Planning chemical syntheses with deep neural networks and symbolic AI
Open accessAbstract
To plan the syntheses of small organic molecules, chemists use retrosynthesis, a problem-solving technique in which target molecules are recursively transformed into increasingly simpler precursors. Computer-aided retrosynthesis would be a valuable tool but at present it is slow and provides results of unsatisfactory quality. Here we use Monte Carlo tree search and symbolic artificial intelligence (AI) to discover retrosynthetic routes. We combined Monte Carlo tree search with an expansion policy network that guides the search, and a filter network to pre-select the most promising retrosynthetic steps. These deep neural networks were trained on essentially all reactions ever published in organic chemistry. Our system solves for almost twice as many molecules, thirty times faster than the traditional computer-aided search method, which is based on extracted rules and hand-designed heuristics. In a double-blind AB test, chemists on average considered our computer-generated routes to be equivalent to reported literature routes.
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
Cites
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.
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612 · see citing works
Neese, F. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.81 · Cited by 13,611 · see citing works
(2012) · Cited by 9,062 · see citing works
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,597 · see citing works
Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015) · arXiv · doi:10.48550/arxiv.1511.07289 · Cited by 4,513 · see citing works
Bandit Based Monte-Carlo Planning
Kocsis, L., & Szepesvari, C. (2006) · European Conference on Machine Learning · doi:10.1007/11871842_29 · Cited by 4,073 · see citing works
A Survey of Monte Carlo Tree Search Methods
Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012) · IEEE Transactions on Computational Intelligence and AI in Games · doi:10.1109/tciaig.2012.2186810 · Cited by 2,970 · see citing works
Convergence Results for Neural Networks via Electrodynamics
Panigrahy, R., Rahimi, A., Sachdeva, S., et al. (2018) · Leibniz international proceedings in informatics · doi:10.4230/lipics.itcs.2018.22 · Cited by 2,848 · see citing works
Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550 · see citing works
Theano: A Python framework for fast computation of mathematical expressionsPreprint
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al. (2016) · arXiv.org · doi:10.48550/arxiv.1605.02688 · Cited by 2,438 · see citing works
Ertl, P., & Schuffenhauer, A. (2009) · Journal of Cheminformatics · doi:10.1186/1758-2946-1-8 · Cited by 1,939 · see citing works
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
Coulom, R. (2006) · Computers and Games · doi:10.1007/978-3-540-75538-8_7 · Cited by 1,819 · see citing works
Static Analysis of Shape in TensorFlow ProgramsPreprint
The Theano Development Team, Al‐Rfou, R., Alain, G., et al. (2016) · arXiv (Cornell University) · doi:10.4230/lipics.ecoop.2020.15 · Cited by 1,629 · see citing works
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 · see citing works
Self-citationTraining Very Deep NetworksPreprint
Srivastava, R. K., Greff, K., & Schmidhuber, J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1507.06228 · Cited by 1,453 · see citing works
A Framework For Representing Knowledge
Minsky, M. (1979) · De Gruyter eBooks · doi:10.1515/9783110858778-003 · Cited by 867 · see citing works
Prediction of Organic Reaction Outcomes Using Machine Learning
Coley, C. W., Barzilay, R., Jaakkola, T., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00064 · Cited by 755 · see citing works
The Oxford Handbook of Thinking and Reasoning
Holyoak, K. J., & Morrison, R. (2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199734689.001.0001 · Cited by 702 · see citing works
Butina, D. (1999) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci9803381 · Cited by 658 · see citing works
The logic of chemical synthesis
Huynh‐Dinh, T. (1996) · Biochimie · doi:10.1016/0300-9084(96)89514-9 · Cited by 651 · see citing works
