Publications

Mahjour, B.A., Lu, J., Fromer, J.C., Casetti, N., Coley, C.W.. Ideation and evaluation of novel multicomponent reactions via mechanistic network analysis and automation. chemrxiv. (2024) preprint: 10.26434/chemrxiv-2024-qfjh9-v2

Preprint predictive chemistry automation

Sun, M., Lo, A., Gao, W., Guo, M., Thost, V., Chen, J., Coley, C.W., Matusik, W.. Syntax-Guided Procedural Synthesis of Molecules. arXiv. (2024) preprint: arXiv:2409.05873

Preprint predictive chemistry design and optimization

Meijer, D., Beniddir, M.A., Coley, C.W., Mejri, Y.M., Ozturk, M., van der Hooft, J.J.J., Medema, M.H., Skiredj, A.. Empowering natural product science with AI: leveraging multimodal data and knowledge graphs. Nat. Prod. Rep.. (2024) DOI: 10.1039/D4NP00008K

data

Yu, K., Roh, J., Li, Z., Gao, W., Wang, R., Coley, C.W.. Double-ended synthesis planning with goal-constrained bidirectional search. arXiv. (2024) preprint: arXiv.2407.06334

Preprint predictive chemistry

Luo, S., Gao, W., Wu, Z., Peng, J. Coley, C.W., Ma, J.. Projecting molecules into synthesizable chemical spaces. Proceedings of the 41st International Conference on Machine Learning (ICML). (2024)

Preprint molecular representation design and optimization

Keto, A., Guo, T., Underdue, M., Stuyver, T., Coley, C.W., Zhang, X., Krenske, E.H., Wiest, O.. Data-efficient, chemistry-aware machine learning predictions of Diels-Alder reaction outcomes. J. Am. Chem. Soc. 146(23), 16052-16061. (2024) DOI: 10.1021/jacs.4c03131

predictive chemistry molecular representation

Raghavan, P., Rago, A.J., Verma, P., Hassan, M.M., Goshu, G.M., Dombrowski, A.W., Pandey, A., Coley, C.W., Wang, Y.. Incorporating synthetic accessibility in drug design: Predicting reaction yields of Suzuki cross-couplings by leveraging AbbVie’s 15-year parallel library data set. J. Am. Chem. Soc. 146(22), 15070-15084. (2024) DOI: 10.1021/jacs.4c00098

predictive chemistry

Mahjour, B.A., Coley, C.W.. Automation of air-free synthesis. Nat. Rev. Chem. 8, 300–301. (2024) DOI: 10.1038/s41570-024-00599-x

automation

Ai, Q., Meng, F., Shi, J., Pelkie, B., Coley, C.W.. Extracting structured data from organic synthesis procedures using a fine-tuned large language model. Digital Discov.. (2024) DOI: 10.1039/D4DD00091A

Preprint data

Fan, V., Qian, Y., Wang, A., Wang, A., Coley, C.W., Barzilay, R.. OpenChemIE: An information extraction toolkit for chemistry literature. J. Chem. Inf. Model. 64(14), 5521-5534. (2024) DOI: 10.1021/acs.jcim.4c00572

Preprint data

Subramanian, A., Gao, W., Barzilay, R., Grossman, J.C., Jaakkola, T., Jegelka, S., Li, M., Li, J., Matusik, W., Olivetti, E., Coley, C.W., Gomez-Bombarelli, R.. Closing the execution gap in generative AI for chemicals and materials: freeways or safeguards. An MIT Exploration of Generative AI. (2024) DOI: 10.21428/e4baedd9.92e511e3

molecular representation design and optimization predictive chemistry automation data

Mahjour, B.A., Coley, C.W.. RDCanon: A python package for canonicalizing the order of tokens in SMARTS queries. J. Chem. Inf. Model. 64(8), 2948–2954. (2024) DOI: 10.1021/acs.jcim.4c00138

predictive chemistry data

Joung, J.F., Fong, M.H., Roh, J., Tu, Z., Bradshaw, J., Coley, C.W.. Reproducing reaction mechanisms with machine learning models trained on a large-scale mechanistic dataset. Angew. Chem. Int. Ed. e202411296. (2024) DOI: 10.1002/anie.202411296

Preprint predictive chemistry

Gao, W., Raghavan, P., Shprints, R., Coley, C.W.. Substrate scope contrastive learning: Repurposing human bias to learn atomic representations. arxiv. (2024) preprint: arXiv:2402.16882

Preprint molecular representation

Haas, B., Hardy, M., Sowndarya, S., Adams, K., Coley, C.W., Paton, R., Sigman, M.. Rapid prediction of conformationally-dependent DFT-level descriptors using graph neural networks for carboxylic acids and alkyl amines. chemrxiv. (2024) preprint: 10.26434/chemrxiv-2024-m5bpn

