PhD Project: Integrative Computational Chemistry, Machine Learning, and AI for Drug Discovery, Repurposing, Regulatory Affairs, Food Safety, and Toxicological Sciences Technological University Dublin in Ireland

This PhD project at Technological University Dublin offers a unique opportunity to work at the intersection of computational chemistry, machine learning, and artificial intelligence. The research is designed to address complex challenges in pharmaceutical, biological, food, chemical, and regulatory sciences. Students may specialize in areas such as drug discovery and development, drug repurposing, ADME optimization, toxicological assessment, regulatory compliance modeling, food safety evaluation, environmental risk assessment, cosmetic safety profiling, natural product analysis, analytical chemistry modeling, quantum chemical calculations, pharmaceutical process optimization, or AI-driven predictive modeling.

The project encompasses a wide range of research topics including drug-target interactions, biological activity prediction, chemical process optimization, complex dataset analysis, and regulatory tool development. Students are encouraged to propose innovative research ideas within this multidisciplinary framework, allowing for creative exploration tailored to individual career goals.

Methodological approaches include target identification and validation, chemical library curation, data preprocessing, pharmacophore modeling, 2D/3D molecular screening, molecular docking, molecular dynamics simulations, free energy calculations, QSAR/QSPR modeling, quantum chemical calculations (DFT, ab initio, molecular orbital analysis), lead optimization, structure/ligand/fragment-based design, retrosynthetic analysis, toxicity/ADME/metabolite prediction, drug repurposing, analytical method development, chemometric analysis, multivariate analysis (PCA, PLS, clustering), pharmaceutical process optimization, Quality by Design (QbD), Design of Experiments (DoE), AI predictive modeling, deep learning, NLP for regulatory text mining, retrieval-augmented generation (RAG), and automated workflows. Computational predictions are validated through in silico and in vitro testing.

Students will have access to a comprehensive suite of software and tools including RDKit, Open Babel, ChEMBL, PubChem, ZINC, AutoDock Vina/4, Smina, GROMACS, OpenMM, NAMD, Amber, PyMOL, Avogadro, Chimera, VMD, scikit-learn, TensorFlow, PyTorch, Keras, DeepChem, Mordred, PaDEL-Descriptor, alvaDesc, KNIME, Orange, SwissADME, pkCSM, ProTox, admetSAR, ORCA, Psi4, Gaussian, GAMESS, NWChem, CP2K, MOPAC, Discovery Studio Visualizer, LigPlot+, PLIP, MDAnalysis, ChimeraX, Schrödinger Maestro, MOE, MATLAB, Python, R, Jupyter, LangChain, LlamaIndex, and Hugging Face Transformers.

Eligibility requires a 2.1 Honors BSc or Master’s degree in a relevant subject, with excellent English language proficiency. The position is self-funded, with no scholarship available; students must pay tuition fees and materials costs (materials costs are not significant). The application deadline is March 31, 2026. Interested candidates should complete an Expression of Interest form at this link and may contact [email protected] for further information.

This project is ideal for students seeking to develop advanced computational and analytical skills in a multidisciplinary research environment, with applications spanning drug development, regulatory science, and food safety.