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Found 3 papers, 1 papers with code
Generating Realistic 3D Molecules with an Equivariant Conditional Likelihood Model
In this paper, we present GEN3D, a model that concurrently generates molecular graphs and 3D geometries, and is equivariant to rotations, translations, and atom permutations.
Efficient hyperparameter optimization by way of PAC-Bayes bound minimization
Identifying optimal values for a high-dimensional set of hyperparameters is a problem that has received growing attention given its importance to large-scale machine learning applications such as neural architecture search.
A deep-learning view of chemical space designed to facilitate drug discovery
Drug discovery projects entail cycles of design, synthesis, and testing that yield a series of chemically related small molecules whose properties, such as binding affinity to a given target protein, are progressively tailored to a particular drug discovery goal.
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