Publications

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Yim, J.; Campbell, A.; Mathieu, E.; Foong, A. Y. K.; Gastegger, M.; Jiménez-Luna, J.; Lewis, S.; Satorras, V. G.; Veeling, B. S.; Noé, F.; Barzilay, R.; Jaakkola, T. S. Improved Motif-Scaffolding with SE(3) Flow Matching. arXiv January 8, 2024. https://doi.org/10.48550/arXiv.2401.04082.

Heid, E.; Greenman, K. P.; Chung, Y.; Li, S.-C.; Graff, D. E.; Vermeire, F. H.; Wu, H.; Green, W. H.; McGill, C. J. Chemprop: A Machine Learning Package for Chemical Property Prediction. J. Chem. Inf. Model. 2024, 64(1), 9–17. https://doi.org/10.1021/acs.jcim.3c01250.

Koscher, B. A.; Canty, R. B.; McDonald, M. A.; Greenman, K. P.; McGill, C. J.; Bilodeau, C. L.; Jin, W.; Wu, H.; Vermeire, F. H.; Jin, B.; Hart, T.; Kulesza, T.; Li, S.-C.; Jaakkola, T. S.; Barzilay, R.; Gómez-Bombarelli, R.; Green, W. H.; Jensen, K. F. Autonomous, Multiproperty-Driven Molecular Discovery: From Predictions to Measurements and Back. Science 2023, 382 (6677), eadi1407. https://doi.org/10.1126/science.adi1407.

Qian, Y.; Li, Z.; Tu, Z.; Coley, C. W.; Barzilay, R. Predictive Chemistry Augmented with Text Retrieval. arXiv December 8, 2023. https://doi.org/10.48550/arXiv.2312.04881.

Zheng, J. W.; Green, W. H. Experimental Compilation and Computation of Hydration Free Energies for Ionic Solutes. J. Phys. Chem. A 2023, 127 (48), 10268–10281. https://doi.org/10.1021/acs.jpca.3c05514.

Wang, C.; Gupta, S.; Uhler, C.; Jaakkola, T. Removing Biases from Molecular Representations via Information Maximization. arXiv December 1, 2023. https://doi.org/10.48550/arXiv.2312.00718.

Pattanaik, L.; Menon, A.; Settels, V.; Spiekermann, K. A.; Tan, Z.; Vermeire, F. H.; Sandfort, F.; Eiden, P.; Green, W. H. ConfSolv: Prediction of Solute Conformer-Free Energies across a Range of Solvents. J. Phys. Chem. B 2023, 127 (47), 10151–10170. https://doi.org/10.1021/acs.jpcb.3c05904.

Payne, A. M.; Wu, H.; Pang, H.-W.; Grambow, C. A.; Ranasinghe, D. S.; Dong, X.; Dana, A. G.; Green, W. H. Towards Accurate Quantum Mechanical Thermochemistry: (1) Extensible Implementation and Comparison of Bond Additivity Corrections and Isodesmic Reactions. ChemRxiv November 29, 2023. https://doi.org/10.26434/chemrxiv-2023-4xlj9-v2.

Corso, G.; Xu, Y.; de Bortoli, V.; Barzilay, R.; Jaakkola, T. Particle Guidance: Non-I.I.D. Diverse Sampling with Diffusion Models. arXiv November 24, 2023. https://doi.org/10.48550/arXiv.2310.13102.

Stärk, H.; Jing, B.; Barzilay, R.; Jaakkola, T. Harmonic Self-Conditioned Flow Matching for Multi-Ligand Docking and Binding Site Design. arXiv November 4, 2023. https://doi.org/10.48550/arXiv.2310.05764.

Goldman, S.; Xin, J.; Provenzano, J.; Coley, C. W. MIST-CF: Chemical Formula Inference from Tandem Mass Spectra. J. Chem. Inf. Model. 2023. https://doi.org/10.1021/acs.jcim.3c01082.

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 2023, 1–15. https://doi.org/10.1038/s42256-023-00708-3.

Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. J. Chem. Inf. Model. 2023, 63 (15), 4574–4588. https://doi.org/10.1021/acs.jcim.3c00546.

Neeser, R. M.; Isert, C.; Stuyver, T.; Schneider, G.; Coley, C. W. QMugs 1.1: Quantum Mechanical Properties of Organic Compounds Commonly Encountered in Reactivity Datasets. Chemical Data Collections 2023, 46, 101040. https://doi.org/10.1016/j.cdc.2023.101040.

Watson, J. L.; Juergens, D.; Bennett, N. R.; Trippe, B. L.; Yim, J.; Eisenach, H. E.; Ahern, W.; Borst, A. J.; Ragotte, R. J.; Milles, L. F.; Wicky, B. I. M.; Hanikel, N.; Pellock, S. J.; Courbet, A.; Sheffler, W.; Wang, J.; Venkatesh, P.; Sappington, I.; Torres, S. V.; Lauko, A.; De Bortoli, V.; Mathieu, E.; Ovchinnikov, S.; Barzilay, R.; Jaakkola, T. S.; DiMaio, F.; Baek, M.; Baker, D. De Novo Design of Protein Structure and Function with RFdiffusion. Nature 2023, 620 (7976), 1089–1100. https://doi.org/10.1038/s41586-023-06415-8.

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. 2023, 63 (14), 4253–4265. https://doi.org/10.1021/acs.jcim.3c00607.

Liu, S.; Tu, Z.; Xu, M.; Zhang, Z.; Lin, L.; Ying, R.; Tang, J.; Zhao, P.; Wu, D. FusionRetro: Molecule Representation Fusion via in-Context Learning for Retrosynthetic Planning. In Proceedings of the 40th International Conference on Machine Learning; ICML’23; JMLR.org: Honolulu, Hawaii, USA, 2023; Vol. 202, pp 22028–22041. https://dl.acm.org/doi/10.5555/3618408.3619322.

Qian, Y.; Guo, J.; Tu, Z.; Coley, C. W.; Barzilay, R. RxnScribe: A Sequence Generation Model for Reaction Diagram Parsing. J. Chem. Inf. Model. 2023, 63 (13), 4030–4041. https://doi.org/10.1021/acs.jcim.3c00439.

Heid, E.; McGill, C. J.; Vermeire, F. H.; Green, W. H. Characterizing Uncertainty in Machine Learning for Chemistry. J. Chem. Inf. Model. 2023, 63 (13), 4012–4029. https://doi.org/10.1021/acs.jcim.3c00373.

Yim, J.; Trippe, B. L.; De Bortoli, V.; Mathieu, E.; Doucet, A.; Barzilay, R.; Jaakkola, T. SE(3) Diffusion Model with Application to Protein Backbone Generation. arXiv May 22, 2023. https://doi.org/10.48550/arXiv.2302.02277.

Goldman, S.; Li, J.; Coley, C. W. Generating Molecular Fragmentation Graphs with Autoregressive Neural Networks. arXiv April 25, 2023. https://doi.org/10.48550/arXiv.2304.13136.

Qian, Y.; Guo, J.; Tu, Z.; Li, Z.; Coley, C. W.; Barzilay, R. MolScribe: Robust Molecular Structure Recognition with Image-to-Graph Generation. J. Chem. Inf. Model. 2023, 63 (7), 1925–1934. https://doi.org/10.1021/acs.jcim.2c01480.

Ketata, M. A.; Laue, C.; Mammadov, R.; Stärk, H.; Wu, M.; Corso, G.; Marquet, C.; Barzilay, R.; Jaakkola, T. S. DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models. arXiv April 7, 2023. https://doi.org/10.48550/arXiv.2304.03889.

Goldman, S.; Bradshaw, J.; Xin, J.; Coley, C. W. Prefix-Tree Decoding for Predicting Mass Spectra from Molecules. arXiv March 11, 2023. https://doi.org/10.48550/arXiv.2303.06470.

Tu, Z.; Stuyver, T.; Coley, C. W. Predictive Chemistry: Machine Learning for Reaction Deployment, Reaction Development, and Reaction Discovery. Chem. Sci. 2023, 14 (2), 226–244. https://doi.org/10.1039/D2SC05089G.

Tu, Z.; Levin, I.; Coley, C. W. Computer-Assisted Synthesis Planning. In Enabling Tools and Techniques for Organic Synthesis; John Wiley & Sons, Ltd, 2023; pp 423–459. https://doi.org/10.1002/9781119855668.ch11.