Preprint molecular representation predictive chemistry

Hua, C., Coley, C.W., Wolf, G., Precup, D., Zheng, S.. Effective protein-protein interaction exploration with PPIretrieval. arxiv. (2024) preprint: arXiv:2402.03675

Preprint molecular representation design and optimization

Goldman, S., Li, J. Coley, C.W.. Generating molecular fragmentation graphs with autoregressive neural networks. Anal. Chem. 96(8), 3419–3428. (2024) DOI: 10.1021/acs.analchem.3c04654

Preprint molecular representation metabolomics

Dicks, L., Graff, D., Jordan, K., Coley, C.W., Pyzer-Knapp, E.. A physics-inspired approach to the understanding of molecular representations and models. Syst. Des. Eng. 9, 449-455. (2024) DOI: 10.1039/D3ME00189J

Preprint molecular representation

Zhu, Y., Hwang, J., Adams, K., Liu, Z., Nan, B., Stenfors, B.A., Du, Y., Chauhan, J., Wiest, O., Isayev, O., Coley, C.W., Sun, Y., Wang, W.. Learning over molecular conformer ensembles: datasets and benchmarks. ICLR. (2024)

Preprint molecular representation

Fromer, J.C., Graff, D.E., Coley, C.W.. Pareto optimization to accelerate multi-objective virtual screening. Digital Discov. 3, 467-481. (2024) DOI: 10.1039/D3DD00227F

Preprint design and optimization

Qian, Y., Li, Z., Tu, Z., Coley, C.W., Barzilay, R.. Predictive chemistry augmented with text retrieval. Proceedings of the 2023 Conference on EMNLP. 12731–12745. (2023) DOI: 10.18653/v1/2023.emnlp-main.784

Preprint predictive chemistry

Raghavan, P., Haas, B.C., Ruos, M.E., Schleinitz, J., Doyle, A.G., Reisman, S.E., Sigman, M.S., Coley, C.W.. Dataset design for building models of chemical reactivity. ACS Cent. Sci. 9(12), 2196–2204. (2023) DOI: 10.1021/acscentsci.3c01163

data predictive chemistry

Fromer, J.C., Coley, C.W.. An algorithmic framework for synthetic cost-aware decision making in molecular design. Nat. Comput. Sci. 4, 440-450. (2024) DOI: 10.1038/s43588-024-00639-y

Preprint design and optimization

Mason, J. W., Hudson, L., Westphal, M. V., Tutter, A., Michaud, G., Shu, W., Ma, X., Coley, C. W., Clemons, P. A., Bonazzi, S., Berst, F., Zécri, F. J., Briner, K., Schreiber, S. L.. DNA-encoded library (DEL)-enabled discovery of proximity-inducing small molecules. Nat. Chem. Biol. 20, 170–179. (2023) DOI: 10.1038/s41589-023-01458-4

Preprint design and optimization data

Jin, T., Coley, C.W., Alexander-Katz, A.. A computationally informed unified view on the effect of polarity and sterics on the glass transition in vinyl-based polymer melts. ACS Macro. Lett. 12(11), 1517–1522. (2023) DOI: 10.1021/acsmacrolett.3c00553

molecular representation predictive chemistry

Frey, N., Soklaski, R., Alexrod, S., Samsi, S., Gomez-Bombarelli, R., Coley, C. W., Gadepally, V.. Neural scaling of deep chemical models. Nat. Mach. Intell. 5, 1297–1305. (2023) DOI: 10.1038/s42256-023-00740-3

Preprint molecular representation

Griffin, D.J., Coley, C.W., Frank, S.A., Hawkins, J.M., Jensen, K.F.. Opportunities for machine learning and artificial intelligence to advance synthetic drug substance process development. Process Res. Dev. 27(11), 1868–1879. (2023) DOI: 10.1021/acs.oprd.3c00229

automation

Goldman, S., Xin, J. Provenzano, J., Coley, C.W.. MIST-CF: Chemical formula inference from tandem mass spectra. J. Chem. Inf. Model. 64(7), 2421–2431. (2024) DOI: 10.1021/acs.jcim.3c01082

Preprint metabolomics

Levin, I., Fortunato, M.E., Tan, K.L., Coley, C.W.. Computer-aided evaluation and exploration of chemical spaces constrained by reaction pathways. AIChE J. 69(12), e18234. (2023) DOI: 10.1002/aic.18234

predictive chemistry

Casetti, N., Alfonso-Ramos, J.E., Coley, C.W., Stuyver, T.. Combining molecular modeling and machine learning for accelerated reaction screening and discovery. Chem. Eur. J. 29, e202301957. (2023) DOI: 10.1002/chem.202301957

predictive chemistry

Wang, H., Fu, T., Du, Y., Gao, W., Huang, K., Liu, Z., Chandak, P., Liu, S., Van Katwyk, P., Deac, A., Anandkumar, A., Bergen, K., Gomes, C., Ho, S., Kohli, P., Lasenby, J., Leskovec, J., Liu, T., Manrai, A., Marks, D., Ramsundar, B., Song, L., Sun, J., Tang, J., Veličković, P., Welling, M., Zhang, L., Coley, C.W., Bengio, Y. & Zitnik, M.. Scientific discovery in the age of artificial intelligence. Nature 620, 47–60. (2023) DOI: 10.1038/s41586-023-06221-2

molecular representation design and optimization predictive chemistry automation data

