Publications – MLPDS

Roh, J.; Joung, J. F.; Yu, K.; Tu, Z.; Bartholomew, G. L.; Santiago-Reyes, O. A.; Fong, M. H.; Sarpong, R.; Reisman, S. E.; Coley, C. W. Higher-Level Strategies for Computer-Aided Retrosynthesis. ChemRxiv February 5, 2025. https://doi.org/10.26434/chemrxiv-2025-21zvt.

Warapande, V.; Meng, F.; Bozan, A.; Graff, D. E.; Fromer, J. C.; Mughal, K.; Mohideen, F. K.; Shivangi; Paruchuri, S.; Johnston, M. L.; Sharma, P.; Crea, T. R.; Rudraraju, R. S.; George, A.; Folvar, C.; Nelson, A. M.; Neiditch, M. B.; Zimmerman, M. D.; Coley, C. W.; Freundlich, J. S. Identification of Antituberculars with Favorable Potency and Pharmacokinetics through Structure-Based and Ligand-Based Modeling. bioRxiv February 4, 2025. https://doi.org/10.1101/2025.02.03.636334.

Bradshaw, J.; Zhang, A.; Mahjour, B.; Graff, D. E.; Segler, M. H. S.; Coley, C. W. Challenging Reaction Prediction Models to Generalize to Novel Chemistry. arXiv January 11, 2025. https://doi.org/10.48550/arXiv.2501.06669.

Tu, Z.; Choure, S. J.; Fong, M. H.; Roh, J.; Levin, I.; Yu, K.; Joung, J. F.; Morgan, N.; Li, S.-C.; Sun, X.; Lin, H.; Murnin, M.; Liles, J. P.; Struble, T. J.; Fortunato, M. E.; Liu, M.; Green, W. H.; Jensen, K. F.; Coley, C. W. ASKCOS: An Open Source Software Suite for Synthesis Planning. arXiv January 3, 2025. https://doi.org/10.48550/arXiv.2501.01835.

Leenhouts, R. J.; Morgan, N.; Ibrahim, E. A.; Green, W. H.; Vermeire, F. H. Pooling Solvent Mixtures for Solvation Free Energy Predictions. arXiv December 11, 2024. https://doi.org/10.48550/arXiv.2412.01982.

Zheng, Z.; Florit, F.; Jin, B.; Wu, H.; Li, S.-C.; Nandiwale, K. Y.; Salazar, C. A.; Mustakis, J. G.; Green, W. H.; Jensen, K. F. Integrating Machine Learning and Large Language Models to Advance Exploration of Electrochemical Reactions. Angewandte Chemie International Edition 2024, e202418074. https://doi.org/10.1002/anie.202418074.

Casetti, N.; Nevatia, P.; Chen, J.; Schwaller, P.; Coley, C. W. Comment on “Molecular Hypergraph Neural Networks” [J. Chem. Phys. 160, 144307 (2024)]. The Journal of Chemical Physics 2024, 161 (20), 207101. https://doi.org/10.1063/5.0239722.

Wohlwend, J.; Corso, G.; Passaro, S.; Reveiz, M.; Leidal, K.; Swiderski, W.; Portnoi, T.; Chinn, I.; Silterra, J.; Jaakkola, T.; Barzilay, R. Boltz-1 Democratizing Biomolecular Interaction Modeling. bioRxiv November 20, 2024. https://doi.org/10.1101/2024.11.19.624167.

Wang, C.; Gupta, S.; Zhang, X.; Tonekaboni, S.; Jegelka, S.; Jaakkola, T.; Uhler, C. An Information Criterion for Controlled Disentanglement of Multimodal Data. arXiv October 31, 2024. https://doi.org/10.48550/arXiv.2410.23996.

Holderrieth, P.; Xu, Y.; Jaakkola, T. Hamiltonian Score Matching and Generative Flows. arXiv October 27, 2024. https://doi.org/10.48550/arXiv.2410.20470.

Adams, K.; Abeywardane, K.; Fromer, J.; Coley, C. W. ShEPhERD: Diffusing Shape, Electrostatics, and Pharmacophores for Bioisosteric Drug Design. arXiv October 22, 2024. https://doi.org/10.48550/arXiv.2411.04130.

Li, P.; Wu, M. Learning to Refine Domain Knowledge for Biological Network Inference. arXiv October 18, 2024. https://doi.org/10.48550/arXiv.2410.14436.

Fromer, J.; Wang, R.; Manjrekar, M.; Tripp, A.; Hernández-Lobato, J. M.; Coley, C. W. Batched Bayesian Optimization with Correlated Candidate Uncertainties. arXiv October 8, 2024. https://doi.org/10.48550/arXiv.2410.06333.

Wu, M.; Padia, U.; Murphy, S. H.; Barzilay, R.; Jaakkola, T. Predicting Perturbation Targets with Causal Differential Networks. arXiv October 4, 2024. https://doi.org/10.48550/arXiv.2410.03380.

Gao, W.; Luo, S.; Coley, C. W. Generative Artificial Intelligence for Navigating Synthesizable Chemical Space. arXiv October 4, 2024. https://doi.org/10.48550/arXiv.2410.03494.

Jing, B.; Stärk, H.; Jaakkola, T.; Berger, B. Generative Modeling of Molecular Dynamics Trajectories. arXiv September 26, 2024. https://doi.org/10.48550/arXiv.2409.17808.

Pracht, P.; Pillai, Y.; Kapil, V.; Csányi, G.; Gönnheimer, N.; Vondrák, M.; Margraf, J. T.; Wales, D. J. Efficient Composite Infrared Spectroscopy: Combining the Doubly-Harmonic Approximation with Machine Learning Potentials. arXiv.org. https://arxiv.org/abs/2408.08174v1.

Zheng, J.; Leito, I.; Green, W. Widespread Misinterpretation of pKa Terminology and Its Consequences. ChemRxiv August 8, 2024. https://doi.org/10.26434/chemrxiv-2024-msd0q-v2.

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. Angewandte Chemie International Edition 2024. https://doi.org/10.1002/anie.202411296.

Yu, K.; Roh, J.; Li, Z.; Gao, W.; Wang, R.; Coley, C. W. Double-Ended Synthesis Planning with Goal-Constrained Bidirectional Search. arXiv.org. https://arxiv.org/abs/2407.06334v1 (accessed 2024-09-06).

Stark, H.; Jing, B.; Barzilay, R.; Jaakkola, T. Harmonic Self-Conditioned Flow Matching for Joint Multi-Ligand Docking and Binding Site Design; ICML, 2024. https://openreview.net/forum?id=XTrMY9sHKF.

Fu, X.; Rosen, A.; Bystrom, K.; Wang, R.; Musaelian, A.; Kozinsky, B.; Smidt, T.; Jaakkola, T. A Recipe for Charge Density Prediction. arXiv May 29, 2024. https://doi.org/10.48550/arXiv.2405.19276.

Gupta, S.; Wang, C.; Wang, Y.; Jaakkola, T.; Jegelka, S. In-Context Symmetries: Self-Supervised Learning through Contextual World Models. arXiv May 28, 2024. https://doi.org/10.48550/arXiv.2405.18193.

Greenman, K. P. Optical Property Prediction and Molecular Discovery through Multi-Fidelity Deep Learning and Computational Chemistry. Thesis, Massachusetts Institute of Technology, 2024. https://dspace.mit.edu/handle/1721.1/155385.

Mahjour, B. A.; Coley, C. W. RDCanon: A Python Package for Canonicalizing the Order of Tokens in SMARTS Queries. J. Chem. Inf. Model. 2024, 64 (8), 2948–2954. https://doi.org/10.1021/acs.jcim.4c00138.

Fan, V.; Qian, Y.; Wang, A.; Wang, A.; Coley, C. W.; Barzilay, R. OpenChemIE: An Information Extraction Toolkit For Chemistry Literature. arXiv April 1, 2024. https://doi.org/10.48550/arXiv.2404.01462.

Corso, G.; Stark, H.; Jegelka, S.; Jaakkola, T.; Barzilay, R. Graph Neural Networks. Nat Rev Methods Primers 2024, 4 (1), 1–13. https://doi.org/10.1038/s43586-024-00294-7.

Corso, G.; Deng, A.; Fry, B.; Polizzi, N.; Barzilay, R.; Jaakkola, T. Deep Confident Steps to New Pockets: Strategies for Docking Generalization. arXiv February 28, 2024. https://doi.org/10.48550/arXiv.2402.18396.

Li, S.-C.; Wu, H.; Menon, A.; Spiekermann, K.; Li, Y.-P.; Green, W. When Do Quantum Mechanical Descriptors Help Graph Neural Networks Predict Chemical Properties? ChemRxiv February 20, 2024. https://doi.org/10.26434/chemrxiv-2024-7q438.

