408 citations · 438 across the 2 of their papers we have counts for
8 papers
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Li Li, Stephan Hoyer, Ryan Pederson +4
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 architectur…
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…
Quantum Optimization with a Novel Gibbs Objective Function and Ansatz Architecture Search
Li Li, Minjie Fan, Marc Coram +2
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansa…
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…
Neural-Guided Symbolic Regression with Asymptotic Constraints
Li Li, Minjie Fan, Rishabh Singh +1
Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about t…
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…