A robustness screen for the rapid assessment of chemical reactions
Collins, K. D., & Glorius, F. (2013) · Nature Chemistry · doi:10.1038/nchem.1669 · Cited by 626 · see citing works
Computer‐Assisted Synthetic Planning: The End of the Beginning
Szymkuć, S., Gajewska, E. P., Klucznik, T., Molga, K., Dittwald, P., Startek, M., Bajczyk, M., & Grzybowski, B. A. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201506101 · Cited by 611 · see citing works
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 · see citing works
Self-citationRetrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models
Liu, B., Ramsundar, B., Kawthekar, P., Shi, J., Gomes, J., Luu Nguyen, Q., Ho, S., Sloane, J., Wender, P., & Pande, V. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00303 · Cited by 536 · see citing works
A Framework for Representing Knowledge
Minsky, M. (1997) · The MIT Press eBooks · doi:10.7551/mitpress/4626.003.0005 · Cited by 535 · see citing works
LXIII.—A synthesis of tropinone
Robinson, R. C. (1917) · Journal of the Chemical Society Transactions · doi:10.1039/ct9171100762 · Cited by 532 · see citing works
Steinbeck, C., Hoppe, C., Kühn, S., Floris, M., Guha, R., & Willighagen, E. (2006) · Current Pharmaceutical Design · doi:10.2174/138161206777585274 · Cited by 463 · see citing works
Organic Synthesis: March of the Machines
Ley, S. V., Fitzpatrick, D. E., Ingham, R. J., & Myers, R. M. (2015) · Angewandte Chemie International Edition · doi:10.1002/anie.201410744 · Cited by 455 · see citing works
Neural Networks for the Prediction of Organic Chemistry Reactions
Wei, J. N., Duvenaud, D., & Aspuru‐Guzik, A. (2016) · ACS Central Science · doi:10.1021/acscentsci.6b00219 · Cited by 436 · see citing works
Computer-Assisted Retrosynthesis Based on Molecular Similarity
Coley, C. W., Rogers, L., Green, W. H., & Jensen, K. F. (2017) · ACS Central Science · doi:10.1021/acscentsci.7b00355 · Cited by 403 · see citing works
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
Sheridan, R. P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400084k · Cited by 376 · see citing works
COMPUTING “ELO RATINGS” OF MOVE PATTERNS IN THE GAME OF GO1
Coulom, R. (2007) · ICGA Journal · doi:10.3233/icg-2007-30403 · Cited by 300 · see citing works
Computing organic stereoselectivity – from concepts to quantitative calculations and predictions
Peng, Q., Duarte, F., & Paton, R. S. (2016) · Chemical Society Reviews · doi:10.1039/c6cs00573j · Cited by 238 · see citing works
Route Designer: A Retrosynthetic Analysis Tool Utilizing Automated Retrosynthetic Rule Generation
Law, J. B., Zsoldos, Z., Simon, A., Reid, D., Liu, Y., Khew, S. Y., Johnson, A. P., Major, S., Wade, R. A., & Ando, H. Y. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800228y · Cited by 228 · see citing works
Multi-armed bandits with episode context
Rosin, C. D. (2011) · Annals of Mathematics and Artificial Intelligence · doi:10.1007/s10472-011-9258-6 · Cited by 213 · see citing works
Schneider, N., Lowe, D. M., Sayle, R. A., & Landrum, G. A. (2014) · Journal of Chemical Information and Modeling · doi:10.1021/ci5006614 · Cited by 196 · see citing works
Modelling Chemical Reasoning to Predict and Invent Reactions
Segler, M. H. S., & Waller, M. P. (2016) · Chemistry - A European Journal · doi:10.1002/chem.201604556 · Cited by 196 · see citing works
Self-citationLearning to Predict Chemical Reactions
Kayala, M. A., Azencott, C., Chen, J. H., & Baldi, P. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200207y · Cited by 190 · see citing works
Kayala, M. A., & Baldi, P. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3003039 · Cited by 186 · see citing works
Computer-aided organic synthesis
Todd, M. H. (2005) · Chemical Society Reviews · doi:10.1039/b104620a · Cited by 183 · see citing works
Structure and reaction based evaluation of synthetic accessibility
Boda, K., Seidel, T., & Gasteiger, J. (2007) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-006-9099-2 · Cited by 178 · see citing works
De Novo Design at the Edge of Chaos
Schneider, P., & Schneider, G. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01849 · Cited by 167 · see citing works
The enumeration of chemical space
Reymond, J., Ruddigkeit, L., Blum, L. C., & van Deursen, R. (2012) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1104 · Cited by 145 · see citing works
Computer‐Assisted Planning of Organic Syntheses: The Second Generation of Programs
Ihlenfeldt, W., & Gasteiger, J. (1996) · Angewandte Chemie International Edition in English · doi:10.1002/anie.199526131 · Cited by 138 · see citing works
Bøgevig, A., Federsel, H., Huerta, F., Hutchings, M. G., Kraut, H., Langer, T., Löw, P., Oppawsky, C., Rein, T., & Saller, H. (2015) · Organic Process Research & Development · doi:10.1021/op500373e · Cited by 137 · see citing works
Satoh, H., & Funatsu, K. (1995) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00023a005 · Cited by 128 · see citing works
Training Deep Convolutional Neural Networks to Play Go
Clark, C., & Storkey, A. (2015) · Cited by 125 · see citing works
End-to-End Differentiable ProvingPreprint
Rocktäschel, T., & Riedel, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.11040 · Cited by 117 · see citing works
Computer‐aided synthesis design: 40 years on
Cook, A. P. F., Johnson, A. P., Law, J. B., Mirzazadeh, M., Ravitz, O., & Simon, A. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.61 · Cited by 115 · see citing works
Rajwanshi, V. K., Håkansson, A. E., Sørensen, M. D., Pitsch, S., Singh, S. K., Kumar, R., Nielsen, P., & Wengel, J. (2000) · Angewandte Chemie International Edition · doi:10.1002/(sici)1521-3773(20000502)39:9 · Cited by 112 · see citing works
Move Evaluation in Go Using Deep Convolutional Neural NetworksPreprint
Maddison, C. J., Huang, A., Sutskever, I., & Silver, D. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6564 · Cited by 110 · see citing works
Dual-Process Theories of Deductive Reasoning: Facts and Fallacies
Evans, J. S. B. T. (2012) · Oxford University Press eBooks · doi:10.1093/oxfordhb/9780199734689.013.0008 · Cited by 108 · see citing works
Mining Electronic Laboratory Notebooks: Analysis, Retrosynthesis, and Reaction Based Enumeration
Christ, C. D., Zentgraf, M., & Kriegl, J. M. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci300116p · Cited by 97 · see citing works
Monte-Carlo Tree Search Solver
Winands, M. H. M., Björnsson, Y., & Saito, J.-T. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87608-3_3 · Cited by 90 · see citing works
Gelernter, H. L., Rose, J. R., & Chen, C. (1990) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00068a023 · Cited by 88 · see citing works
Expert System for Predicting Reaction Conditions: The Michael Reaction Case
Marcou, G., Aires de Sousa, J., Latino, D. A. R. S., de Luca, A., Horvath, D., Rietsch, V., & Varnek, A. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/ci500698a · Cited by 86 · see citing works
Enabling future drug discovery by de novo design
Hartenfeller, M., & Schneider, G. (2011) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.49 · Cited by 84 · see citing works
Knowledge-Based Approach to <i>de Novo</i> Design Using Reaction Vectors
Patel, H., Bodkin, M. J., Chen, B., & Gillet, V. J. (2009) · Journal of Chemical Information and Modeling · doi:10.1021/ci800413m · Cited by 82 · see citing works
Bayesian pattern ranking for move prediction in the game of Go
Stern, D., Herbrich, R., & Graepel, T. (2006) · International Conference on Machine Learning · doi:10.1145/1143844.1143954 · Cited by 75 · see citing works
Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers
Zhang, Q., & Aires‐de‐Sousa, J. (2005) · Journal of Chemical Information and Modeling · doi:10.1021/ci0502707 · Cited by 73 · see citing works
Concerning one system of classification and codification of organic reactions
Влэдуц, Г. Э. (1963) · Information Processing & Management · doi:10.1016/0020-0271(63)90013-5 · Cited by 63 · see citing works
Automatized Assessment of Protective Group Reactivity: A Step Toward Big Reaction Data Analysis
Lin, A. I., Madzhidov, T. I., Klimchuk, O., Nugmanov, R. I., Antipin, I. S., & Varnek, A. (2016) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.6b00319 · Cited by 51 · see citing works
Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural NetworksPreprint
