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Found 11 papers, 4 papers with code
AAVDiff: Experimental Validation of Enhanced Viability and Diversity in Recombinant Adeno-Associated Virus (AAV) Capsids through Diffusion Generation
Additionally, in the absence of AAV9 capsid data, apart from one wild-type sequence, we used the same model to directly generate a number of viable sequences with up to 9 mutations.
Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation
Most earlier 3D structure-based molecular generation approaches follow an atom-wise paradigm, incrementally adding atoms to a partially built molecular fragment within protein pockets.
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.
Multiscale Topology in Interactomic Network: From Transcriptome to Antiaddiction Drug Repurposing
This study embarked on an innovative and rigorous strategy to unearth potential drug repurposing candidates for opioid and cocaine addiction treatment, bridging the gap between transcriptomic data analysis and drug discovery.
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.
Delete: Deep Lead Optimization Enveloped in Protein Pocket through Unified Deleting Strategies and a Structure-aware Network
Drug discovery is a highly complicated process, and it is unfeasible to fully commit it to the recently developed molecular generation methods.
Highly accurate and efficient deep learning paradigm for full-atom protein loop modeling with KarmaLoop
Protein loop modeling is the most challenging yet highly non-trivial task in protein structure prediction.
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.
Recent advances in artificial intelligence for retrosynthesis
Afterwards, we analyze these methods in terms of their mechanism and performance, and introduce popular evaluation metrics for them, in which we also provide a detailed comparison among representative methods on several public datasets.
Root-aligned SMILES: A Tight Representation for Chemical Reaction Prediction
Chemical reaction prediction, involving forward synthesis and retrosynthesis prediction, is a fundamental problem in organic synthesis.
