12 citations · 21 across the 3 of their papers we have counts for
4 papers
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
Daniel Flam-Shepherd, Alexander Zhigalin, Alán Aspuru-Guzik
Machine learning has the potential to automate molecular design and drastically accelerate the discovery of new functional compounds. Towards this goal, generative models and reinf…
Bayesian Variational Optimization for Combinatorial Spaces
Tony C. Wu, Daniel Flam-Shepherd, Alán Aspuru-Guzik
This paper focuses on Bayesian Optimization in combinatorial spaces. In many applications in the natural science. Broad applications include the study of molecules, proteins, DNA,…
Neural Message Passing on High Order Paths
Daniel Flam-Shepherd, Tony Wu, Pascal Friederich +1
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as func…
Graph Deconvolutional Generation
Daniel Flam-Shepherd, Tony Wu, Alan Aspuru-Guzik
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering. In this work, we focus on the modern equivalent of the E…