180 citations · 463 across the 9 of their papers we have counts for
4 papers · 1 filter
Deep Learning the Physics of Transport Phenomena
Amir Barati Farimani, Joseph Gomes, Vijay S. Pande
We have developed a new data-driven paradigm for the rapid inference, modeling and simulation of the physics of transport phenomena by deep learning. Using conditional generative a…
Retrosynthetic reaction prediction using neural sequence-to-sequence models
Bowen Liu, Bharath Ramsundar, Prasad Kawthekar +7
We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end t…
Atomic Convolutional Networks for Predicting Protein-Ligand Binding Affinity
Joseph Gomes, Bharath Ramsundar, Evan N. Feinberg +1
Empirical scoring functions based on either molecular force fields or cheminformatics descriptors are widely used, in conjunction with molecular docking, during the early stages of…
MoleculeNet: A Benchmark for Molecular Machine Learning
Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg +5
Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make…