Search Results for author: Rafael Gomez-Bombarelli

Found 7 papers, 4 papers with code

An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming

1 code implementation15 May 2021 Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang

Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.

Bilevel Optimization

Chemistry-informed Macromolecule Graph Representation for Similarity Computation and Supervised Learning

no code implementations ICLR Workshop GTRL 2021 Somesh Mohapatra, Joyce An, Rafael Gomez-Bombarelli

Macromolecules are large, complex molecules composed of covalently bonded monomer units, existing in different stereochemical configurations and topologies.

Decision Making

Molecular machine learning with conformer ensembles

1 code implementation15 Dec 2020 Simon Axelrod, Rafael Gomez-Bombarelli

Here we investigate how the 3D information of multiple conformers, traditionally known as 4D information in the cheminformatics community, can improve molecular property prediction in deep learning models.

Drug Discovery Molecular Property Prediction

Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks

2 code implementations28 Jul 2020 Jurgis Ruza, Wujie Wang, Daniel Schwalbe-Koda, Simon Axelrod, William H. Harris, Rafael Gomez-Bombarelli

The potential of mean force is expressed as two jointly-trained neural network interatomic potentials that learn the coupled short-range and the many-body long range molecular interactions.

Computational Physics Materials Science

GEOM: Energy-annotated molecular conformations for property prediction and molecular generation

1 code implementation9 Jun 2020 Simon Axelrod, Rafael Gomez-Bombarelli

The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133, 000 species from QM9, and 317, 000 species with experimental data related to biophysics, physiology, and physical chemistry.

Transfer Learning

Graph similarity drives zeolite diffusionless transformations and intergrowth

no code implementations6 Dec 2018 Daniel Schwalbe-Koda, Zach Jensen, Elsa Olivetti, Rafael Gomez-Bombarelli

Predicting and directing polymorphic transformations is a critical challenge in zeolite synthesis.

Graph Similarity Materials Science

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