Meijer, D.; A. Beniddir, M.; W. Coley, C.; M. Mejri, Y.; Öztürk, M.; Hooft, J. J. J. van der; H. Medema, M.; Skiredj, A. Empowering Natural Product Science with AI: Leveraging Multimodal Data and Knowledge Graphs. Natural Product Reports 2024. https://doi.org/10.1039/D4NP00008K.

Stark, H.; Jing, B.; Wang, C.; Corso, G.; Berger, B.; Barzilay, R.; Jaakkola, T. Dirichlet Flow Matching with Applications to DNA Sequence Design. arXiv February 8, 2024. https://doi.org/10.48550/arXiv.2402.05841.

Campbell, A.; Yim, J.; Barzilay, R.; Rainforth, T.; Jaakkola, T. Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design. arXiv February 7, 2024. https://doi.org/10.48550/arXiv.2402.04997.

Wright, R. Explainer: The Roots and Realities of 10 Conflicts in the Middle East | Wilson Center. https://www.wilsoncenter.org/article/explainer-roots-and-realities-10-conflicts-middle-east (accessed 2024-07-02).

Canty, R. B. Autonomous Experimentation for Molecular Discovery Applications. Thesis, Massachusetts Institute of Technology, 2024. https://dspace.mit.edu/handle/1721.1/153673 (accessed 2024-05-28).

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.

Yim, J.; Stärk, H.; Corso, G.; Jing, B.; Barzilay, R.; Jaakkola, T. S. Diffusion Models in Protein Structure and Docking. WIREs Computational Molecular Science 2024, 14 (2), e1711. https://doi.org/10.1002/wcms.1711.

McDonald, M. A.; Koscher, B. A.; Canty, R. B.; Jensen, K. F. Calibration-Free Reaction Yield Quantification by HPLC with a Machine-Learning Model of Extinction Coefficients. Chemical Science 2024. https://doi.org/10.1039/D4SC01881H.

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.

Qian, Y.; Li, Z.; Tu, Z.; Coley, C.; Barzilay, R. Predictive Chemistry Augmented with Text Retrieval. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing; Bouamor, H., Pino, J., Bali, K., Eds.; Association for Computational Linguistics: Singapore, 2023; pp 12731–12745. https://aclanthology.org/2023.emnlp-main.784.

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.

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. Org. Process Res. Dev. 2023, 27 (11), 1868–1879. https://doi.org/10.1021/acs.oprd.3c00229.

Fromer, J. C.; Coley, C. W. An Algorithmic Framework for Synthetic Cost-Aware Decision Making in Molecular Design. arXiv November 13, 2023. https://doi.org/10.48550/arXiv.2311.02187.

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.

Koscher, B.; 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, Multi-Property-Driven Molecular Discovery: From Predictions to Measurements and Back. ChemRxiv April 13, 2023. https://doi.org/10.26434/chemrxiv-2023-r7b01.

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.

Sankaranarayanan, K.; Jensen, K. F. Computer-Assisted Multistep Chemoenzymatic Retrosynthesis Using a Chemical Synthesis Planner – Chemical Science (RSC Publishing). Chemical Science 2023, 14, 6467–6475. https://doi.org/10.1039/D3SC01355C.

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.

Goldman, S.; Wohlwend, J.; Stražar, M.; Haroush, G.; Xavier, R. J.; Coley, C. W. Annotating Metabolite Mass Spectra with Domain-Inspired Chemical Formula Transformers. bioRxiv December 31, 2022, p 2022.12.30.522318. https://doi.org/10.1101/2022.12.30.522318.

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.

Fu, T.; Gao, W.; Coley, C. W.; Sun, J. Reinforced Genetic Algorithm for Structure-Based Drug Design; 2022.

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.

Vermeire, F. H.; Chung, Y.; Green, W. H. Predicting Solubility Limits of Organic Solutes for a Wide Range of Solvents and Temperatures. J. Am. Chem. Soc. 2022, 144 (24), 10785–10797. https://doi.org/10.1021/jacs.2c01768.

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.

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 [cs] 2022.

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.

Wang, Y.; Wang, J.; Cao, Z.; Farimani, A. B. Molecular Contrastive Learning of Representations via Graph Neural Networks. Nat Mach Intell 2022, 4 (3), 279–287. https://doi.org/10.1038/s42256-022-00447-x.

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.

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