Zhang, X., Wang, L., Helwig, J., Luo, Y., Fu, C., Xie, Y., Adams, K., Coley, C.W., Qian, X., Qian, X., Smidt, T., Ji, S.. Artificial intelligence for science: Quantum, atomistic, and continuum systems. arxiv. (2023) preprint: arXiv:2307.08423

Preprint molecular representation design and optimization predictive chemistry data

Wierenga, R.P., Golas, S., Ho, W., Coley, C.W., Elsvelt, K.M.. PyLabRobot: An open-source hardware agnostic interface for liquid-handling robots and accessories. Device 1, 100111. (2023) DOI: 10.1016/j.device.2023.100111

Preprint automation

Hudson, L., Mason, J. W., Westphal, M. V., Richter, M. J. R., Thielman, J. R., Hua, B. K., Gerry, C. J., Xia, G., Osswald, H. L., Knapp, J. M., Tan, Z. Y., Kokkonda, P., Tresco, B. I. C., Liu, S. Reidenbach, A. G., Lim, K. S., Poirier, J., Capece, J., Bonazzi, S., Gampe, C. M., Smith, N. J., Bradner, J. E., Coley, C. W., Clemons, P. A., Melillo, B., Ottl, J. Dumelin, C. E., Schaefer, J. V., Faust, A. M. E., Berst, F., Schreiber, S. L., Zécri, F. J., Briner, K.. Diversity-oriented synthesis encoded by deoxyoligonucleotides. Nat Commun 14, 4930. (2023) DOI: 10.1038/s41467-023-40575-5

Preprint design and optimization

Mercado, R., Kearnes, S.M., Coley, C.W.. Data sharing in chemistry: Lessons learned and a case for mandating structured reaction data. J. Chem. Inf. Model. 63(14), 4253–4265. (2023) DOI: 10.1021/acs.jcim.3c00607

data

Qian, Y., Guo, J., Tu, Z., Coley, C.W., Barzilay, R.. RxnScribe: A sequence generation model for reaction diagram parsing. J. Chem. Inf. Model. 63(13), 4030–4041. (2023) DOI: 10.1021/acs.jcim.3c00439

Preprint data

David, N., Sun, W., Coley, C.W.. The promise and pitfalls of AI for molecular and materials synthesis. Comput. Sci. 3, 362–364. (2023) DOI: 10.1038/s43588-023-00446-x

molecular representation design and optimization predictive chemistry automation data

Graff, D.E., Pyzer-Knapp, E.O., Jordan, K.E., Shakhnovich, E.I., Coley, C.W.. Evaluating the roughness of structure-property relationships using pretrained molecular representations. Digital Discov. 2, 1452-1460. (2023) DOI: 10.1039/D3DD00088E

Preprint molecular representation predictive chemistry

Neeser, R., Isert, C., Stuyver, T., Schneider, G., Coley, C.W.. QMugs 1.1: quantum mechanical properties of organic compounds commonly encountered in reactivity datasets. Data Collect. 46(101040), 2405-8300. (2023) DOI: 10.1016/j.cdc.2023.101040

predictive chemistry

Reidenbach, D., Coley, C.W., Yang, K.. Generating multi-step chemical reaction pathways with black-box optimization. ICLR Workshop on Machine Learning in Drug Discovery. (2023)

predictive chemistry

Jiang, Y., Yu, Y., Kong, M., Mei, Y., Yuan, L., Huang, Z., Kuang, K., Wang, Z., Yao, H., Zou, J., Coley, C. W., Wei, Y.. Artificial intelligence for retrosynthesis prediction. Engineering 25, 32-50. (2023) DOI: 10.1016/j.eng.2022.04.021

predictive chemistry

Wu, G., Zhou, H., Chang, J., Tian, Z., Liu, X., Wang, S., Coley, C.W., Lu, H.. A high-throughput platform for efficient exploration of functional polypeptides chemical space. Nat. Synth. 2, 515–526. (2023) DOI: 10.1038/s44160-023-00294-7

