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Found 3 papers, 1 papers with code
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders
A critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks aimed at optimizing specific molecular properties.
Leveraging Active Subspaces to Capture Epistemic Model Uncertainty in Deep Generative Models for Molecular Design
Deep generative models have been accelerating the inverse design process in material and drug design.
Multi-Objective Latent Space Optimization of Generative Molecular Design Models
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.
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