CharlyEmpereurmot

PeptideBuilder

A simple Python library to generate model peptides

dssp

The DSSP building software

cgbind

metallocage construction and binding affinity calculations

mealpy

A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)

DIG

A library for graph deep learning research

LaMCTS

The release codes of LA-MCTS with its application to Neural Architecture Search.

SwarmCGM

Optimization tool for calibrating coarse-grained force fields of lipids, relying on the simultaneous usage of reference AA trajectories (bottom-up) and experimental data (top-down)

masif_seed

Masif seed paper repository

lipyphilic

A Python toolkit for the analyis of lipid membrane simulations

icosphere

Constructs geodesic icosahedron given subdivision frequency.

Ax

Adaptive Experimentation Platform

alebo

Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization

optimesh

:spider_web: Mesh optimization, mesh smoothing.

PyREMBO

Python implementation of REMBO built on GPyTorch.

rembo

Bayesian optimization in high-dimensions via random embedding.

morbo

Multi-Objective Bayesian Optimization over High-Dimensional Search Spaces

Deeprank-GNN

Graph Network for protein-protein interface

mplfinance

Financial Markets Data Visualization using Matplotlib

TuRBO

numpyro

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

saasbo

SAASBO: a package for high-dimensional bayesian optimization

bayesian-algorithm-execution

Bayesian algorithm execution (BAX)

presearch-packages

Instant information packages for the Presearch engine

node

Mysterium Network Node - official implementation of distributed VPN network (dVPN) protocol

hugo

The world’s fastest framework for building websites.

GREASY

Greasy is a tool designed to make easier the deployment of embarrassingly parallel simulations in any environment.

GREASY

Greasy is an HTC approach to HPC environments (HT&PC). Versatile and easy-to-use parallel framework/runtime aimed at many task computing, ideally in HPC environments. Greasy is a tool designed to make easier the deployment of embarrassingly parallel simulations in any environment. It is able to run in parallel a list of different tasks, schedule them and run them using the available resources. It is the perfect tool to use, for example, when your application is a serial program, and you need to run a large number of instances with different parameters. Greasy packs all these separate runs and uses the resources granted to run as many tasks as possible in parallel. As this tasks finish, Greasy will continue starting the tasks that were waiting for resources. Since one of the main principles of Greasy is to keep it simple for the user, the list of tasks is just that: a list of tasks in a text file. Then, each line in the file becomes a task to be run by Greasy. It is able to manage dependencies between tasks, or to rerun a task in case of failure if desired. Greasy can be easily configured by default with a configuration file, and can be also customized for each particular execution using environment variables. It also provides a log system where all greasy actions will be recorded to keep track of what is the progress of your run.

gromacs-fda

Force Distribution Analysis (FDA) for GROMACS

pytorch-GAT

My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!

awesome-gcn

resources for graph convolutional networks （图卷积神经网络相关资源）