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Found 7 papers, 6 papers with code
Adversarial Consistency for Single Domain Generalization in Medical Image Segmentation
An organ segmentation method that can generalize to unseen contrasts and scanner settings can significantly reduce the need for retraining of deep learning models.
Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models
The goal of structure-based drug discovery is to find small molecules that bind to a given target protein.
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models.
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models
We show that valid and unique molecules can be readily sampled from the variational latent space defined by a reference `seed' structure and generated structures have reasonable interactions with the binding site.
Visualizing Convolutional Neural Network Protein-Ligand Scoring
Here we present three methods for visualizing how individual protein-ligand complexes are interpreted by 3D convolutional neural networks.
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks
Docking is an important tool in computational drug discovery that aims to predict the binding pose of a ligand to a target protein through a combination of pose scoring and optimization.
Protein-Ligand Scoring with Convolutional Neural Networks
A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.
