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BioNeMo is an AI-powered framework developed by NVIDIA for protein and molecular generation using deep learning models. It provides researchers and developers with tools to design, analyze, and optimize biological molecules, aiding in drug discovery and synthetic biology applications.

PySCeS CBMPy is a new platform for constraint based modelling and analysis. It has been designed using principles developed in the PySCeS simulation software project: usability, flexibility and accessibility. CBMPy supports the latest standards for encoding CBM models encoding, SBML L3 FBC, COBRA as well as MIRIAM compliant RDF and custom annotations. Its architecture is both extensible and flexible using data structures that are intuitive to the biologist while transparently...

!!! PTEROS DEVELOPMENT MOVED TO GITHUB !!! New project page: https://github.com/yesint/pteros New documentation page: https://yesint.github.io/pteros/ Pteros is the C++ library for custom molecular modeling and simulations codes designed for researchers, not for C++ gurus. Provides facilities for PDB, XTC and TRR files IO, powerful selections, geometry transformations, RMSD fitting and alignment, etc.

miRStat enables identification of regulatory microRNA targeting several genes in a custom gene group. This Python application is based on the TargetScan 6.2 microRNA target prediction data. Conserved and Nonconserved site context+ scores files are required (unzip and place to directory with program). Available at http://targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61

metaProt is a python pipeline to analyze and extract data from protein sequences found in metagenomic projects. It integrates several existing tools (HMMer, Pepstats, Blast...) to be used against custom databases. Please, read the README.txt file to find more about this.

A lightweight, browsing-based, 100% Python, federated data integration framework. Users may create custom schemas for disparate sources, query and expand results across sources to find related data; for use in fields such as bioinformatics and datamining

Galileo is a library for developing custom distributed genetic algorithms developed in Python. It provides a robust set of objects that can be used directly or as the basis of derived objects. Its modularity makes it easy to extend the functionality. The