825 citations · 978 across the 6 of their papers we have counts for
6 papers
Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models
Matthew Ragoza, Tomohide Masuda, David Ryan Koes
The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain…
Generating 3D Molecular Structures Conditional on a Receptor Binding Site with Deep Generative Models
Tomohide Masuda, Matthew Ragoza, David Ryan Koes
Deep generative models have been applied with increasing success to the generation of two dimensional molecules as SMILES strings and molecular graphs. In this work we describe for…
Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models
Matthew Ragoza, Tomohide Masuda, David Ryan Koes
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach th…
Visualizing Convolutional Neural Network Protein-Ligand Scoring
Joshua Hochuli, Alec Helbling, Tamar Skaist +2
Protein-ligand scoring is an important step in a structure-based drug design pipeline. Selecting a correct binding pose and predicting the binding affinity of a protein-ligand comp…
Ligand Pose Optimization with Atomic Grid-Based Convolutional Neural Networks
Matthew Ragoza, Lillian Turner, David Ryan Koes
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 optimiz…
Protein-Ligand Scoring with Convolutional Neural Networks
Matthew Ragoza, Joshua Hochuli, Elisa Idrobo +2
Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug desig…