Antonio Peon

Computational chemist with 20 years driving small-molecule drug discovery through molecular modelling and machine learning. Expert in structure- and ligand-based design, protein–ligand interactions, free-energy calculations and ML-enhanced predictive workflows, with hands-on delivery from hit identification to experimental validation. Proven in multidisciplinary, cross-functional teams and in building pipelines for de novo molecular design.

Proficient in Python and leading molecular-modelling platforms. Seeking a senior computational chemistry role in drug discovery.

Senior Computational Chemist II at Tozaro (2022-01 – 2026-04) • Lead computational chemistry on drug discovery projects, driving structure- and ligand-based molecular design and the prioritisation of compounds against project milestones.

• Built an AI-driven generative pipeline for de novo molecular design (RDKit, DeepChem), producing chemically valid molecules optimised for drug-likeness and synthesisability.
• Run molecular screening and docking with MOE and AutoDock to generate mechanistic insight into protein–ligand interactions and guide design.
• Perform molecular dynamics and free-energy / binding-energy estimation with GROMACS, AmberTools and Parmed (MM-PBSA).
• Develop and implement machine-learning models to predict compound properties; build reproducible analysis workflows in Python, RDKit and KNIME.
• Lead project planning, resource allocation and workflow optimisation, delivering key milestones on time and within budget.

Computational

Chemist at University of Porto (2017-01 – 2021-12) • Ran molecular dynamics simulations of bacterial lipid membranes and (cyclo)peptides using all-atom, united-atom and coarse-grained force fields to investigate membrane interactions.

• Performed force-field parameterisation and optimisation to ensure accurate representation of the membrane systems.
• Developed Python / MDAnalysis tools for large-scale trajectory analysis.

Chemoinformatics

Chemist at Cancer Research Center of Marseille (CRCM) (2015-01 – 2017-12) • Applied machine-learning algorithms for data-driven target prediction (target fishing) to support drug-discovery decisions.

• Developed a Python / RDKit web server integrated with a SQL database for efficient compound and prediction data management.
• Analysed drug–gene associations (MANOVA) and collaborated with interdisciplinary teams to translate findings into research direction. Computational & Organic Chemist at University of Santiago de Compostela (2006-01 – 2014-12) • Designed and synthesised inhibitors targeting Mycobacterium tuberculosis and Helicobacter pylori enzymes.
• Used docking and fragment docking to explore binding modes and affinity against target proteins, supporting structure-based design.
• Conducted molecular dynamics simulations (AMBER) to investigate inhibitor–protein dynamics and interactions.

PhD in Organic Chemistry – University of Santiago de Compostela (2013-01)

Degree in Chemistry – University of Santiago de Compostela (2006-01)