Segler, M., Kogej, T., Tyrchan, C., & Waller, M. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.01329 · Cited by 49 · see citing works
Self-citationGeneric strategies for chemical space exploration
Andersen, J. L., Christoph Flamm, A., Merkle, D., & Stadler, P. F. (2014) · International Journal of Computational Biology and Drug Design · doi:10.1504/ijcbdd.2014.061649 · Cited by 40 · see citing works
Ugi, I., Bauer, J., Blomberger, C., Brandt, J., Dietz, A. J., Fontain, E., Gruber, B., von Scholley‐Pfab, A., Senff, A., & Stein, N. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00017a001 · Cited by 35 · see citing works
HORACE: An automatic system for the hierarchical classification of chemical reactions
Rose, J. R., & Gasteiger, J. (1994) · Journal of Chemical Information and Computer Sciences · doi:10.1021/ci00017a010 · Cited by 33 · see citing works
The ROBIA Program for Predicting Organic Reactivity
Socorro, I. M., & Goodman, J. M. (2006) · Journal of Chemical Information and Modeling · doi:10.1021/ci050379e · Cited by 30 · see citing works
Parsy, C., Alexandre, F.-R., Bidau, V., et al. (2015) · Bioorganic & Medicinal Chemistry Letters · doi:10.1016/j.bmcl.2015.09.009 · Cited by 27 · see citing works
Gini, A., Segler, M., Kellner, D., et al. (2015) · Chemistry - A European Journal · doi:10.1002/chem.201501314 · Cited by 27 · see citing works
Self-citationStructure–reactivity modeling using mixture-based representation of chemical reactions
Polishchuk, P., Madzhidov, T., Gimadiev, T., Bodrov, A., Nugmanov, R., & Varnek, A. (2017) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-017-0044-3 · Cited by 27 · see citing works
Machine learning of chemical reactivity from databases of organic reactions
Carrera, G. V. S. M., Gupta, S. V., & Aires‐de‐Sousa, J. (2009) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-009-9275-2 · Cited by 24 · see citing works
Schneider, N., Lowe, D. M., Sayle, R. A., et al. (2015) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5b00046 · Cited by 6 · see citing works
Nirogi, R. V. S., Badange, R., Reballi, V., et al. (2015) · Asian Journal of Chemistry · doi:10.14233/ajchem.2015.17783 · Cited by 6 · see citing works
Monte Carlo Connection Prover.Preprint
Färber, M., Kaliszyk, C., & Urban, J. (2016) · arXiv (Cornell University) · Cited by 3 · see citing works
Neural Networks for Video Game AI
Winands, M. H. M. (2015) · Research Publications (Maastricht University) · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 6
- [1][1] Clayden, J., Greeves, N., Warren, S. & Wothers, P. Organic chemistry, 2nd Ed. (Oxford University Press, 2008). -
- [2][2] Br¨ uckner, R. Reaktionsmechanismen: organische Reaktionen, Stereochemie, moderne Synthesemethoden (Springer-Verlag, 2014). -
- [5][5] Kurti, L. & Czak´ o, B. Strategic applications of named reactions in organic synthesis (Elsevier, 2005). -
- [20][20] Fick, R. Konzepte zur Syntheseplanung: Strukturelle ¨ Ahnlichkeit und strategische Bindungen . Ph.D. thesis, Friedrich-Alexander-Universit¨ at Erlangen-N¨ urnberg (1996). -
- [47][47] Sierra, M. A. & de la Torre, M. C. Dead ends and detours en route to total syntheses of the 1990s. Angewandte Chemie International Edition 39 , 1538-1559 (2000). -
- [53][53] Silver, D. Reinforcement Learning and Simulation-Based Search . Ph.D. thesis, University of Alberta (2009). -
Cited by
Sorted by citations
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968 · see citing works
Data-driven prediction of battery cycle life before capacity degradation
Severson, K. A., Attia, P. M., Jin, N., Perkins, N., Jiang, B., Yang, Z., Chen, M. H., Aykol, M., Herring, P. K., Fraggedakis, D., et al. (2019) · Nature Energy · doi:10.1038/s41560-019-0356-8 · Cited by 3,228 · see citing works
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228 · see citing works
On the Opportunities and Risks of Foundation Models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv · doi:10.48550/arxiv.2108.07258 · Cited by 2,676 · see citing works
The rise of deep learning in drug discovery
Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works
Scientific discovery in the age of artificial intelligence.
Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., et al. (2023) · Nature · doi:10.1038/s41586-023-06221-2 · Cited by 1,823 · see citing works
Artificial intelligence in drug discovery and development
Paul, D., Sanap, G., Shenoy, S., Kalyane, D., Kalia, K., & Tekade, R. K. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2020.10.010 · Cited by 1,695 · see citing works
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works
Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
Baricitinib as potential treatment for 2019-nCoV acute respiratory disease
Richardson, P., Griffin, I., Tucker, C., Smith, D., Oechsle, O., Phelan, A., Rawling, M., Savory, E., & Stebbing, J. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30304-4 · Cited by 1,456 · see citing works
Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J., Antonoglou, I., Hubert, T., Simonyan, K., Sifre, L., Schmitt, S., Guez, A., Lockhart, E., Hassabis, D., Graepel, T., et al. (2020) · Nature · doi:10.1038/s41586-020-03051-4 · Cited by 1,214 · see citing works
COVID-19: combining antiviral and anti-inflammatory treatments
Stebbing, J., Phelan, A., Griffin, I., Tucker, C., Oechsle, O., Smith, D., & Richardson, P. (2020) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(20)30132-8 · Cited by 1,117 · see citing works
Artificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works
Rethinking drug design in the artificial intelligence era
Schneider, P., Walters, W. P., Plowright, A. T., Sieroka, N., Listgarten, J., Goodnow, R. A., Fisher, J., Jansen, J. M., Duca, J. S., Rush, T. S., et al. (2020) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0050-3 · Cited by 894 · see citing works
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913 · see citing works
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 · see citing works
GuacaMol: Benchmarking Models for de Novo Molecular Design
Brown, N., Fiscato, M., Segler, M. H. S., & Vaucher, A. C. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00839 · Cited by 895 · see citing works
Self-citationAugmenting 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 · see citing works
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 · see citing works
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683 · see citing works
Prediction of higher-selectivity catalysts by computer-driven workflow and machine learning
Zahrt, A. F., Henle, J. J., Rose, B. T., Wang, Y., Darrow, W. T., & Denmark, S. E. (2019) · Science · doi:10.1126/science.aau5631 · Cited by 683 · see citing works
A graph-convolutional neural network model for the prediction of chemical reactivity
Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., & Jensen, K. F. (2019) · Chemical Science · doi:10.1039/c8sc04228d · Cited by 679 · see citing works
Machine Learning for Catalysis Informatics: Recent Applications and Prospects
Toyao, T., Maeno, Z., Takakusagi, S., Kamachi, T., Takigawa, I., & Shimizu, K.-I. (2020) · ACS Catalysis · doi:10.1021/acscatal.9b04186 · Cited by 617 · see citing works
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 · see citing works
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 · see citing works
Mater, A. C., & Coote, M. L. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00266 · Cited by 565 · see citing works
Discovering faster matrix multiplication algorithms with reinforcement learning
Fawzi, A., Balog, M., Huang, A., Hubert, T., Romera-Paredes, B., Barekatain, M., Novikov, A., R. Ruiz, F. J., Schrittwieser, J., Swirszcz, G., et al. (2022) · Nature · doi:10.1038/s41586-022-05172-4 · Cited by 478 · see citing works
Artificial Intelligence in Society
OECD (2019) · doi:10.1787/eedfee77-en · Cited by 487 · see citing works
Machine Learning Methods in Drug Discovery
Patel, L., Shukla, T., Huang, X., Ussery, D. W., & Wang, S. (2020) · Molecules · doi:10.3390/molecules25225277 · Cited by 460 · see citing works
