Computational Drug Discovery
M.Sc. Pharmaceutical Science
Institute of Pharmaceutical and Medicinal Chemistry
The goal of my research is the development of a target-specific scoring function. For this purpose, I use PADIF (Protein per Atom Score Contributions Derived Interaction Fingerprint) to train different classification models and test these as an activity predictor for each target. Activity prediction models usually focus on ligands, particularly their structures, hindering the exploration of new chemical spaces. PADIF can avoid this bias because it describes the interactions between proteins and ligands after a docking process, taking advantage of structural and ligand features for drug discovery. At the end of this research project, an automatic workflow will be created, making the application of this methodology easy to use.
Victoria-Muñoz, F., Sánchez-Cruz, N., Medina-Franco, J. L, & Lopez-Vallejo, F. Cheminformatics analysis of molecular datasets of transcription factors associated with quorum sensing in Pseudomonas aeruginosa. RSC Adv., 2022, 12, 6783-6790. https://doi.org/10.1039/D1RA08352J