Preprint automation design and optimization

Maloney, M.P., Coley, C.W., Genheden, S., Carson, N., Helquist, P., Norrby, P.-O., Wiest, O.. Negative data in data sets for machine learning training. J. Org. Chem. 88(9), 5239–5241. (2023) DOI: 10.1021/acs.joc.3c00844

data

Goldman, S., Bradshaw, J., Xin, J., Coley, C.W.. Prefix-tree decoding for predicting mass spectra from molecules. NeurIPS. (2024) preprint: arXiv:2303.06470

Preprint metabolomics molecular representation

Qian, Y., Tu, Z., Guo, J. Coley, C. W., Barzilay, R.. MolScribe: Robust molecular image recognition: a graph generation approach. J. Chem. Inf. Model. 63(7), 1925–1934. (2023) DOI: 10.1021/acs.jcim.2c01480

Preprint data molecular representation

Jin, T., Coley, C.W., Alexander-Katz, A.. Adsorption of biomimetic amphiphilic heteropolymers onto graphene and its derivatives. Macromolecules 56(5), 1798–1809. (2023) DOI: 10.1021/acs.macromol.2c02413

predictive chemistry

Fromer, J., Coley, C.W.. Computer-aided multi-objective optimization in small molecule discovery. Patterns 4(2), 100678. (2023) DOI: 10.1016/j.patter.2023.100678

design and optimization

Goldman, S., Wohlwend, J., Stražar, M., Haroush, G., Xavier, R.J., Coley, C.W.. Annotating metabolite mass spectra with domain-inspired chemical formula transformers. Nat. Mach. Intell. 5, 965–979. (2022) DOI: 10.1038/s42256-023-00708-3

Preprint metabolomics molecular representation

Stuyver, T., Coley, C.W.. Machine learning-guided computational screening of new bio-orthogonal click reactions. Chem. Eur. J. 29, e202300387. (2023) DOI: 10.1002/chem.202300387

Preprint predictive chemistry

Stuyver, T., Jorner, Kjell, Coley, C.W.. Reaction profiles for quantum chemistry-computed [3+2] cycloaddition reactions. Sci. Data 10, 66. (2023) DOI: 10.1038/s41597-023-01977-8

Preprint predictive chemistry

Tu, Z., Stuyver, T., Coley, C. W.. Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery. Chem. Sci. 14, 226-244. (2023) DOI: 10.1039/D2SC05089G

predictive chemistry

Levin, I., Liu, M., Voigt, C.A., Coley, C.W.. Merging enzymatic and synthetic chemistry with computational synthesis planning. Nat. Commun. 13, 7747. (2022) DOI: 10.1038/s41467-022-35422-y

predictive chemistry

Nori, D., Coley, C.W., Mercado, R.. De novo PROTAC design using graph-based deep generative models. NeurIPS AI4Science Workshop. (2022)

Preprint design and optimization molecular representation

Jin, T., Coley, C.W., Alexander-Katz, A.. Molecular signatures of the glass transition in polymers. Phys. Rev. E 106(1), 6470. (2022) DOI: 10.1103/PhysRevE.106.014506

Preprint

Adams, K., Coley, C. W.. Equivariant shape-conditioned generation of 3D molecular for ligand-based drug design. ICLR. (2023)

Preprint design and optimization molecular representation

Gao, W., Fu, T., Sun, J. Coley, C. W.. Sample efficiency matters: A benchmark for practical molecular optimization. 36th Conference on Neural Information Processing Systems (NeurIPS). (2022)

Preprint design and optimization

Fu, T., Gao, W., Coley, C. W., Sun, J.. Reinforced genetic algorithm for structure-based drug design. 36th Conference on Neural Information Processing Systems (NeurIPS). (2022)

Preprint design and optimization

Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., Zitnik, M.. Artificial intelligence foundation for therapeutic science. Nature Chem. Bio. 18, 1033–1036. (2022) DOI: 10.1038/s41589-022-01131-2

design and optimization molecular representation predictive chemistry data

Aldeghi, M., Graff, D. E., Frey, N., Morrone, J. A., Pyzer-Knapp, E. O., Jordan, K. E, Coley, C. W.. Roughness of molecular property landscapes and its impact on modellability. J. Chem. Inf. Model. 62(19), 4660–4671. (2022) DOI: 10.1021/acs.jcim.2c00903

Preprint design and optimization

Aldeghi, M., Coley, C. W.. A focus on simulation and machine learning as complementary tools for chemical space navigation. Chem. Sci. 13, 8221-8223. (2022) DOI: 10.1039/D2SC90130G

design and optimization

Aldeghi, M., Coley, C. W.. A graph representation of molecular ensembles for polymer property prediction. Chem. Sci. 13, 10486-10498. (2022) DOI: 10.1039/D2SC02839E

Preprint molecular representation predictive chemistry

Zheng, S., Zeng, T., Li, C., Chen, B., Coley, C. W., Yang, Y., Wu, R.. BioNavi-NP: Biosynthesis navigator for natural products. Nat. Commun. 13, 3342. (2022) DOI: 10.1038/s41467-022-30970-9

