187 citations · 287 across the 8 of their papers we have counts for
9 papers
Atom-by-atom protein generation and beyond with language models
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Protein language models learn powerful representations directly from sequences of amino acids. However, they are constrained to generate proteins with only the set of amino acids r…
Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files
Daniel Flam-Shepherd, Alán Aspuru-Guzik
Language models are powerful tools for molecular design. Currently, the dominant paradigm is to parse molecular graphs into linear string representations that can easily be trained…
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…
Keeping it Simple: Language Models can learn Complex Molecular Distributions
Daniel Flam-Shepherd, Kevin Zhu, Alán Aspuru-Guzik
Deep generative models of molecules have grown immensely in popularity, trained on relevant datasets, these models are used to search through chemical space. The downstream utility…
Learning quantum dynamics with latent neural ODEs
Matthew Choi, Daniel Flam-Shepherd, Thi Ha Kyaw +1
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question. In the area of quan…
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
Daniel Flam-Shepherd, Tony Wu, Xuemei Gu +3
Quantum physics experiments produce interesting phenomena such as interference or entanglement, which are core properties of numerous future quantum technologies. The complex relat…
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…