Search Results for author: Marcin J. Skwark

Found 4 papers, 0 papers with code

Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning

no code implementations3 Dec 2020 Marcin J. Skwark, Nicolás López Carranza, Thomas Pierrot, Joe Phillips, Slim Said, Alexandre Laterre, Amine Kerkeni, Uğur Şahin, Karim Beguir

This suggests that combining leading protein design methods with modern deep reinforcement learning is a viable path for discovering a Covid-19 cure and may accelerate design of peptide-based therapeutics for other diseases.

reinforcement-learning

3D Deep Learning for Biological Function Prediction from Physical Fields

no code implementations13 Apr 2017 Vladimir Golkov, Marcin J. Skwark, Atanas Mirchev, Georgi Dikov, Alexander R. Geanes, Jeffrey Mendenhall, Jens Meiler, Daniel Cremers

In this paper, we show that deep learning can predict biological function of molecules directly from their raw 3D approximated electron density and electrostatic potential fields.

Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images

no code implementations NeurIPS 2016 Vladimir Golkov, Marcin J. Skwark, Antonij Golkov, Alexey Dosovitskiy, Thomas Brox, Jens Meiler, Daniel Cremers

A contact map is a compact representation of the three-dimensional structure of a protein via the pairwise contacts between the amino acid constituting the protein.

Improving contact prediction along three dimensions

no code implementations3 Mar 2014 Christoph Feinauer, Marcin J. Skwark, Andrea Pagnani, Erik Aurell

Correlation patterns in multiple sequence alignments of homologous proteins can be exploited to infer information on the three-dimensional structure of their members.

Cannot find the paper you are looking for? You can Submit a new open access paper.