Preprint predictive chemistry

Graff, D. E., Aldeghi, M., Marrone, J. A., Jordan, K. E., Pyzer-Knapp, E. O., Coley, C. W.. Self-focusing virtual screening with active design space pruning. J. Chem. Inf. Model. 62(16), 3854–3862. (2022) DOI: 10.1021/acs.jcim.2c00554

Preprint design and optimization

Sankaranarayanan, K., Heid, E., Coley, C.W., Verma, D., Green, W.H., Jensen, K.F.. Similarity based enzymatic retrosynthesis. Chem. Sci. 13, 6039-6053. (2022) DOI: 10.1039/D2SC01588A

predictive chemistry

Gao, W., Raghavan, P., Coley, C. W.. Autonomous platforms for data-driven organic synthesis. Nat. Commun. 13, 1075. (2022) DOI: 10.1038/s41467-022-28736-4

automation

Lin, M.-H., Tu, Z., Coley, C. W.. Improving the performance of models for one-step retrosynthesis through re-ranking. J. Cheminform. 14, 15. (2022) DOI: 10.1186/s13321-022-00594-8

predictive chemistry

Graff, D. E., Coley, C. W.. pyscreener: a Python wrapper for computational docking software. JOSS 7(71), 3950. (2022) DOI: 10.21105/joss.03950

Preprint automation molecular representation design and optimization

Frey, N. C., Samsi, S., McDonald, J., Coley, C. W., Gadepally, V.. Scalable geometric deep learning on molecular graphs. NeurIPS AI4Science Workshop. (2021)

Preprint molecular representation design and optimization

Frey, N. C., Samsi, S., Ramsundar, B., Coley, C. W., Gadepally, V.. Bringing atomistic deep learning to prime time. NeurIPS AI4Science Workshop. (2021)

Preprint molecular representation design and optimization

Kearnes, S. M., Maser, M. R., Wleklinski, M., Kast, A., Doyle, A. G., Dreher, S. D., Hawkins, J. M., Jensen, K. F. Coley, C. W.. The Open Reaction Database. J. Am. Chem. Soc. 143(45), 18820–18826. (2021) DOI: 10.1021/jacs.1c09820

data

Tu, Z., Coley, C. W.. Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction. J. Chem. Inf. Model. 62(15), 3503–3513. (2022) DOI: 10.1021/acs.jcim.2c00321

Preprint predictive chemistry

Gao, W., Mercado, R., Coley, C. W.. Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design. ICLR. (2022)

Preprint design and optimization

Adams, K., Pattanaik, L., Coley, C. W.. Learning 3D representations of molecular chirality with invariance to bond rotations. ICLR. (2022)

Preprint molecular representation design and optimization

Fu, T., Gao, W., Xiao, C., Yasonik, J., Coley, C. W., Sun, J.. Differentiable scaffolding tree for molecular optimization. ICLR. (2022)

Preprint design and optimization molecular representation

Goldman, S., Das, R., Yang, K. K., Coley C. W.. Machine learning modeling of family wide enzyme-substrate specificity screens. PLOS Comp. Bio. 18(2), e1009853. (2022) DOI: 10.1371/journal.pcbi.1009853

Preprint predictive chemistry

Soleimany, A. P., Amini, A., Goldman, S., Rus, D., Bhatia, S., Coley, C. W.. Evidential deep learning for guided molecular property prediction and discovery. ACS Cent. Sci. 7(8), 1356–1367. (2021) DOI: 10.1021/acscentsci.1c00546

predictive chemistry design and optimization

Stuyver, T., Coley C. W.. Quantum chemistry-augmented neural networks for reactivity prediction: Performance, generalizability and interpretability. J. Chem. Phys. 156, 084104. (2022) DOI: 10.1063/5.0079574

Preprint predictive chemistry molecular representation

Bi, H., Wang, H., Shi, C., Coley C. W., Tang, J., Guo, H.. Non-autoregressive electron redistribution modeling for reaction prediction. Proceedings of the 38th ICML. (2021)

Preprint predictive chemistry molecular representation

Ganea, O. E., Pattanaik, L., Coley, C. W., Barzilay, R., Jensen, K. F., Green, W. H., Jaakkola, T. S.. GeoMol: Torsional geometric generation of molecular 3D conformer ensembles. NeurIPS. (2021)

Preprint molecular representation

Guo, J., Ibanez-Lopez, A. S., Gao, H., Quach, V., Coley, C. W., Jensen, K. F., Barzilay, R.. Automated chemical reaction extraction from scientific literature. J. Chem. Inf. Model. 62(9), 2035–2045. (2021) DOI: 10.1021/acs.jcim.1c00284

data

Heid, E., Goldman, S., Sankaranarayanan, K., Coley, C. W., Flamm, C., Green, W. H.. EHreact: Extended Hasse diagrams for the extraction and scoring of enzymatic reaction templates. J. Chem. Inf. Model. 61(10), 4949–4961. (2021) DOI: 10.1021/acs.jcim.1c00921

