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Found 5 papers, 2 papers with code
Roughness of molecular property landscapes and its impact on modellability
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space.
Self-focusing virtual screening with active design space pruning
In this study, we propose an extension to the framework of model-guided optimization that mitigates inferences costs using a technique we refer to as design space pruning (DSP), which irreversibly removes poor-performing candidates from consideration.
In-Pocket 3D Graphs Enhance Ligand-Target Compatibility in Generative Small-Molecule Creation
Proteins in complex with small molecule ligands represent the core of structure-based drug discovery.
Analysis of training and seed bias in small molecules generated with a conditional graph-based variational autoencoder -- Insights for practical AI-driven molecule generation
The application of deep learning to generative molecule design has shown early promise for accelerating lead series development.
Combining docking pose rank and structure with deep learning improves protein-ligand binding mode prediction
We develop a deep learning model for binding mode prediction that uses docking ranking as input in combination with docking structures.
