Connor W. Coley
Connor W. Coley
Department of Chemical Engineering, MIT
Подтвержден адрес электронной почты в домене mit.edu - Главная страница
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
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
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
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
Machine learning in computer-aided synthesis planning
CW Coley, WH Green, KF Jensen
Accounts of chemical research 51 (5), 1281-1289, 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
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
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
Computer-assisted retrosynthesis based on molecular similarity
CW Coley, L Rogers, WH Green, KF Jensen
ACS central science 3 (12), 1237-1245, 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
Autonomous discovery in the chemical sciences part I: Progress
CW Coley, NS Eyke, KF Jensen
arXiv preprint arXiv:2003.13754, 2020
The synthesizability of molecules proposed by generative models
W Gao, CW Coley
Journal of chemical information and modeling 60 (12), 5714-5723, 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
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
Autonomous discovery in the chemical sciences part II: Outlook
CW Coley, NS Eyke, KF Jensen
Angewandte Chemie International Edition, 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
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
Accelerating high-throughput virtual screening through molecular pool-based active learning
DE Graff, EI Shakhnovich, CW Coley
Chemical science 12 (22), 7866-7881, 2021
Current and future roles of artificial intelligence in medicinal chemistry synthesis
TJ Struble, JC Alvarez, SP Brown, M Chytil, J Cisar, RL DesJarlais, ...
Journal of medicinal chemistry 63 (16), 8667-8682, 2020
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
В данный момент система не может выполнить эту операцию. Повторите попытку позднее.
Статьи 1–20