134 citations · 326 across the 6 of their papers we have counts for
7 papers
An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries
Aryan Pedawi, Pawel Gniewek, Chaoyi Chang +2
Virtual, make-on-demand chemical libraries have transformed early-stage drug discovery by unlocking vast, synthetically accessible regions of chemical space. Recent years have witn…
Learning physics confers pose-sensitivity in structure-based virtual screening
Pawel Gniewek, Bradley Worley, Kate Stafford +2
In drug discovery, structure-based virtual high-throughput screening (vHTS) campaigns aim to identify bioactive ligands or "hits" for therapeutic protein targets from docked poses…
ATOM3D: Tasks On Molecules in Three Dimensions
Raphael J. L. Townshend, Martin Vögele, Patricia Suriana +10
Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural net…
A community-powered search of machine learning strategy space to find NMR property prediction models
Lars A. Bratholm, Will Gerrard, Brandon Anderson +26
The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML st…
Lorentz Group Equivariant Neural Network for Particle Physics
Alexander Bogatskiy, Brandon Anderson, Jan T. Offermann +3
We present a neural network architecture that is fully equivariant with respect to transformations under the Lorentz group, a fundamental symmetry of space and time in physics. The…
Cormorant: Covariant Molecular Neural Networks
Brandon Anderson, Truong-Son Hy, Risi Kondor
We propose Cormorant, a rotationally covariant neural network architecture for learning the behavior and properties of complex many-body physical systems. We apply these networks t…
Covariant Compositional Networks For Learning Graphs
Risi Kondor, Hy Truong Son, Horace Pan +2
Most existing neural networks for learning graphs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors…