1.7k citations · 4.1k across the 11 of their papers we have counts for
11 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…
Energy-based View of Retrosynthesis
Ruoxi Sun, Hanjun Dai, Li Li +2
Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery. Existing machine le…
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…
Machine learning prediction errors better than DFT accuracy
Felix A. Faber, Luke Hutchison, Bing Huang +7
We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state propert…
Modeling Industrial ADMET Data with Multitask Networks
Steven Kearnes, Brian Goldman, Vijay Pande
Deep learning methods such as multitask neural networks have recently been applied to ligand-based virtual screening and other drug discovery applications. Using a set of industria…
ROCS-Derived Features for Virtual Screening
Steven Kearnes, Vijay Pande
Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted sums to combine many aspects…
Molecular Graph Convolutions: Moving Beyond Fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl +2
Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications. However, fingerprint representati…
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…