Publications


Corso, G.; Stärk, H.; Jing, B.; Barzilay, R.; Jaakkola, T. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. 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. 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. NeurIPS June 1, 2022. https://doi.org/10.48550/arXiv.2206.01729.

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.

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; 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.


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.


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.


Coley, C. W.; Green, W. H.; Jensen, K. F. Machine Learning in Computer-Aided Synthesis Planning. Acc. Chem. Res. 2018, 51 (5), 1281–1289. https://doi.org/10.1021/acs.accounts.8b00087.


Coley, C. W.; Rogers, L.; Green, W. H.; Jensen, K. F. SCScore: Synthetic Complexity Learned from a Reaction Corpus. J. Chem. Inf. Model. 2018, 58 (2), 252–261. https://doi.org/10.1021/acs.jcim.7b00622.


Jin, W.; Coley, C. W.; Barzilay, R.; Jaakkola, T. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. arXiv December 29, 2017. https://doi.org/10.48550/arXiv.1709.04555.


Coley, C. W.; Rogers, L.; Green, W. H.; Jensen, K. F. Computer-Assisted Retrosynthesis Based on Molecular Similarity. ACS Cent. Sci. 2017, 3 (12), 1237–1245. https://doi.org/10.1021/acscentsci.7b00355.


Lei, T.; Jin, W.; Barzilay, R.; Jaakkola, T. Deriving Neural Architectures from Sequence and Graph Kernels. arXiv October 30, 2017. https://doi.org/10.48550/arXiv.1705.09037.


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 2017, 57 (8), 1757–1772. https://doi.org/10.1021/acs.jcim.6b00601.


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. 2017, 3 (5), 434–443. https://doi.org/10.1021/acscentsci.7b00064.