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446 · see citing works
Using Machine Learning To Predict Suitable Conditions for Organic Reactions
Gao, H., Struble, T. J., Coley, C. W., Wang, Y., Green, W. H., & Jensen, K. F. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00357 · Cited by 431 · see citing works
Next-Generation Experimentation with Self-Driving Laboratories
Häse, F., Roch, L. M., & Aspuru-Guzik, A. (2019) · Trends in Chemistry · doi:10.1016/j.trechm.2019.02.007 · Cited by 426 · see citing works
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368 · see citing works
Nandy, A., Duan, C., Taylor, M. G., Liu, F., Steeves, A. H., & Kulik, H. J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00347 · Cited by 358 · see citing works
Deep learning in drug discovery: opportunities, challenges and future prospects
Lavecchia, A. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.07.006 · Cited by 353 · see citing works
Dimiduk, D. M., Holm, E. A., & Niezgoda, S. R. (2018) · Integrating Materials and Manufacturing Innovation · doi:10.1007/s40192-018-0117-8 · Cited by 347 · see citing works
Luo, K., Chen, X., Zheng, H., & Shi, Z. (2022) · Journal of Energy Chemistry · doi:10.1016/j.jechem.2022.06.049 · Cited by 292 · see citing works
Prediction of chemical reaction yields using deep learning
Schwaller, P., Vaucher, A. C., Laino, T., & Reymond, J.-L. (2021) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/abc81d · Cited by 291 · see citing works
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 · see citing works
A review of large language models and autonomous agents in chemistry
Ramos, M. C., Collison, C. J., & White, A. D. (2025) · Chemical Science · doi:10.1039/d4sc03921a · Cited by 274 · see citing works
The Integration of Artificial Intelligence into Clinical Practice
Karalis, V. D. (2024) · Applied Biosciences · doi:10.3390/applbiosci3010002 · Cited by 255 · see citing works
Integrating Machine Learning with Human Knowledge
Deng, C., Ji, X., Rainey, C., Zhang, J., & Lu, W. (2020) · iScience · doi:10.1016/j.isci.2020.101656 · Cited by 243 · see citing works
Artificial intelligence for aging and longevity research: Recent advances and perspectives
Zhavoronkov, A., Mamoshina, P., Vanhaelen, Q., Scheibye-Knudsen, M., Moskalev, A., & Aliper, A. (2019) · Ageing Research Reviews · doi:10.1016/j.arr.2018.11.003 · Cited by 246 · see citing works
Shape-Based Generative Modeling for de Novo Drug Design
Skalic, M., Jiménez, J., Sabbadin, D., & De Fabritiis, G. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00706 · Cited by 229 · see citing works
Accelerating antibiotic discovery through artificial intelligence
Melo, M. C. R., Maasch, J. R. M. A., & de la Fuente-Nunez, C. (2021) · Communications Biology · doi:10.1038/s42003-021-02586-0 · Cited by 234 · see citing works
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216 · see citing works
Prediction of drug efficacy from transcriptional profiles with deep learning
Zhu, J., Wang, J., Wang, X., Gao, M., Guo, B., Gao, M., Liu, J., Yu, Y., Wang, L., Kong, W., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00946-z · Cited by 193 · see citing works
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 · see citing works
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 · see citing works
Artificial intelligence in chemistry and drug design
Brown, N., Ertl, P., Lewis, R., Luksch, T., Reker, D., & Schneider, N. (2020) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-020-00317-x · Cited by 165 · see citing works
Looking beyond the hype: applied AI and machine learning in translational medicine
Toh, T. S., Dondelinger, F., & Wang, D. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.08.027 · Cited by 159 · see citing works
Semi-supervised machine-learning classification of materials synthesis procedures
Huo, H., Rong, Z., Kononova, O., Sun, W., Botari, T., He, T., Tshitoyan, V., & Ceder, G. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0204-1 · Cited by 151 · see citing works
