Regio-selectivity prediction with a machine-learned reaction representation and on-the-fly quantum mechanical descriptors (ChemRxiv 2020)
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)
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)
Generative Models for Graph-Based Protein Design (ICLR Workshop, 2019)
John Ingraham, Vikas Garg, Regina Barzilay, Tommi Jaakkola
Autonomous discovery in the chemical sciences part I: Progress (Angew. Chem. Int. Ed., 2020)
Connor W. Coley, Natalie S. Eyke, Klavs F. Jensen
Autonomous discovery in the chemical sciences part II: Outlook (Angew. Chem. Int. Ed., 2020)
Connor W. Coley, Natalie S. Eyke, 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, Klavs F. Jensen
Path-Augmented Graph Transformer Network (arXiv preprint, 2019)
Benson Chen, Regina Barzilay, 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, 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, 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, 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, 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