1 code implementation • 22 Nov 2023 • Ian Dunn, David Ryan Koes
Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design.
1 code implementation • 22 Jul 2023 • Michael Brocidiacono, Konstantin I. Popov, David Ryan Koes, Alexander Tropsha
Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function.
2 code implementations • 28 Oct 2021 • 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.
1 code implementation • 30 Sep 2021 • Michael Arcidiacono, David Ryan Koes
Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties.
1 code implementation • 17 Oct 2020 • Matthew Ragoza, Tomohide Masuda, David Ryan Koes
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models.
1 code implementation • 16 Oct 2020 • Tomohide Masuda, Matthew Ragoza, David Ryan Koes
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.
3 code implementations • 16 Oct 2020 • Jonathan E. King, David Ryan Koes
Despite recent advancements in deep learning methods for protein structure prediction and representation, little focus has been directed at the simultaneous inclusion and prediction of protein backbone and sidechain structure information.
1 code implementation • 10 Dec 2019 • Jocelyn Sunseri, David Ryan Koes
There are many ways to represent a molecule as input to a machine learning model and each is associated with loss and retention of certain kinds of information.
1 code implementation • 6 Mar 2018 • Joshua Hochuli, Alec Helbling, Tamar Skaist, Matthew Ragoza, David Ryan Koes
Here we present three methods for visualizing how individual protein-ligand complexes are interpreted by 3D convolutional neural networks.
1 code implementation • 20 Oct 2017 • 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 optimization.
2 code implementations • 8 Dec 2016 • Matthew Ragoza, Joshua Hochuli, Elisa Idrobo, Jocelyn Sunseri, David Ryan Koes
A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding.