Numpy and scipy are python libraries excellent at handling mathematical and scientific calculation. The two libraries are widely used in the project to construct the basic mathematical logic of the algorithm.

Pytorch was chosen as our main deep learning library for its enormous power and efficiency. Numerous deep learning models were created with Pytorch to explore options for Peplib Generator in the early stage.

The Biopython library dedicates to facilitating biological calculations, integrating numerous bioinformatic algorithms. Multiple indispensable values that supported the optimization process were processed through Biopython.

Pyswarms greatly accelerated the deployment of Particle Swarm Optimization in python. We applied to slightly modified version to perform as the basis of our optimization algorithm.

AlphaFold, developed by Deepmind, is the core of our optimization system. AlphaFold produces state-of-the-art prediction of protein-peptide complex structure, which provided us with (mostly) accurate affinity score to perform optimization of peptides. AlphaFold_Advanced is a python file included in ColabFold. It allowed swift implementation of AlphaFold in Google Collaboratory, which helped accelerated our optimization process.

The Masif library can generate the surface mesh and calculate surface properties of proteins. We used the library to analyze the interface region of the receptors.

This GitHub repository helps calculate parameters from pdb files, like geometric center, center of mass, gyration radius, RMSD, etc.

This is a platform for geometry processing with a group of functions to process and calculate mesh data. Combined with msms, APBS, pdb2pqr, you can convert a pdb file into a gemetry file.

This is a C++ template library for calculate linear algebra: matrices, vectors, numerical solvers, and related algorithms.