Preprint predictive chemistry

Graff, D. E., Shakhnovich, E. I., Coley, C. W.. Accelerating high-throughput virtual screening through molecular pool-based active learning. Chem. Sci. 12, 7866-7881. (2021) DOI: 10.1039/D0SC06805E

Preprint predictive chemistry automation

Huang, K., Fu, T., Gao, W., Zhao, Y., Roohani, Y., Leskovec, J., Coley, C. W., Xiao, C., Sun, J., Zitnik, M.. Therapeutics Data Commons: Machine learning datasets and tasks for therapeutics. NeurIPS Datasets and Benchmarks. (2021)

Preprint data

Guan, Y., Coley, C. W., Wu, H., Ranasinghe, D., Heid, E., Struble, T. J., Pattanaik, L., Green, W. H., Jensen, K. F.. Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors. Chem. Sci. 12, 2198-2208. (2021) DOI: 10.1039/D0SC04823B

Preprint predictive chemistry molecular representation

Coley, C. W.. Defining and exploring chemical spaces. Trends in Chemistry 3(2), 133-145. (2021) DOI: 10.1016/j.trechm.2020.11.004

design and optimization

Gao, H., Pauphilet, J., Struble, T. J., Coley, C. W., Jensen, K. F.. Direct optimization across computer-generated reaction networks balances materials use and feasibility of synthesis plans for molecule libraries. J. Chem. Inf. Model. 61(1), 493–504. (2020) DOI: 10.1021/acs.jcim.0c01032

predictive chemistry design and optimization

Mo, Y., Guan, Y., Verma, P., Guo, J., Fortunato, M. E., Lu, Z., Coley, C. W., Jensen, K. F.. Evaluating and clustering retrosynthesis pathways with learned strategy. Chem. Sci. 12, 1469-1478. (2020) DOI: 10.1039/D0SC05078D

predictive chemistry

Pattanaik, L., Ganea, O. E., Coley, I., Jensen, K. F., Green, W. H., Coley, C. W.. Message passing networks for molecules with tetrahedral chirality. NeurIPS ML4Molecules. (2020)

Preprint molecular representation

Wang, X., Qian, Y., Gao, H. Coley, C. W., Mo, Y., Barzilay, R., Jensen, K. F.. Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning. Chem. Sci. 11, 10959-10972. (2020) DOI: 10.1039/D0SC04184J

predictive chemistry

Plehiers, P. P., Coley, C. W., Gao, H., Vermeire, F. H., Dobbelaere, M. R., Stevens, C. V., Van Geem, K. M., Green, W. H.. Artificial intelligence for computer-aided synthesis in flow: Analysis and selection of reaction components. Front. Chem. Eng. 2, 5. (2020) DOI: 10.3389/fceng.2020.00005

automation predictive chemistry

Somnath, V. R., Bunne, C., Coley, C. W., Krause, A., Barzilay, R.. Learning graph models for template-free retrosynthesis. 35th Conference on Neural Information Processing Systems (NeurIPS). (2021)

Preprint

Pattanaik, L., Coley, C. W.. Molecular representation: Going long on fingerprints. Chem 6(6), 1204-1207. (2020) DOI: 10.1016/j.chempr.2020.05.002

molecular representation

Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R., Coley, C. W.. Uncertainty quantification using neural networks for molecular property prediction. J. Chem. Inf. Model. 60(8), 3770–3780. (2020) DOI: 10.1021/acs.jcim.0c00502

Preprint predictive chemistry

Gottipati, S. K., Sattarov, B., Niu, S., Pathak, Y., Wei, H., Liu, S., Thomas, K. M. J., Blackburn, S., Coley, C. W., Tang, J., Chandar, S., Bengio, Y.. Learning to navigate the synthetically accessible chemical space using reinforcement learning. Proceedings of the 37 th International Conference on Machine Learning (ICML). (2020)

Preprint design and optimization

Struble, T. S., Alvarez, J. C., Brown, S., Chytil, M., Cisar, J., DesJarlais, R., Engkvist, O., Frank, S. A., Greve, D. R., Griffin, D. J. Hou, X., Johannes, J. W., Kreatsoulas, C., Lahue, B., Mathea, M., Mogk, G., Nicolaou, C. A., Palmer, A. D., Price, D. J., Robinson, R. I., Salentin, S., Xing, L., Jaakkola, T., Green, W. H., Barzilay, R., Coley, C. W., Jensen, K. F.. Current and future roles of artificial intelligence in medicinal chemistry synthesis. J. Med. Chem. 63(16), 8667–8682. (2020) DOI: 10.1021/acs.jmedchem.9b02120

automation predictive chemistry design and optimization

Gao, W., Coley, C. W.. The synthesizability of molecules proposed by generative models. J. Chem. Inf. Model. 60(12), 5714–5723. (2020) DOI: 10.1021/acs.jcim.0c00174

