A set of libraries for aligning, profiling, and predicting the structure of proteins
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Updated
Nov 1, 2017 - Python
A set of libraries for aligning, profiling, and predicting the structure of proteins
A deep learning method that identifies improved protein conformational states in trajectory data from refinement simulations
spdkit: Structure Predication Development Kit
Recurrent neural network implementations for protein secondary structure prediction and language models
Active Imitation Learing with Noisy Guidance
Computing descriptors for AIRSS style SHELX files.
An implementation of a genetic algorithm in Python for predicting equilibrium crystal structures for a given potential. The potential implemented here is the Daoud-Cotton model, but this can be easily changed.
An implementation of the DeepMind's AlphaFold based on PyTorch for research
Data and code for the manuscript "Assessing the utility of CASP14 models for Molecular Replacement: https://doi.org/10.22541/au.162412334.48194370/v1
Deep neural models for core NLP tasks (Pytorch version)
Official code repository for EquiFold: Protein Structure Prediction with a Novel Coarse-Grained Structure Representation
Python bindings for the `buildcell` program for Ab Initio Random Structure Searching (AIRSS)
spdkit: Structure Predication Development Kit
nucleic acid folding
[ACL'23 main] DICE: Data-Efficient Clinical Event Extraction with Generative Models
[EMNLP 2022] Generative Knowledge Graph Construction: A Review
Protein Secondary Structure predictor using Convolutional Neural Networks
Searching proteins against a database of embeddings
[ACL 2023] SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Prospr is a universal toolbox for protein structure prediction within the HP-model. The Python package is based on a C++ core, which gives Prospr its high performance. The C++ core is made available as a separate zip file to facilitate high-performance computing applications. The package comes with many prediction algorithms and datasets to use.
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