1 code implementation • 3 Mar 2022 • He Ma, Arunachalam Narayanaswamy, Patrick Riley, Li Li
Systematic development of accurate density functionals has been a decades-long challenge for scientists.
1 code implementation • 17 Sep 2020 • Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin D. Cubuk, Patrick Riley, Kieron Burke
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures.
2 code implementations • ICLR 2021 • Subham Sekhar Sahoo, Subhashini Venugopalan, Li Li, Rishabh Singh, Patrick Riley
In this work, we propose a technique for combining gradient-based methods with symbolic techniques to scale such analyses and demonstrate its application for model explanation.
no code implementations • 31 Jan 2020 • Kevin McCloskey, Eric A. Sigel, Steven Kearnes, Ling Xue, Xia Tian, Dennis Moccia, Diana Gikunju, Sana Bazzaz, Betty Chan, Matthew A. Clark, John W. Cuozzo, Marie-Aude Guié, John P. Guilinger, Christelle Huguet, Christopher D. Hupp, Anthony D. Keefe, Christopher J. Mulhern, Ying Zhang, Patrick Riley
We demonstrate a new approach applying machine learning to DEL selection data by identifying active molecules from a large commercial collection and a virtual library of easily synthesizable compounds.
no code implementations • 18 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.
1 code implementation • 23 Jan 2019 • Li Li, Minjie Fan, Rishabh Singh, Patrick Riley
The second part, which we call Neural-Guided Monte Carlo Tree Search, uses the network during a search to find an expression that conforms to a set of data points and desired leading powers.
7 code implementations • 19 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)
4 code implementations • 22 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.
2 code implementations • 2 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.
Ranked #11 on
Drug Discovery
on QM9
1 code implementation • 6 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.