Preprint design and optimization predictive chemistry

Struble, T. S., Coley, C. W., Jensen, K. F.. Multitask prediction of site selectivity in aromatic C-H functionalization reactions. React. Chem. Eng. 5, 896-902. (2020) DOI: 10.1039/D0RE00071J

predictive chemistry

Fortunato, M. E., Coley, C. W., Barnes, B. C., Jensen, K. F.. Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning. J. Chem. Inf. Model. 60(7), 3398–3407. (2020) DOI: 10.1021/acs.jcim.0c00403

Preprint predictive chemistry

Gao, H., Coley, C. W., Struble, T., Li, L., Qian, Y. Green, W. H., Jensen, K. F.. Combining retrosynthesis and mixed-integer optimization for minimizing the chemical inventory needed to realize a WHO essential medicines list. React. Chem. Eng. 5, 367-376. (2020) DOI: 10.1039/C9RE00348G

predictive chemistry design and optimization

Dai, H., Li, C. Coley, C. W., Dai, B., Song, L.. Retrosynthesis prediction with conditional graph logic network. 33rd Conference on Neural Information Processing Systems (NeurIPS). (2019)

Preprint predictive chemistry

Coley, C. W., Eyke, N. S., Jensen, K. F.. Autonomous discovery in the chemical sciences part II: Outlook. Angew. Chem. Int. Ed. 59, 23414. (2020) DOI: 10.1002/anie.201909989

Preprint automation

Coley, C. W., Eyke, N. S., Jensen, K. F.. Autonomous discovery in the chemical sciences part I: Progress. Angew. Chem. Int. Ed. 59, 22858. (2020) DOI: 10.1002/anie.201909987

Preprint automation

Lin, T.-S., Coley, C. W., Mochigase, H., Beech, H. K., Wang, W., Wang, Z., Woods, E., Craig, S. L., Johnson, J. A., Kalow, J. A., Jensen, K. F., Olsen, B. D.. BigSMILES: a structurally-based line notation for describing macromolecules. ACS Cent. Sci. 5(9), 1523–1531. (2019) DOI: 10.1021/acscentsci.9b00476

molecular representation

Coley, C. W., Thomas III, D. A., Lummiss, J. A. M., Jaworski, J. N., Breen, C. P., Schultz, V., Hart, T., Fishman, J. S., Rogers, L., Gao, H., Hicklin, R. W., Plehiers, P. P., Byington, J., Piotti, J. S., Green, W. H., Hart, A. J., Jamison, T. F., Jensen, K. F.. A robotic platform for flow synthesis of organic compounds informed by AI planning. Science 365(6453),. (2019) DOI: 10.1126/science.aax1566

automation predictive chemistry

Yang, K., Swanson, K., Jin, W., Coley, C. W., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, Tm., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K. F., Barzilay, R.. Analyzing learned molecular representations for property prediction. J. Chem. Inf. Model. 59(8), 3370–3388. (2019) DOI: 10.1021/acs.jcim.9b00237

Preprint predictive chemistry molecular representation

Coley, C. W., Green, W. H., Jensen, K. F.. RDChiral: an RDKit wrapper for handling stereochemistry in retrosynthetic template extraction and application. J. Chem. Inf. Model. 59(6), 2529–2537. (2019) DOI: 10.1021/acs.jcim.9b00286

Preprint molecular representation

Schreck, J. S., Coley, C. W., Bishop, K. J. M.. Learning retrosynthetic planning through simulated experience. ACS Cent. Sci. 5(6), 970–981. (2019) DOI: 10.1021/acscentsci.9b00055

predictive chemistry

Coley, C. W., Jin, W., Rogers, L., Jamison, T. F., Jaakkola, T. S., Green, W. H., Barzilay, R., Jensen, K. F.. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem. Sci. 10, 370-377. (2019) DOI: 10.1039/C8SC04228D

