Integrative Computational Chemistry, Machine Learning, and AI: Applications in Drug Discovery, Repurposing, Regulatory Affairs, Food Safety, and Toxicological Sciences

This PhD project combines computational chemistry, machine learning, and AI to tackle challenges in pharmaceutical, biological, food, chemical, and regulatory sciences. Students can specialize in: 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. Research encompasses 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 for creative exploration aligned with their career goals.

Methodology
Approaches include: target identification/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 validated through in silico/in vitro testing.

Software and Tools
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, Hugging Face Transformers.

https://www.tudublin.ie/explore/faculties-and-schools/sciences-health/school-chemical-biopharmaceutical-sciences/

2.1 Honors BSc or Master's degree in a relevant subject

Self Funded (Scholarship not available. Fees & Materials to be paid by the student. Materials costs not significant)

If you are interested in submitting an application for this project, please complete an Expression of Interest.

 https://forms.office.com/e/0hCcrv2Gkp