Levin, I.; Fortunato, M. E.; Tan, K. L.; Coley, C. W. Computer-Aided Evaluation and Exploration of Chemical Spaces Constrained by Reaction Pathways. AIChE Journal 2023, 69 (12), e18234. https://doi.org/10.1002/aic.18234.

Stuyver, T.; Coley, C. Machine Learning-Guided Computational Screening of New Bio-Orthogonal Click Reactions. arXiv December 15, 2022. https://doi.org/10.48550/arXiv.2212.07621.

Zahrt, A. F.; Mo, Y.; Nandiwale, K. Y.; Shprints, R.; Heid, E.; Jensen, K. F. Machine-Learning-Guided Discovery of Electrochemical Reactions. J. Am. Chem. Soc. 2022, 144 (49), 22599–22610. https://doi.org/10.1021/jacs.2c08997.

Levin, I.; Liu, M.; Voigt, C. A.; Coley, C. W. Merging Enzymatic and Synthetic Chemistry with Computational Synthesis Planning. Nat Commun 2022, 13 (1), 7747. https://doi.org/10.1038/s41467-022-35422-y.

Stuyver, T.; Jorner, K.; Coley, C. Reaction Profiles for Quantum Chemistry-Computed [3 + 2] Cycloaddition Reactions. arXiv December 12, 2022. https://doi.org/10.48550/arXiv.2212.06014.

Fu, T.; Gao, W.; Coley, C. W.; Sun, J. Reinforced Genetic Algorithm for Structure-Based Drug Design. NeurIPS November 30, 2022. https://doi.org/10.48550/arXiv.2211.16508.

Nori, D.; Coley, C. W.; Mercado, R. De Novo PROTAC Design Using Graph-Based Deep Generative Models. arXiv November 4, 2022. https://doi.org/10.48550/arXiv.2211.02660.

Gao, W.; Fu, T.; Sun, J.; Coley, C. W. Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization. arXiv October 9, 2022. https://doi.org/10.48550/arXiv.2206.12411.

Adams, K.; Coley, C. W. Equivariant Shape-Conditioned Generation of 3D Molecules for Ligand-Based Drug Design. arXiv October 6, 2022. https://doi.org/10.48550/arXiv.2210.04893.

Corso, G.; Stärk, H.; Jing, B.; Barzilay, R.; Jaakkola, T. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. arXiv October 4, 2022. https://doi.org/10.48550/arXiv.2210.01776.

Xu, J.; Kalyani, D.; Struble, T.; Dreher, S.; Krska, S.; Buchwald, S. L.; Jensen, K. F. Roadmap to Pharmaceutically Relevant Reactivity Models Leveraging High-Throughput Experimentation. 2022. https://doi.org/10.26434/chemrxiv-2022-x694w.

Zahrt, A.; Mo, Y.; Nandiwale, K. Y.; Shprints, R.; Heid, E.; Jensen, K. Machine Learning-Guided Discovery of New Electrochemical Reactions. 2022. https://doi.org/10.26434/chemrxiv-2022-bjg1p.

Fisch, A.; Jaakkola, T.; Barzilay, R. Calibrated Selective Classification. arXiv August 25, 2022. https://doi.org/10.48550/arXiv.2208.12084.

Tu, Z.; Coley, C. W. Permutation Invariant Graph-to-Sequence Model for Template-Free Retrosynthesis and Reaction Prediction. J. Chem. Inf. Model. 2022, 62 (15), 3503–3513. https://doi.org/10.1021/acs.jcim.2c00321.

Jin, W.; Barzilay, R.; Jaakkola, T. Antibody-Antigen Docking and Design via Hierarchical Equivariant Refinement. ICML July 13, 2022. https://doi.org/10.48550/arXiv.2207.06616.

Stärk, H.; Ganea, O.-E.; Pattanaik, L.; Barzilay, R.; Jaakkola, T. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction. ICML June 4, 2022. https://doi.org/10.48550/arXiv.2202.05146.

Jing, B.; Corso, G.; Chang, J.; Barzilay, R.; Jaakkola, T. Torsional Diffusion for Molecular Conformer Generation. arXiv: NeurIPS June 1, 2022. https://doi.org/10.48550/arXiv.2206.01729.

Qian, Y.; Tu, Z.; Guo, J.; Coley, C. W.; Barzilay, R. Robust Molecular Image Recognition: A Graph Generation Approach. arXiv May 27, 2022. https://doi.org/10.48550/arXiv.2205.14311.

Lin, M. H.; Tu, Z.; Coley, C. W. Improving the Performance of Models for One-Step Retrosynthesis through Re-Ranking. Journal of Cheminformatics 2022, 14 (1), 15. https://doi.org/10.1186/s13321-022-00594-8.

Ganea, O.-E.; Huang, X.; Bunne, C.; Bian, Y.; Barzilay, R.; Jaakkola, T.; Krause, A. Independent SE(3)-Equivariant Models for End-to-End Rigid Protein Docking; arXiv:2111.07786; arXiv, 2022. https://doi.org/10.48550/arXiv.2111.07786.

Xie, T.; Fu, X.; Ganea, O.-E.; Barzilay, R.; Jaakkola, T. Crystal Diffusion Variational Autoencoder for Periodic Material Generation. ICLR 2022. http://arxiv.org/abs/2110.06197.

Gao, W.; Mercado, R.; Coley, C. W. Amortized Tree Generation for Bottom-up Synthesis Planning and Synthesizable Molecular Design; arXiv:2110.06389; arXiv, 2022. https://doi.org/10.48550/arXiv.2110.06389.

Stuyver, T.; Coley, C. W. Quantum Chemistry-Augmented Neural Networks for Reactivity Prediction: Performance, Generalizability and Interpretability. J. Chem. Phys. 2022, 156 (8), 084104. https://doi.org/10.1063/5.0079574.

Fisch, A.; Schuster, T.; Jaakkola, T.; Barzilay, R. Conformal Prediction Sets with Limited False Positives. arXiv: ICML February 15, 2022. https://doi.org/10.48550/arXiv.2202.07650.

Chung, Y.; Vermeire, F. H.; Wu, H.; Walker, P. J.; Abraham, M. H.; Green, W. H. Group Contribution and Machine Learning Approaches to Predict Abraham Solute Parameters, Solvation Free Energy, and Solvation Enthalpy. J. Chem. Inf. Model. 2022, 62 (3), 433–446. https://doi.org/10.1021/acs.jcim.1c01103.

Goldman, S.; Das, R.; Yang, K. K.; Coley, C. W. Machine Learning Modeling of Family Wide Enzyme-Substrate Specificity Screens. PLoS Comput Biol 2022, 18 (2), e1009853. https://doi.org/10.1371/journal.pcbi.1009853.

Jin, W.; Wohlwend, J.; Barzilay, R.; Jaakkola, T. Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-Design; arXiv:2110.04624; ICLR, 2022. https://doi.org/10.48550/arXiv.2110.04624.

Heid, E.; Liu, J.; Aude, A.; Green, W. H. Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications. J. Chem. Inf. Model. 2022, 62 (1), 16–26. https://doi.org/10.1021/acs.jcim.1c01192.

Sankaranarayanan, K.; Heid, E.; W. Coley, C.; Verma, D.; H. Green, W.; F. Jensen, K. Similarity Based Enzymatic Retrosynthesis. Chemical Science 2022, 13 (20), 6039–6053. https://doi.org/10.1039/D2SC01588A.

Bilodeau, C.; Jin, W.; Jaakkola, T.; Barzilay, R.; Jensen, K. F. Generative Models for Molecular Discovery: Recent Advances and Challenges. WIREs Computational Molecular Science 2022, 12 (5), e1608. https://doi.org/10.1002/wcms.1608.

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. 2021, 143 (45), 18820–18826. https://doi.org/10.1021/jacs.1c09820.

Heid, E.; Green, W. H. Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction. 2021. https://doi.org/10.26434/chemrxiv-2021-frfhz-v2.

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. 2021, 61 (10), 4949–4961. https://doi.org/10.1021/acs.jcim.1c00921.

Chen, B.; Bécigneul, G.; Ganea, O.-E.; Barzilay, R.; Jaakkola, T. Optimal Transport Graph Neural Networks. arXiv:2006.04804, 2021. http://arxiv.org/abs/2006.04804.

