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Found 4 papers, 3 papers with code
Multi-scale Iterative Refinement towards Robust and Versatile Molecular Docking
This model is then paired with GPU-accelerated sampling algorithms.
A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design
Recent developments in geometric deep learning, focusing on the integration and processing of 3D geometric data, coupled with the availability of accurate protein 3D structure predictions from tools like AlphaFold, have greatly advanced the field of structure-based drug design.
Multi-task Bioassay Pre-training for Protein-ligand Binding Affinity Prediction
By introducing multi-task pre-training to treat the prediction of different affinity labels as different tasks and classifying relative rankings between samples from the same bioassay, MBP learns robust and transferrable structural knowledge from our new ChEMBL-Dock dataset with varied and noisy labels.
An adaptive graph learning method for automated molecular interactions and properties predictions
In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized their architectures and hyperparameters, making them lose advantage in repurposing to new data generated in drug discovery.
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