Zobrazeno 1 - 10
of 244
pro vyhledávání: '"Koes, David"'
Autor:
Dunn, Ian, Koes, David Ryan
Deep generative models that produce novel molecular structures have the potential to facilitate chemical discovery. Diffusion models currently achieve state of the art performance for 3D molecule generation. In this work, we explore the use of flow m
Externí odkaz:
http://arxiv.org/abs/2404.19739
Autor:
Dunn, Ian, Koes, David Ryan
Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of graph neural n
Externí odkaz:
http://arxiv.org/abs/2311.13466
Molecular docking aims to predict the 3D pose of a small molecule in a protein binding site. Traditional docking methods predict ligand poses by minimizing a physics-inspired scoring function. Recently, a diffusion model has been proposed that iterat
Externí odkaz:
http://arxiv.org/abs/2307.12090
Publikováno v:
In Biophysical Journal 3 September 2024 123(17):2730-2739
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 cheminformatic properties, but has not yet been applied to generating
Externí odkaz:
http://arxiv.org/abs/2110.15200
Autor:
Arcidiacono, Michael, Koes, David Ryan
Recent advances in machine learning have enabled generative models for both optimization and de novo generation of drug candidates with desired properties. Previous generative models have focused on producing SMILES strings or 2D molecular graphs, wh
Externí odkaz:
http://arxiv.org/abs/2109.15308
Autor:
Ricci, Morgan M.C., Orenberg, Andrew, Ohayon, Lee, Gau, David, Wills, Rachel C., Bae, Yongho, Das, Tuhin, Koes, David, Hammond, Gerald R.V., Roy, Partha
Publikováno v:
In Journal of Biological Chemistry January 2024 300(1)
Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent sp
Externí odkaz:
http://arxiv.org/abs/2010.08687
Autor:
King, Jonathan E., Koes, David Ryan
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. We present
Externí odkaz:
http://arxiv.org/abs/2010.08162
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 the first time a deep generative model that can generate 3D molecular
Externí odkaz:
http://arxiv.org/abs/2010.14442