Recurrent Geometric Networks for end-to-end differentiable learning of protein structure
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Updated
Aug 1, 2019 - Python
Recurrent Geometric Networks for end-to-end differentiable learning of protein structure
Recurrent Geometric Networks for end-to-end differentiable learning of protein structure
This project aims to solve a simplified version of the Protein Structure Prediction problem, represented as a combinatorial optimization task, using Reinforcement Learning.
Source code for the manuscript: FingerprintContacts: Predicting Alternative Conformations of Proteins from Coevolution
Distance-based protein model quality estimation using deep ResNets
Algoritmo para o cálculo de RMSD para proteínas otimizadas com o modelo 3D AB Off-Lattice.
An implementation of the DeepMind's AlphaFold based on PyTorch for research
Hybridized distance- and contact-based hierarchical protein folding
A machine learning model that builds amino acids into a protein model.
Protein Structure prediction using Hybrid Differential Evolution (HybridDE)
ResNetQA: Improved protein model quality assessment by integrating sequential and pairwise features using deep learning
Alpha-Protein: Protein Contact-map Prediction Boosted by Attention
Protein Secondary Structure Prediction project with RNNs and Transformers
Transformer model for protein structure prediction
Protein contact map quality estimation by evolutionary reconciliation
Code for paper "Adversarial Attacks on Protein Language Models", Ginevra Carbone, Francesca Cuturello, Luca Bortolussi, Alberto Cazzaniga (2022).
CB513 datasets for Protein Secondary Structure Prediction
Source code and dataset for 2022 Publication "Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction"
Repository with scripts and data generated during my internship at Institut Pasteur of Paris
[ICLR 2022] OntoProtein: Protein Pretraining With Gene Ontology Embedding
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