun, W., Zheng, Y., Yang, K., Zhang, Q., Shah, A. A., Wu, Z., Sun, Y., Feng, L., Chen, D., Xiao, Z., et al. (2019) · Science Advances · doi:10.1126/sciadv.aay4275 · Cited by 344
Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development
Sarkar, C., Das, B., Rawat, V. S., Wahlang, J. B., Nongpiur, A., Tiewsoh, I., Lyngdoh, N. M., Das, D., Bidarolli, M., & Sony, H. T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24032026 · Cited by 347
Exposing the Limitations of Molecular Machine Learning with Activity Cliffs
van Tilborg, D., Alenicheva, A., & Grisoni, F. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01073 · Cited by 337
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Koutsoukas, A., Monaghan, K. J., Li, X., & Huan, J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0226-y · Cited by 315
Covalent docking of large libraries for the discovery of chemical probes
London, N., Miller, R. M., Krishnan, S., Uchida, K., Irwin, J. J., Eidam, O., Gibold, L., Cimermančič, P., Bonnet, R., Shoichet, B. K., et al. (2014) · Nature Chemical Biology · doi:10.1038/nchembio.1666 · Cited by 274
Mol-CycleGAN: a generative model for molecular optimization
Maziarka, Ł., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., & Warchoł, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-019-0404-1 · Cited by 275
An artificial intelligence accelerated virtual screening platform for drug discovery
(2024) · doi:10.1038/s41467-024-52061-7 · Cited by 259
The transformational role of GPU computing and deep learning in drug discovery
Pandey, M., Fernandez, M., Gentile, F., Isayev, O., Tropsha, A., Stern, A. C., & Cherkasov, A. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00463-x · Cited by 255
Deep Learning Based Regression and Multiclass Models for Acute Oral Toxicity Prediction with Automatic Chemical Feature Extraction
Xu, Y., Pei, J., & Lai, L. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00244 · Cited by 250
iPSC-Based Compound Screening and In Vitro Trials Identify a Synergistic Anti-amyloid β Combination for Alzheimer’s Disease
Kondo, T., Imamura, K., Funayama, M., Tsukita, K., Miyake, M., Ohta, A., Woltjen, K., Nakagawa, M., Asada, T., Arai, T., et al. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.10.109 · Cited by 221
Generative molecular design in low data regimes
Moret, M., Friedrich, L., Grisoni, F., Merk, D., & Schneider, G. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-0160-y · Cited by 219
MSNovelist: de novo structure generation from mass spectra
Stravs, M. A., Dührkop, K., Böcker, S., & Zamboni, N. (2022) · Nature Methods · doi:10.1038/s41592-022-01486-3 · Cited by 226
Bayer’s in silico ADMET platform: a journey of machine learning over the past two decades
Göller, A. H., Kuhnke, L., Montanari, F., Bonin, A., Schneckener, S., ter Laak, A., Wichard, J., Lobell, M., & Hillisch, A. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2020.07.001 · Cited by 215
Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling
Wawer, M. J., Li, K., Gustafsdottir, S. M., Ljosa, V., Bodycombe, N. E., Marton, M. A., Sokolnicki, K. L., Bray, M.-A., Kemp, M. M., Winchester, E., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1410933111 · Cited by 196
A general model to predict small molecule substrates of enzymes based on machine and deep learning
Kroll, A., Ranjan, S., Engqvist, M. K. M., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-38347-2 · Cited by 193
Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning
Kroll, A., Rousset, Y., Hu, X.-P., Liebrand, N. A., & Lercher, M. J. (2023) · Nature Communications · doi:10.1038/s41467-023-39840-4 · Cited by 186
Combining generative artificial intelligence and on-chip synthesis for de novo drug design
Grisoni, F., Huisman, B. J. H., Button, A. L., Moret, M., Atz, K., Merk, D., & Schneider, G. (2021) · Science Advances · doi:10.1126/sciadv.abg3338 · Cited by 190
Structure-based drug design with geometric deep learning
Isert, C., Atz, K., & Schneider, G. (2023) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2023.102548 · Cited by 182
Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
Friederich, P., dos Passos Gomes, G., De Bin, R., Aspuru-Guzik, A., & Balcells, D. (2020) · Chemical Science · doi:10.1039/d0sc00445f · Cited by 179
Generative machine learning for de novo drug discovery: A systematic review
Martinelli, D. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105403 · Cited by 177
Proteochemometric modeling as a tool to design selective compounds and for extrapolating to novel targets
van Westen, G. J. P., Wegner, J. K., IJzerman, A. P., van Vlijmen, H. W. T., & Bender, A. (2011) · MedChemComm · doi:10.1039/c0md00165a · Cited by 176
Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects
Liu, Y., Tan, X., Liang, J., Han, H., Xiang, P., & Yan, W. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202214271 · Cited by 174
Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets
Marchese Robinson, R. L., Palczewska, A., Palczewski, J., & Kidley, N. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00753 · Cited by 167
Leveraging Uncertainty in Machine Learning Accelerates Biological Discovery and Design
Hie, B., Bryson, B. D., & Berger, B. (2020) · Cell Systems · doi:10.1016/j.cels.2020.09.007 · Cited by 154
DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143
Molecular fingerprint-based artificial neural networks QSAR for ligand biological activity predictions
Myint, K.-Z., Wang, L., Tong, Q., & Xie, X.-Q. (2012) · Molecular Pharmaceutics · doi:10.1021/mp300237z · Cited by 139
tmQM Dataset─Quantum Geometries and Properties of 86k Transition Metal Complexes
Balcells, D., & Skjelstad, B. B. (2020) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.0c01041 · Cited by 143
Deep learning allows genome-scale prediction of Michaelis constants from structural features
Kroll, A., Engqvist, M. K. M., Heckmann, D., & Lercher, M. J. (2021) · PLOS Biology · doi:10.1371/journal.pbio.3001402 · Cited by 132
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
Clark, A. M., Dole, K., Coulon-Spektor, A., McNutt, A., Grass, G., Freundlich, J. S., Reynolds, R. C., & Ekins, S. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00143 · Cited by 114
Potential neutralizing antibodies discovered for novel corona virus using machine learning
Magar, R., Yadav, P., & Barati Farimani, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84637-4 · Cited by 114
Effect of Increasing the Descriptor Set on Machine Learning Prediction of Small Molecule-Based Organic Solar Cells
Zhao, Z.-W., del Cueto, M., Geng, Y., & Troisi, A. (2020) · Chemistry of Materials · doi:10.1021/acs.chemmater.0c02325 · Cited by 102
Comparative Study of Multitask Toxicity Modeling on a Broad Chemical Space
Sosnin, S., Karlov, D., Tetko, I. V., & Fedorov, M. V. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00685 · Cited by 102
“OEstate descriptors which are based on electrostatic properties of atoms and bonds CDK2 (3D) 43 Chemistry development kit descriptors, version 2.0 Morgan ﬁngerprints 38,39 Morgan (circular) ﬁngerprints of radius two (which corresponds to ECFP4 38 ) calculated by RDKit.”
MethodInfluential
Effectiveness of molecular fingerprints for exploring the chemical space of natural products
Boldini, D., Ballabio, D., Consonni, V., Todeschini, R., Grisoni, F., & Sieber, S. A. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00830-3 · Cited by 97
DeepCPI: A Deep Learning-Based Framework for Large-Scale in Silico Drug Screening
Wan, F., Zhu, Y., Hu, H., Dai, A., Cai, X., Chen, L., Gong, H., Xia, T., Yang, D., Wang, M.-W., et al. (2019) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.04.003 · Cited by 92
Quantitative evaluation of explainable graph neural networks for molecular property prediction
Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns · doi:10.1016/j.patter.2022.100628 · Cited by 90
Annotating metabolite mass spectra with domain-inspired chemical formula transformers
Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R. J., & Coley, C. W. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00708-3 · Cited by 87
Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings
Nelson, C. A., Butte, A. J., & Baranzini, S. E. (2019) · Nature Communications · doi:10.1038/s41467-019-11069-0 · Cited by 85
Benchmarking data sets for the evaluation of virtual ligand screening methods: review and perspectives.
Lagarde, N., Zagury, J.-F., & Montes, M. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00090 · Cited by 85
Interpretation of QSAR Models by Coloring Atoms According to Changes in Predicted Activity: How Robust Is It?
Sheridan, R. P. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00825 · Cited by 84
Realizing private and practical pharmacological collaboration
Hie, B., Cho, H., & Berger, B. (2018) · Science · doi:10.1126/science.aat4807 · Cited by 76
Deep contrastive learning enables genome-wide virtual screening
Jia, Y., Gao, B., Tan, J., Zheng, J., Hong, X., Zhu, W., Tan, H., Xiao, Y., Tan, L., Cai, H., et al. (2026) · Science · doi:10.1126/science.ads9530 · Cited by 66
Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer’s disease
Cheng, F., Wang, F., Tang, J., Zhou, Y., Fu, Z., Zhang, P., Haines, J. L., Leverenz, J. B., Gan, L., Hu, J., et al. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101379 · Cited by 63
Machine learning in materials design: Algorithm and application*
Song, Z., Chen, X., Meng, F., Cheng, G., Wang, C., Sun, Z., & Yin, W.-J. (2020) · Chinese Physics B · doi:10.1088/1674-1056/abc0e3 · Cited by 63
Equivariant Graph Neural Networks for Toxicity Prediction
Cremer, J., Medrano Sandonas, L., Tkatchenko, A., Clevert, D.-A., & De Fabritiis, G. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.3c00032 · Cited by 62
Deep Learning for Prediction and Optimization of Fast-Flow Peptide Synthesis
(2020) · doi:10.1021/acscentsci.0c00979 · Cited by 61
Computational methods for prediction of in vitro effects of new chemical structures
Banerjee, P., Siramshetty, V. B., Drwal, M. N., & Preissner, R. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0162-2 · Cited by 60
Repurposing potential of FDA‐approved and investigational drugs for COVID‐19 targeting SARS‐CoV‐2 spike and main protease and validation by machine learning algorithm
Verma, A. K., & Aggarwal, R. (2021) · Chemical Biology & Drug Design · doi:10.1111/cbdd.13812 · Cited by 54
DeepLPI: a novel deep learning-based model for protein–ligand interaction prediction for drug repurposing
Wei, B., Zhang, Y., & Gong, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-23014-1 · Cited by 53
Reliable Prediction Errors for Deep Neural Networks Using Test-Time Dropout
Cortés-Ciriano, I., & Bender, A. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00297 · Cited by 51
Assessing Deep and Shallow Learning Methods for Quantitative Prediction of Acute Chemical Toxicity
Liu, R., Madore, M., Glover, K. P., Feasel, M. G., & Wallqvist, A. (2018) · Toxicological Sciences · doi:10.1093/toxsci/kfy111 · Cited by 47
Structural and thermodynamic framework for PIEZO1 modulation by small molecules
Jiang, W., Wijerathne, T. D., Zhang, H., Lin, Y.-C., Jo, S., Im, W., Lacroix, J. J., & Luo, Y. L. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310933120 · Cited by 44
DLTKcat: deep learning-based prediction of temperature-dependent enzyme turnover rates
Qiu, S., Zhao, S., & Yang, A. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad506 · Cited by 43
Estimation of acute oral toxicity in rat using local lazy learning
Lu, J., Peng, J., Wang, J., Shen, Q., Bi, Y., Gong, L., Zheng, M., Luo, X., Zhu, W., Jiang, H., et al. (2014) · Journal of Cheminformatics · doi:10.1186/1758-2946-6-26 · Cited by 42

Authors: Rogers, D., & Hahn, M.
Venue: Journal of Chemical Information and Modeling
Publisher: American Chemical Society (ACS)
Published: 2010-04-28
Volume: 50
Issue: 5
Pages: 742-754
ISSN: 1549-9596 (ISSN-L)
1549-960X
DOI: 10.1021/ci100050t
OpenAlex: W1988037271
Cited by: 7,524
References: 2
FWCI: 87.11
Type: journal article
Language: en
Open access: closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.

Extended-Connectivity Fingerprints

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Self-check

Citations

Cites

Cited by

Metadata

Record

About these checks

Data sources