Preprint predictive chemistry molecular representation

Gao, H., Struble, T. J., Coley, C. W., Wang, Y., Green, W. H., Jensen, K. F.. Using machine learning to predict suitable conditions for organic reactions. ACS Cent. Sci. 4(11), 1465–1476. (2018) DOI: 10.1021/acscentsci.8b00357

predictive chemistry

Zhu, C., Raghuvanshi, K., Coley, C. W., Mason, D., Rodgers, J., Janka, M. E., Abolhasani, M.. Flow chemistry-enabled studies of rhodium-catalyzed hydroformylation reactions. Chem. Comm. 54, 8567-8570. (2018) DOI: 10.1039/C8CC04650F

automation

Coley, C. W., Green, W. H., Jensen, K. F.. Machine learning in computer-aided organic synthesis. Acc. Chem. Res. 51(5), 1281–1289. (2018) DOI: 10.1021/acs.accounts.8b00087

predictive chemistry

Baumgartner, L., Coley, C. W., Reizman, B., Gao, K., Jensen, K. F.. Optimum catalyst selection over continuous and discrete process variables with a single droplet microfluidic reaction platform. React. Chem. Eng. 3, 301-311. (2018) DOI: 10.1039/C8RE00032H

automation

Hsieh, H.-W., Coley, C. W., Baumgartner, L., Jensen, K. F., Robison, R.. Photoredox iridium-nickel dual catalyzed decarboxylative arylation cross-coupling: from batch to continuous flow via self-optimizing segmented flow reactor. Org. Process Res. Dev. 22(4), 542–550. (2018) DOI: 10.1021/acs.oprd.8b00018

automation

Epps, R. W., Felton, K.C., Coley, C. W., Abolhasani, M.. A modular microfluidic technology for systematic studies of colloidal semiconductor nanocrystals. J. Vis. Exp. 135, e57666. (2018) DOI: 10.3791/57666

automation

Lazzari, S., Theiler, P. M., Shen, Y., Coley, C. W., Stemmer, A., Jensen, K. F.. Ligand-mediated nanocrystal growth. Langmuir 34(10), 3307–3315. (2018) DOI: 10.1021/acs.langmuir.8b00076

automation predictive chemistry

Coley, C. W., Rogers, L., Green, W. H., Jensen, K. F.. SCScore: Synthetic complexity learned from a reaction corpus. J. Chem. Inf. Model. 58(2), 252–261. (2018) DOI: 10.1021/acs.jcim.7b00622

predictive chemistry molecular representation

Coley, C. W., Rogers, L., Green, W. H., Jensen, K. F.. Computer-assisted retrosynthesis based on molecular similarity. ACS Cent. Sci. 3(12), 1237–1245. (2017) DOI: 10.1021/acscentsci.7b00355

predictive chemistry

Shen, Y., Abolhasani, M., Chen, Y., Xie, L., Yang, L., Coley, C. W., Bawendi, M., Jensen, K. F.. In-situ microfluidic studies of bi-phasic nanocrystal ligand exchange reaction using oscillatory flow reactor. Angew. Chem. Int. Ed. 56, 16333. (2017) DOI: 10.1002/anie.201710899

automation

Epps, R.W., Felton, K.C., Coley, C. W., Abolhasani, M.. Automated microfluidic platform for systematic studies of colloidal perovskite nanocrystals: towards continuous nano-manufacturing. Lab Chip 17, 4040-4047. (2017) DOI: 10.1039/C7LC00884H

automation

Jin, W., Coley, C. W., Barzilay, R., Jaakkola, T.. Predicting organic reaction outcomes with weisfeiler-lehman network. 31st Conference on Neural Information Processing Systems (NeurIPS). (2017)

Preprint predictive chemistry

Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S., Jensen, K. F.. Convolutional embedding of attributed molecular graphs for physical property prediction. J. Chem. Inf. Model. 57(8), 1757–1772. (2017) DOI: 10.1021/acs.jcim.6b00601

predictive chemistry molecular representation

Coley, C. W., Abolhasani, M., Lin, H., Jensen, K. F.. Material-efficient microfluidic platform for exploratory studies of visible-light photoredox catalysis. Angew. Chem. 56, 9847. (2017) DOI: 10.1002/anie.201705148

automation

Coley, C. W., Barzilay, R., Jaakkola, T. S., Green, W. H., Jensen, K. F.. Prediction of organic reaction outcomes using machine learning. ACS Cent. Sci. 3(5), 434–443. (2017) DOI: 10.1021/acscentsci.7b00064

predictive chemistry

Hwang, Y.-J, Coley, C. W., Abolhasani, M., Marzinzik, A.L., Koch, G., Spanka, C., Lehmann, H., Jensen, K.F.. A segmented flow platform for on-demand medicinal chemistry and compound synthesis in oscillating droplets. Chem. Comm. 53, 6649-6652. (2017) DOI: 10.1039/C7CC03584E

automation

Abolhasani, M., Coley, C. W., Jensen, K. F.. Multiphase oscillatory flow strategy for in situ measurement and screening of partition coefficients. Anal. Chem. 87(21), 11130–11136. (2015) DOI: 10.1021/acs.analchem.5b03311

automation

Abolhasani, M., Coley, C. W., Xie, L., Chen, O., Bawendi, M. G., Jensen, K. F.. Oscillatory microprocessor for growth and in situ characterization of semiconductor nanocrystals. Chem. Mater. 27(17), 6131–6138. (2015) DOI: 10.1021/acs.chemmater.5b02821

automation