Publications

Evaluating and clustering retrosynthesis pathways with learned strategy (Chem. Sci., 2021, Advance Article)

Yiming Mo, Yanfei Guan, Pritha Verma, Jiang Guo, Mike E. Fortunato, Zhaohong Lu, Connor W. Coley and Klavs F. Jensen


Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors  (Chem. Sci., 2021, Advance Article)

Yanfei Guan, Connor W. Coley, Haoyang Wu, Duminda Ranasinghe, Esther Heid, Thomas J. Struble, Lagnajit Pattanaik, William H. Green and Klavs F. Jensen

Direct Optimization across Computer-Generated Reaction Networks Balances Materials Use and Feasibility of Synthesis Plans for Molecule Libraries (Journal of Chemical Information and Modeling, 2020)

Hanyu Gao, Jean Pauphilet, Thomas J. Struble, Connor W. Coley, and Klavs F. Jensen


Optimal Transport Graph Neural Networks (ChemRxiv 2020)

Gary Bécigneul, Octavian-Eugen Ganea, Benson Chen, Regina Barzilay, and Tommi Jaakkola


Enforcing Predictive Invariance across Structured Biomedical Domains (ChemRxiv 2020)

Wengong Jin, Regina Barzilay, and Tommi Jaakkola


Uncertainty Quantification Using Neural Networks for Molecular Property Prediction (J. Chem. Inf. Model., 2020, 60, 8, 3770–3780)

Lior Hirschfeld, Kyle Swanson, Kevin Yang, Regina Barzilay, and Connor W. Coley


Evaluating Scalable Uncertainty Estimation Methods for Deep Learning-Based Molecular Property Prediction (J. Chem. Inf. Model., 2020, 60, 6, 2697–2717)

Gabriele Scalia, Colin A. Grambow, Barbara Pernici, Yi-Pei Li, and William H. Green


The Synthesizability of Molecules Proposed by Generative Models (J. Chem. Inf. Model, 2020)

Wenhao Gao and Connor W. Coley


A Deep Learning Approach to Antibiotic discovery (Cell 2020, 180, 4, 688-702)

Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackermann, Victoria M. Tran, Anush Chiappino-Pepe, Ahmed H. Badran, Ian W. Andrews, Emma J. Chory, George M. Church, Eric D. Brown, Tommi S. Jaakkola, Regina Barzilay, and James J.Collins


Modeling Drug Combinations based on Molecular Structures and Biological Targets (NeuralPS Machine Learning for Molecules Workshop, 2020)

Wengong Jin, Kyle Swanson, Regina Barzilay, and Tommi Jaakkola


Heirarchical Generation of Molecular Graphs using Structural Motifs (International Conference on Machine Learning, 2020)

Wengong Jin, Kyle Swanson, Regina Barzilay, and Tommi Jaakkola


Multi-Objective Molecule Generation using Interpretable Substructures (International Conference on Machine Learning, 2020)

Wengong Jin, Kyle Swanson, Regina Barzilay, and Tommi Jaakkola


Improving Molecular Design by Stochastic Iterative Target Augmentation (International Conference on Machine Learning, 2020)

Kevin Yang, Wengong Jin, Kyle Swanson, Regina Barzilay, and Tommi Jaakkola


Iterative experimental design based on active machine learning reduces the experimental burden associated with reaction screening (React. Chem. Eng. 2020)


Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning, (Chem. Sci. 2020)


Data augmentation and pretraining for template-based retrosynthetic prediction in computer-aided synthesis planning (J. Chem. Inf. Model. 2020, 60, 7, 3398–3407)

Michael E. Fortunato, Connor W. ColeyBrian C. Barnes, and Klavs F. Jensen

Current and future roles of artificial intelligence in medicinal chemistry synthesis (J. Med. Chem., J. Med. Chem. 2020, 63, 8667−8682)