Jin, W.; Stokes, J. M.; Eastman, R. T.; Itkin, Z.; Zakharov, A. V.; Collins, J. J.; Jaakkola, T. S.; Barzilay, R. Deep Learning Identifies Synergistic Drug Combinations for Treating COVID-19. Proceedings of the National Academy of Sciences 2021, 118 (39), e2105070118. https://doi.org/10.1073/pnas.2105070118.

Soleimany, A. P.; Amini, A.; Goldman, S.; Rus, D.; Bhatia, S. N.; Coley, C. W. Evidential Deep Learning for Guided Molecular Property Prediction and Discovery. ACS Cent. Sci. 2021, 7 (8), 1356–1367. https://doi.org/10.1021/acscentsci.1c00546.

Yang, K.; Jin, W.; Swanson, K.; Barzilay, R.; Jaakkola, T. Improving Molecular Design by Stochastic Iterative Target Augmentation. arXiv:2002.04720. 2021. http://arxiv.org/abs/2002.04720.

Vermeire, F. H.; Green, W. H. Transfer Learning for Solvation Free Energies: From Quantum Chemistry to Experiments. Chemical Engineering Journal 2021, 418, 129307. https://doi.org/10.1016/j.cej.2021.129307.

McGill, C.; Forsuelo, M.; Guan, Y.; Green, W. H. Predicting Infrared Spectra with Message Passing Neural Networks. J. Chem. Inf. Model. 2021, 61 (6), 2594–2609. https://doi.org/10.1021/acs.jcim.1c00055.

Graff, D. E.; Shakhnovich, E. I.; Coley, C. W. Accelerating High-Throughput Virtual Screening through Molecular Pool-Based Active Learning. Chem. Sci. 2021, 12 (22), 7866–7881. https://doi.org/10.1039/D0SC06805E.

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; arXiv:2106.07802.; 2021. https://doi.org/10.48550/arXiv.2106.07802.

Somnath, V. R.; Bunne, C.; Coley, C. W.; Krause, A.; Barzilay, R. Learning Graph Models for Retrosynthesis Prediction. arXiv June 4, 2021. https://doi.org/10.48550/arXiv.2006.07038.

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. 2021, 12 (6), 2198–2208. https://doi.org/10.1039/D0SC04823B.

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. 2021, 12 (4), 1469–1478. https://doi.org/10.1039/D0SC05078D.

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. 2021, 61 (1), 493–504. https://doi.org/10.1021/acs.jcim.0c01032.

Gao, W.; Coley, C. W. The Synthesizability of Molecules Proposed by Generative Models. J. Chem. Inf. Model.2020, 60 (12), 5714–5723. https://doi.org/10.1021/acs.jcim.0c00174.

Jin, W.; Barzilay, R.; Jaakkola, T. Discovering Synergistic Drug Combinations for COVID with Biological Bottleneck Models. arXiv:2011.04651 2020. http://arxiv.org/abs/2011.04651.

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. 2020, 11(40), 10959–10972. https://doi.org/10.1039/D0SC04184J.

Jin, W.; Barzilay, R.; Jaakkola, T. Enforcing Predictive Invariance across Structured Biomedical Domains. arXiv:2006.03908. 2020. http://arxiv.org/abs/2006.03908.

Eyke, N. S.; Green, W. H.; Jensen, K. F. Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening. React. Chem. Eng. 2020, 5 (10), 1963–1972. https://doi.org/10.1039/D0RE00232A.

Struble, T. J.; Alvarez, J. C.; Brown, S. P.; Chytil, M.; Cisar, J.; DesJarlais, R. L.; 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, William. H.; Barzilay, R.; Coley, C. W.; Jensen, K. F. Current and Future Roles of Artificial Intelligence in Medicinal Chemistry Synthesis. J. Med. Chem. 2020, 63 (16), 8667–8682. https://doi.org/10.1021/acs.jmedchem.9b02120.

Hirschfeld, L.; Swanson, K.; Yang, K.; Barzilay, R.; Coley, C. W. Uncertainty Quantification Using Neural Networks for Molecular Property Prediction. J. Chem. Inf. Model. 2020, 60 (8), 3770–3780. https://doi.org/10.1021/acs.jcim.0c00502.

