Search Results for author: Steven Kearnes

Found 13 papers, 5 papers with code

Towards understanding retrosynthesis by energy-based models

no code implementations NeurIPS 2021 Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

In this paper, we propose a framework that unifies sequence- and graph-based methods as energy-based models (EBMs) with different energy functions.

Drug Discovery Retrosynthesis

Improving Hit-finding: Multilabel Neural Architecture with DEL

no code implementations NeurIPS Workshop AI4Scien 2021 Kehang Han, Steven Kearnes, Jin Xu, Wen Torng, JW Feng

DNA-Encoded Libraries (DEL thereafter) data, often with millions of data points, enables large deep learning models to make real contributions in the drug discovery process (e. g., hit-finding).

Drug Discovery

Pursuing a Prospective Perspective

no code implementations26 Aug 2020 Steven Kearnes

Retrospective testing of predictive models does not consider the real-world context in which models are deployed.

Energy-based View of Retrosynthesis

no code implementations14 Jul 2020 Ruoxi Sun, Hanjun Dai, Li Li, Steven Kearnes, Bo Dai

Retrosynthesis -- the process of identifying a set of reactants to synthesize a target molecule -- is of vital importance to material design and drug discovery.

Drug Discovery Retrosynthesis +1

Decoding Molecular Graph Embeddings with Reinforcement Learning

no code implementations18 Apr 2019 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.

Graph Matching reinforcement-learning +1

Optimization of Molecules via Deep Reinforcement Learning

6 code implementations19 Oct 2018 Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick Riley

We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions).

 Ranked #1 on Molecular Graph Generation on ZINC (QED Top-3 metric)

Molecular Graph Generation Multi-Objective Reinforcement Learning +2

Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds

3 code implementations22 Feb 2018 Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, Patrick Riley

We introduce tensor field neural networks, which are locally equivariant to 3D rotations, translations, and permutations of points at every layer.

Data Augmentation Translation

Machine learning prediction errors better than DFT accuracy

no code implementations J. Chem. Theory Comput. 2017 Felix A. Faber, Luke Hutchison, Bing Huang, Justin Gilmer, Samuel S. Schoenholz, George E. Dahl, Oriol Vinyals, Steven Kearnes, Patrick F. Riley, O. Anatole von Lilienfeld

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of thirteen electronic ground-state properties of organic molecules.

BIG-bench Machine Learning Drug Discovery +2

Modeling Industrial ADMET Data with Multitask Networks

no code implementations28 Jun 2016 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.

Drug Discovery

ROCS-Derived Features for Virtual Screening

1 code implementation6 Jun 2016 Steven Kearnes, Vijay Pande

Rapid overlay of chemical structures (ROCS) is a standard tool for the calculation of 3D shape and chemical ("color") similarity.

BIG-bench Machine Learning

Molecular Graph Convolutions: Moving Beyond Fingerprints

2 code implementations2 Mar 2016 Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, Patrick Riley

Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.

BIG-bench Machine Learning Drug Discovery +1

Massively Multitask Networks for Drug Discovery

1 code implementation6 Feb 2015 Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, Vijay Pande

Massively multitask neural architectures provide a learning framework for drug discovery that synthesizes information from many distinct biological sources.

Drug Discovery

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