408 citations · 464 across the 3 of their papers we have counts for
6 papers
Pursuing a Prospective Perspective
Steven Kearnes
Retrospective testing of predictive models does not consider the real-world context in which models are deployed. Prospective validation, on the other hand, enables meaningful comp…
Machine learning on DNA-encoded libraries: A new paradigm for hit-finding
Kevin McCloskey, Eric A. Sigel, Steven Kearnes +16
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up…
Decoding Molecular Graph Embeddings with Reinforcement Learning
Steven Kearnes, Li Li, Patrick Riley
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previous…
Optimization of Molecules via Deep Reinforcement Learning
Zhenpeng Zhou, Steven Kearnes, Li Li +2
We present a framework, which we call Molecule Deep -Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement l…
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Nathaniel Thomas, Tess Smidt, Steven Kearnes +4
We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer. 3D rotation equivariance removes…
Massively Multitask Networks for Drug Discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley +3
Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources. To train these architec…