Pharmacist · ML engineer for drug discovery & pharmacogenomics
I build deep-learning models that predict how genes and drugs interact — bridging pharmacology and computational science to make therapy more personalized.
PharmD (Universidad de Granada) · MSc Bioinformatics (VIU) · 📍 Spain
Pharmagen — a pharmacogenomic prediction engine Predicting the phenotypic effect of a drug (toxicity, efficacy, pharmacokinetic alterations) from genetic variants.
- Model: hybrid neural network — Factorization Machines + Transformers with attention — trained on ClinPGx & dbSNP (86.9% accuracy).
- Pipeline: NGS variant extraction and standardization in Python (ClinPGx, dbSNP, PubChem, Ensembl VEP).
- Engineering: hyperparameter search with Optuna, class-imbalance handling via Adaptive Focal Loss.
- v2 — graph architecture (in progress): a graph-based pharmacogenomics library — molecular graphs (RDKit → PyTorch Geometric) and positional genomic graphs for gene variants, feeding a Twin-Tower GATv2 architecture.
- v2 — production refactor (in progress): rearchitecting the codebase to industry standards — typed data models (Pydantic), a FastAPI service layer, and reproducible ML workflows — hardening a research prototype into maintainable, deployable software.
Durin — clinical software for polypharmacy safety (early development) Tooling to support the care of polymedicated geriatric patients — ingesting a medication list and clinical profile to surface the drug–drug interactions, dosing concerns and cumulative side-effect burden worth a clinician's attention.
- Foundation built: typed domain models (Pydantic v2) for drugs & patients, an ATC/DDD catalog via a custom scraper, and i18n locale catalogs (ES/EN).
- Service layer: FastAPI scaffolding in place.
- On the roadmap: the interaction/risk engine and an AI layer to prioritize and explain risks.
- Stack:
Python 3.14·Pydantic v2·FastAPI·BeautifulSoup·Polars
Also exploring: computational rheology of topical pharmaceutical forms (non-Newtonian fluid simulation phi-flow) and a Quarto research portfolio.
Pharmacogenomics · AI-driven drug design · Graph Neural Networks
Deep Learning for molecules · Precision medicine · NGS pipelines
ML / DL: PyTorch · PyTorch Geometric · Optuna · scikit-learn
Cheminformatics / Bio: RDKit · NGS/VCF · Ensembl VEP · dbSNP · PubChem
Data: pandas · Polars · NumPy
Tooling: Python · C++ · Git · Linux (Debian) · Quarto
- TFM — AI & Personalized Medicine: pharmacogenomic prediction pipelines and deep-learning models (VIU, 2026).
- TFG — Pharmacogenomics of hypertension (Universidad de Granada, 2022).
PharmD who taught himself to code — I like turning biological questions into models. Open to research collaborations and PhD/CDT opportunities in AI for drug discovery.