Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design

no code implementations • 30 Apr 2024 • A N M Nafiz Abeer, Sanket Jantre, Nathan M Urban, Byung-Jun Yoon

Deep generative models have been accelerating the inverse design process in material and drug design.

Bayesian Inference Uncertainty Quantification

Multi-Objective Latent Space Optimization of Generative Molecular Design Models

1 code implementation • 1 Mar 2022 • A N M Nafiz Abeer, Nathan Urban, M Ryan Weil, Francis J. Alexander, Byung-Jun Yoon

Molecular design based on generative models, such as variational autoencoders (VAEs), has become increasingly popular in recent years due to its efficiency for exploring high-dimensional molecular space to identify molecules with desired properties.