The purpose of this work is the modeling of the wine preferences by physicochemical properties. Such model is useful to support the oenologist wine tasting evaluations, improve and speed-up the wine production. A Neural Network was trained using Tensorflow, which was later tuned in order to achieve high-accuracy quality predictions.

MHC binding prediction based on modeled physicochemical properties of peptides

ProtParam based program that outputs the id, pI, charge and molecular weight of each protein

Physicochemical Property data extraction from small molecule

Contains relevant project files to publicly available tautomer database "Tautobase" for DataWarrior

Prediction of the RMSD (Root Mean Square Deviation) of a decoy set using Physicochemical Properties of Protein Tertiary Structure Data Set using Machine Learning