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Computational and AI-Guided Drug Discovery
Using molecular modelling, docking, molecular dynamics, quantum chemistry and machine learning to explore therapeutic targets, predict molecular properties and support drug discovery workflows.
DockingMolecular dynamicsMachine learningQSAR
Overview
A case study in applying computational and data-driven methods to prioritise compounds, predict molecular properties and guide experimental decisions in early-stage discovery.
Scientific question
How can computational and machine-learning methods accelerate the identification and prioritisation of promising therapeutic molecules?
Why it matters
Computational methods can accelerate early-stage discovery, prioritise compounds and guide experimental decisions before committing laboratory resources.
Approach
Molecular docking, molecular dynamics, QSAR, quantum descriptors, machine learning, protein modelling and data-driven interpretation.
Relevance to biotech/pharma
Computational and AI-guided approaches are core to modern pharma R&D, reducing time and cost in hit identification, lead optimisation and property prediction.
Selected outputs / publications
Selected publications cover LRRK2 inhibitors, DPP-4 inhibitors, tyrosinase inhibitors, SARS-CoV-2 drug repurposing, QSAR studies and ElectroPredictor.