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. 2020, 60 (7), 3398–3407. https://doi.org/10.1021/acs.jcim.0c00403.

Jin, W.; Barzilay, R.; Jaakkola, T. Multi-Objective Molecule Generation Using Interpretable Substructures. arXiv:2002.03244. 2020. http://arxiv.org/abs/2002.03244.

Scalia, G.; Grambow, C. A.; Pernici, B.; Li, Y.-P.; Green, W. H. Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction. J. Chem. Inf. Model. 2020, 60 (6), 2697–2717. https://doi.org/10.1021/acs.jcim.9b00975.

Stokes, J. M.; Yang, K.; Swanson, K.; Jin, W.; Cubillos-Ruiz, A.; Donghia, N. M.; MacNair, C. R.; French, S.; Carfrae, L. A.; Bloom-Ackermann, Z.; Tran, V. M.; Chiappino-Pepe, A.; Badran, A. H.; Andrews, I. W.; Chory, E. J.; Church, G. M.; Brown, E. D.; Jaakkola, T. S.; Barzilay, R.; Collins, J. J. A Deep Learning Approach to Antibiotic Discovery. Cell 2020, 180 (4), 688-702.e13. https://doi.org/10.1016/j.cell.2020.01.021.

Gao, H.; Coley, C. W.; Struble, T. J.; 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. 2020, 5 (2), 367–376. https://doi.org/10.1039/C9RE00348G.

Coley, C. W.; Eyke, N. S.; Jensen, K. F. Autonomous Discovery in the Chemical Sciences Part II: Outlook. Angewandte Chemie International Edition 2020, 59 (52), 23414–23436. https://doi.org/10.1002/anie.201909989.

Coley, C. W.; Eyke, N. S.; Jensen, K. F. Autonomous Discovery in the Chemical Sciences Part I: Progress. Angewandte Chemie International Edition 2020, 59 (51), 22858–22893. https://doi.org/10.1002/anie.201909987.

Struble, T. J.; Coley, C. W.; Jensen, K. F. Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions. Reaction Chemistry & Engineering 2019, 5 (5). https://doi.org/10.26434/chemrxiv.9735599.v1.

Yang, K.; Swanson, K.; Jin, W.; Coley, C.; Eiden, P.; Gao, H.; Guzman-Perez, A.; Hopper, T.; Kelley, B.; Mathea, M.; Palmer, A.; Settels, V.; Jaakkola, T.; Jensen, K.; Barzilay, R. Analyzing Learned Molecular Representations for Property Prediction. J. Chem. Inf. Model. 2019, 59 (8), 3370–3388. https://doi.org/10.1021/acs.jcim.9b00237.

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. 2019, 59 (6), 2529–2537. https://doi.org/10.1021/acs.jcim.9b00286.

Chen, B.; Barzilay, R.; Jaakkola, T. Path-Augmented Graph Transformer Network. arXiv May 29, 2019. https://doi.org/10.48550/arXiv.1905.12712.

Jin, W.; Barzilay, R.; Jaakkola, T. Junction Tree Variational Autoencoder for Molecular Graph Generation. arXiv March 29, 2019. https://doi.org/10.48550/arXiv.1802.04364.

Li, Y.-P.; Han, K.; Grambow, C. A.; Green, W. H. Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry. J. Phys. Chem. A 2019, 123 (10), 2142–2152. https://doi.org/10.1021/acs.jpca.8b10789.

Jin, W.; Yang, K.; Barzilay, R.; Jaakkola, T. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization. arXiv January 28, 2019. https://doi.org/10.48550/arXiv.1812.01070.

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. 2019, 10 (2), 370–377. https://doi.org/10.1039/C8SC04228D.

Ingraham, J.; Garg, V. K.; Barzilay, R.; Jaakkola, T. GENERATIVE MODELS FOR GRAPH-BASED PROTEIN DESIGN. ICLR Workshop, 2019, 10. https://openreview.net/pdf?id=SJgxrLLKOE.

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. 2018, 4 (11), 1465–1476. https://doi.org/10.1021/acscentsci.8b00357.

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