Poster: Machine learning applications for drug discover

To aid drug discovery in neurodegeneration, we created four unique computational methodologies, leveraging the capabilities of scikit-learn and PyTorch. Despite being developed for neurodegeneration, these methodologies hold potential for application in other medical fields. The ML predictions of the first variant were based on carbon-13 isotope and proton nuclear magnetic resonance (13CNMR ,1HNMR) spectroscopic data originating from the Simplified Molecular Input Line Entry System (SMILES) notations of small biomolecules. The conversion into spectroscopic data was carried on by the NMRDB software. We utilized case studies to illustrate the predictive modelling of the DNA Damage-Inducible Transcript 3 (CHOP); Transthyretin transcription activators; human dopamine D1 receptor antagonists. The second approach was based on atomic features of small biomolecules provided by PubChem, the world’s largest collection of freely accessible chemical information, or calculated additionally by us. The case studies were on predicting the active G9a inhibitors and their efficacy magnitude. Despite appearing to contradict established machine learning principles, the third variant predicted small