(Last updated Oct. 30, 2024. See Google Scholar for most up-to-date publications)
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Adams, K., Abeywardane, K.A., Coley, C.W.. ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design. TBD. (2024) preprint: TBD
Preprint molecular representation design and optimization
Fromer, J., Wang, R., Manjrekar, M., Tripp, A., Hernández-Lobato, J.M., Coley, C.W.. Batched Bayesian optimization with correlated candidate uncertainties. arXiv. (2024) preprint: arXiv:2410.06333
Preprint design and optimization predictive chemistry
Gao, W., Luo, S., Coley, C.W.. Generative artificial intelligence for navigating synthesizable chemical space. arXiv. (2024) preprint: arXiv:2410.03494
Preprint design and optimization
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
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
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
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. (2023) 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, K., 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
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)
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
Coley Research Group