Summary
Daniel García is a lead engineer blending a pharmacist’s domain knowledge, a PhD in molecular modeling, and 12 years of hands‑on experience building production-grade AI and software for drug discovery. At AstraZeneca he designs and deploys AI-augmented DMTA platforms that connect scientific data, models, and workflows to accelerate decision-making at scale. He previously led computational chemistry and platform innovation—developing structure-based tools, free energy methods, and SEE‑Tx integrations—that directly shortened hit-to-lead cycles. Comfortable in both code and the lab, he translates medicinal chemistry needs into scalable Python, ML, and MLOps solutions while routinely bridging computational outputs with experimental teams. His academic work produced MDMix, an MD‑based method for detecting cryptic pockets now used to inform binding-site druggability and virtual screening. Based in Barcelona, he combines scientific rigor with engineering discipline to turn complex discovery problems into reliable, deployable systems.
12 years of coding experience
7 years of employment as a software developer
MSc Bioinformatics for Health Sciences Computational chemistry computational biology bioinformatics drug design, MSc Bioinformatics for Health Sciences Computational chemistry computational biology bioinformatics drug design at Universitat Pompeu Fabra
Doctor of Philosophy (Ph.D.) Molecular modeling, Doctor of Philosophy (Ph.D.) Molecular modeling at Universitat de Barcelona
Spanish, Catalan, English, French