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Found 9 papers, 3 papers with code
Atom-by-atom protein generation and beyond with language models
However, they are constrained to generate proteins with only the set of amino acids represented in their vocabulary.
Language models can generate molecules, materials, and protein binding sites directly in three dimensions as XYZ, CIF, and PDB files
In doing so, we demonstrate that it is not necessary to use simplified molecular representations to train chemical language models -- that they are powerful generative models capable of directly exploring chemical space in three dimensions for very different structures.
Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning
We introduce a novel RL framework for scalable 3D design that uses a hierarchical agent to build molecules by placing molecular substructures sequentially in 3D space, thus attempting to build on the existing human knowledge in the field of molecular design.
Keeping it Simple: Language Models can learn Complex Molecular Distributions
In this work, we investigate the capacity of simple language models to learn distributions of molecules.
Learning quantum dynamics with latent neural ODEs
The core objective of machine-assisted scientific discovery is to learn physical laws from experimental data without prior knowledge of the systems in question.
Learning Interpretable Representations of Entanglement in Quantum Optics Experiments using Deep Generative Models
The complex relationship between the setup structure of a quantum experiment and its entanglement properties is essential to fundamental research in quantum optics but is difficult to intuitively understand.
Bayesian Variational Optimization for Combinatorial Spaces
This paper focuses on Bayesian Optimization in combinatorial spaces.
Neural Message Passing on High Order Paths
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 functional groups and molecular geometry.
Graph Deconvolutional Generation
Graph generation is an extremely important task, as graphs are found throughout different areas of science and engineering.
