Computational Chemist & Research Software Engineer — Machine Learning & Representation Learning for Chemistry
Eduardo Alberto Aguilar-Bejarano
Nottingham, UK (visiting MIT, Cambridge, MA — from June 2026) | eduardo.aguilar-bejarano@nottingham.ac.uk
Download Full CV (PDF)Chemistry-trained machine-learning researcher and research software engineer whose work centres on the chemical representation problem — how molecules, materials, and reactions should be encoded for learning, and when geometric deep learning (graph- and geometry-based representations) outperforms classical tabular descriptors. My PhD is a three-part study of this question across transition-metal complexes (asymmetric catalysis), transition-metal carbides (energetics), and polymers (biomaterials), pairing each system with interpretability methods that attribute predictions to chemically meaningful substructures. First author of five articles (Cell Press, RSC) and lead developer of several open-source scientific Python libraries, with full-cycle engineering experience — architecture, CI/CD, automated testing, deployment, documentation, and HPC — consistently in close collaboration with experimental and industrial partners.
Based at the Koch Institute for Integrative Cancer Research (Cambridge, MA). Developing interpretable computational models for the design and prioritisation of lipid nanoparticles (LNPs) for more efficient drug delivery, extending prior LNP composition-analysis work toward model-guided exploration of lipid chemical space. Continuation of the Nottingham–MIT LNP collaboration with Prof. Daniel Anderson's lab.
On the EPSRC Large Grant: Designing bio-instructive materials for translation-ready medical devices (Advisor: Dr. Grazziela Figueredo). First author and architect of Helix 1.0 (CI/CD-managed framework for reproducible, interpretable ML on tabular scientific data; applied to biomarker discovery of macrophage cell types from high-dimensional metabolomics data). Lead developer of PolyNet (end-to-end Python/Streamlit library for structure-based polymer property prediction) and of LCNet / LNPQuantification (the neural-network model behind the first quantitative Q-Cryo-OrbiSIMS method for LNP characterisation, with MIT's Anderson Lab — curated the 337-spot calibration dataset; predicts the four canonical LNP lipid components to ~14 mol% MAE). Lead modeller on an antibiofilm polymer-discovery programme (ensemble GNNs over ~500 polymer structures against high-throughput biofilm assays for P. aeruginosa, S. aureus, and uropathogenic E. coli). Also delivered in-house data-science and software training and promoted open-source, reproducible research across an interdisciplinary EPSRC team.
Doctoral thesis studying chemical representation across three systems of increasing difficulty: (1) transition-metal complexes — interpretable GNN pipeline for enantioselective catalyst/ligand optimisation (→ HCat-GNet, iScience; AsymBench, J. Cheminform.; data curation, Molecules); (2) transition-metal carbides — new 3D-masking explainability for crystal-convolution GNNs and machine-learnt interatomic potentials, applied to molybdenum carbides (→ PCCP); (3) polymers — ensemble-GNN methodology for virtual screening and multi-property optimisation of polymer biomaterials (→ PolyNet).
Cheminformatic modelling for in-silico prediction of physicochemical profiles and rational design of bioactive compounds (Advisor: Prof. William Zamora-Ramírez). Built a novel lipophilicity descriptor from toluene/water partition-coefficient prediction (MLR, SVM, random forest, gradient boosting); developed the first multivariable model for tautomer equilibrium constants in water, extended toward organic solvents; and characterised antimicrobial-peptide structures using QM continuum-solvation calculations.
First author unless noted. Full list with graphical abstracts on the Publications page.
Lead/first-author role unless noted. Most publications ship with an accompanying public code repository.
Co-supervised three MSc students (2024–2026) on deep-learning projects in the chemical sciences with Dr. Grazziela Figueredo and Dr. Kristaps Ermanis (2023-2024) and Prof. Simon Woodward (2025).
Designed and delivered in-house Python and ML training to interdisciplinary research teams.
Teaching assistant for Prof. William Zamora's Cheminformatics course (programming, data visualisation, ML algorithms, cheminformatic tools).
Assisted across seven laboratory courses: Physical Chemistry I & II, Analytical Chemistry I, Organic Chemistry I, Chemical Experimentation II, and General & Qualitative Chemistry.