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Found 4 papers, 1 papers with code
MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models
As we train our predictive models jointly with a conformer decoder, the new latent embeddings can be mapped to their corresponding inputs, which we call \textit{MoleCLUEs}, or (molecular) counterfactual latent uncertainty explanations \citep{antoran2020getting}.
3D molecule generation by denoising voxel grids
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids.
BOtied: Multi-objective Bayesian optimization with tied multivariate ranks
At the heart of MOBO is the acquisition function, which determines the next candidate to evaluate by navigating the best compromises among the objectives.
SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers
We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers.
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