| Analyzing learned molecular representations for property prediction K Yang, K Swanson, W Jin, C Coley, P Eiden, H Gao, A Guzman-Perez, ... Journal of chemical information and modeling 59 (8), 3370-3388, 2019 | 1521 | 2019 |
| A robotic platform for flow synthesis of organic compounds informed by AI planning CW Coley, DA Thomas III, JAM Lummiss, JN Jaworski, CP Breen, ... Science 365 (6453), eaax1566, 2019 | 844 | 2019 |
| Prediction of organic reaction outcomes using machine learning CW Coley, R Barzilay, TS Jaakkola, WH Green, KF Jensen ACS central science 3 (5), 434-443, 2017 | 729 | 2017 |
| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 713 | 2023 |
| A graph-convolutional neural network model for the prediction of chemical reactivity CW Coley, W Jin, L Rogers, TF Jamison, TS Jaakkola, WH Green, ... Chemical science 10 (2), 370-377, 2019 | 697 | 2019 |
| Machine learning in computer-aided synthesis planning CW Coley, WH Green, KF Jensen Accounts of chemical research 51 (5), 1281-1289, 2018 | 649 | 2018 |
| Convolutional embedding of attributed molecular graphs for physical property prediction CW Coley, R Barzilay, WH Green, TS Jaakkola, KF Jensen Journal of chemical information and modeling 57 (8), 1757-1772, 2017 | 479 | 2017 |
| Using machine learning to predict suitable conditions for organic reactions H Gao, TJ Struble, CW Coley, Y Wang, WH Green, KF Jensen ACS central science 4 (11), 1465-1476, 2018 | 362 | 2018 |
| Computer-assisted retrosynthesis based on molecular similarity CW Coley, L Rogers, WH Green, KF Jensen ACS central science 3 (12), 1237-1245, 2017 | 354 | 2017 |
| Predicting organic reaction outcomes with weisfeiler-lehman network W Jin, C Coley, R Barzilay, T Jaakkola Advances in neural information processing systems 30, 2017 | 344 | 2017 |
| Autonomous discovery in the chemical sciences part I: Progress CW Coley, NS Eyke, KF Jensen arXiv preprint arXiv:2003.13754, 2020 | 320 | 2020 |
| The synthesizability of molecules proposed by generative models W Gao, CW Coley Journal of chemical information and modeling 60 (12), 5714-5723, 2020 | 306 | 2020 |
| SCScore: synthetic complexity learned from a reaction corpus CW Coley, L Rogers, WH Green, KF Jensen Journal of chemical information and modeling 58 (2), 252-261, 2018 | 292 | 2018 |
| Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development K Huang, T Fu, W Gao, Y Zhao, Y Roohani, J Leskovec, CW Coley, ... arXiv preprint arXiv:2102.09548, 2021 | 290 | 2021 |
| Autonomous discovery in the chemical sciences part II: Outlook CW Coley, NS Eyke, KF Jensen Angewandte Chemie International Edition, 2019 | 243 | 2019 |
| Uncertainty quantification using neural networks for molecular property prediction L Hirschfeld, K Swanson, K Yang, R Barzilay, CW Coley Journal of Chemical Information and Modeling 60 (8), 3770-3780, 2020 | 235 | 2020 |
| Accelerating high-throughput virtual screening through molecular pool-based active learning DE Graff, EI Shakhnovich, CW Coley Chemical science 12 (22), 7866-7881, 2021 | 234 | 2021 |
| BigSMILES: a structurally-based line notation for describing macromolecules TS Lin, CW Coley, H Mochigase, HK Beech, W Wang, Z Wang, E Woods, ... ACS central science 5 (9), 1523-1531, 2019 | 214 | 2019 |
| The open reaction database SM Kearnes, MR Maser, M Wleklinski, A Kast, AG Doyle, SD Dreher, ... Journal of the American Chemical Society 143 (45), 18820-18826, 2021 | 211 | 2021 |
| Retrosynthesis prediction with conditional graph logic network H Dai, C Li, C Coley, B Dai, L Song Advances in Neural Information Processing Systems 32, 2019 | 206 | 2019 |
Klavs F.JensenChemical Engineering, MITDirección de correo verificada de mit.edu