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Found 4 papers, 2 papers with code
Generative AI for Controllable Protein Sequence Design: A Survey
To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms.
From molecules to scaffolds to functional groups: building context-dependent molecular representation via multi-channel learning
Self-supervised learning (SSL) has emerged as a popular solution, utilizing large-scale, unannotated molecular data to learn a foundational representation of chemical space that might be advantageous for downstream tasks.
MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation
However, limited attention is paid to hierarchical generative models, which can exploit the inherent hierarchical structure (with rich semantic information) of the molecular graphs and generate complex molecules of larger size that we shall demonstrate to be difficult for most existing models.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets
Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space.