Thomas J. Struble, Juan C. Alvarez, Scott P. Brown, Milan Chytil, Justin Cisar, >Renee L. DesJarlais, Ola Engkvist, Scott A. Frank, Daniel R. Greve, Daniel J. Griffin, Xinjun Hou, Jeffrey W. Johannes, Constantine Kreatsoulas, Brian Lahue, Miriam Mathea, Georg Mogk, Christos A. Nicolaou, Andrew D. Palmer, Daniel J. Price, Richard I. Robinson, Sebastian Salentin, Li Xing, Tommi Jaakkola, William. H. Green, Regina Barzilay, Connor W. Coley and Klavs F. Jensen


Combining retrosynthesis and mixed-integer optimization for minimizing the chemical inventory needed to realize a WHO essential medicines list (React. Chem. Eng., 2020, 5, 367-376)

Hanyu Gao, Connor W. Coley, Thomas J. Struble, Linyan Li, Yujie Qian, William H. Green and Klavs F. Jensen


Generative Models for Graph-Based Protein Design (ICLR Workshop, 2019)

John Ingraham, Vikas Garg, Regina Barzilay, and Tommi Jaakkola


Autonomous discovery in the chemical sciences part I: Progress (Angew. Chem. Int. Ed., 2020)

Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen


Autonomous discovery in the chemical sciences part II: Outlook (Angew. Chem. Int. Ed., 2020)

Connor W. Coley, Natalie S. Eyke, and Klavs F. Jensen


Multitask Prediction of Site Selectivity in Aromatic C-H Functionalization Reactions (React. Chem. Eng., 2020, 5, 896)

Thomas J. Struble, Connor W. Coley, and Klavs F. Jensen


Path-Augmented Graph Transformer Network (arXiv preprint, 2019)

Benson Chen, Regina Barzilay, and Tommi Jaakkola


Analyzing Learned Molecular Representations for Property Prediction (JCIM, 2019)

Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay


RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application (J. Chem. Inf. Model., 2019)

Connor W. Coley, William H. Green, and Klavs F. Jensen


Learning Multimodal Graph-to-Graph Translation for Molecular Optimization (ICLR, 2019)

Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola


Self-Evolving Machine: A Continuously Improving Model for Molecular Thermochemistry (J. Phys. Chem. A, 2019)

Yi-Pei Li, Kehang Han, Colin A. Grambow, and William H. Green


A graph-convolutional neural network model for the prediction of chemical reactivity (Chemical Science, 2018)

Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen


Using Machine Learning To Predict Suitable Conditions for Organic Reactions (ACS Central Science, 2018)

Hanyu Gao, Thomas J. Struble, Connor W. Coley, Yuran Wang, William H. Green, and Klavs F. Jensen


Machine Learning in Computer-Aided Synthesis Planning (Accounts of Chemical Research, 2018)

Connor W. Coley, William H. Green, and Klavs F. Jensen


Junction Tree Variational Autoencoder for Molecular Graph Generation (ICML 2018)

Wengong Jin, Regina Barzilay, and Tommi Jaakkola


SCScore: Synthetic Complexity Learned from a Reaction Corpus (Journal of Chemical Information and Modeling, 2018)

Connor W. Coley, Luke Rogers, William H. Green, and Klavs F. Jensen


Computer-Assisted Retrosynthesis Based on Molecular Similarity (ACS Central Science, 2017)

Connor W. Coley, Luke Rogers, William H. Green, and Klavs F. Jensen


Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network (NIPS 2017)

Wengong Jin, Connor W. Coley, Regina Barzilay, and Tommi S. Jaakkola


Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction (Journal of Chemical Information and Modeling, 2017)

Connor W. Coley, Regina Barzilay, William H. Green, Tommi S. Jaakkola, and Klavs F. Jensen


Prediction of Organic Reaction Outcomes Using Machine Learning (ACS, 2017)

Connor W. Coley, Regina Barzilay, Tommi S. Jaakkola, William H. Green, and Klavs F. Jensen


Deriving Neural Architectures from Sequence and Graph Kernels (ICML, 2017)

Tao Lei, Wengong Jin, Regina Barzilay, and Tommi S. Jaakkola