Zhao, J., Feng, X., Wang, J., Lian, Y., Ouyang, M., & Burke, A. F. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121949 · Cited by 152 · see citing works
The Advent of Generative Chemistry
Vanhaelen, Q., Lin, Y.-C., & Zhavoronkov, A. (2020) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.0c00088 · Cited by 151 · see citing works
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 · see citing works
Futures of artificial intelligence through technology readiness levels
Martínez-Plumed, F., Gómez, E., & Hernández-Orallo, J. (2021) · Telematics and Informatics · doi:10.1016/j.tele.2020.101525 · Cited by 130 · see citing works
Application of machine learning for advanced material prediction and design
Chan, C. H., Sun, M., & Huang, B. (2022) · EcoMat · doi:10.1002/eom2.12194 · Cited by 123 · see citing works
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103 · see citing works
AlQuraishi, M., & Sorger, P. K. (2021) · Nature Methods · doi:10.1038/s41592-021-01283-4 · Cited by 96 · see citing works
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 · see citing works
Lehr, S. A., Caliskan, A., Liyanage, S., & Banaji, M. R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2404328121 · Cited by 66 · see citing works
Deep learning for low-data drug discovery: Hurdles and opportunities
van Tilborg, D., Brinkmann, H., Criscuolo, E., Rossen, L., Özçelik, R., & Grisoni, F. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102818 · Cited by 61 · see citing works
Brown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59 · see citing works
Incentivizing the Sharing of Healthcare Data in the AI Era
Panagopoulos, A., Minssen, T., Sideri, K., Yu, H., & Compagnucci, M. C. (2022) · Computer Law & Security Review · doi:10.1016/j.clsr.2022.105670 · Cited by 39 · see citing works
Pepe, M., Hesami, M., de la Cerda, K. A., Perreault, M. L., Hsiang, T., & Jones, A. M. P. (2023) · Biotechnology Advances · doi:10.1016/j.biotechadv.2023.108247 · Cited by 36 · see citing works
Machine Learning in Drug Discovery
Hochreiter, S., Klambauer, G., & Rarey, M. (2018) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.8b00478 · Cited by 36 · see citing works
Siebert, M., Krennrich, G., Seibicke, M., Siegle, A. F., & Trapp, O. (2019) · Chemical Science · doi:10.1039/c9sc04591k · Cited by 32 · see citing works
Formatting biological big data for modern machine learning in drug discovery
Duran‐Frigola, M., Fernández‐Torras, A., Bertoni, M., & Aloy, P. (2019) · WIREs Computational Molecular Science · doi:10.1002/wcms.1408 · Cited by 25 · see citing works
Evaluating Large Language Models in Scientific Discovery
Song, Z., Lu, J., Du, Y., Yu, B., Pruyn, T. M., Huang, Y., Guo, K., Luo, X., Qu, Y., Qu, Y., et al. (2025) · arXiv · Cited by 3 · see citing works
- Authors
- Segler, M. H. S., Preuss, M., & Waller, M. P.
- Affiliations
- University of Münster (DE)
- Shanghai University (CN)
- Corresponding author
- Segler, Marwin H. S.
- Waller, Mark P.
- Venue
- Nature
- Published
- 2018-03
- Volume
- 555
- Issue
- 7698
- Pages
- 604-610
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2747592475
- Cited by
- 1,847
- References
- 77
- Citations by year
- 2026: 163 · 2025: 232 · 2024: 217 · 2023: 230 · 2022: 215 · 2021: 270 · 2020: 265 · 2019: 188 · 2018: 66 · 2017: 1
- FWCI
- 147.91
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft (SFB858)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence in Games
- Keywords
- retrosynthesis, computer-aided synthesis planning, Monte Carlo tree search, symbolic AI, filter networks, organic reactions, deep neural network, chemical reaction prediction, small organic molecules
- MeSH terms
- Artificial Intelligence, Chemistry, Organic, Monte Carlo Method, Neural Networks, Computer, Chemistry Techniques, Synthetic
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
