Generative AI & Computational Drug Discovery
Designing and prioritising candidate molecules in silico, compressing the early discovery cycle.
What this centre does
Applies generative artificial intelligence to the discovery and repurposing of cancer therapeutics — designing and prioritising candidate molecules in silico, predicting their properties and interactions, and extending to drug repurposing and combination design.
Methods
Generative molecular design; property and ADMET prediction; molecular docking and free-energy estimation; combination-synergy modelling; repurposing screens against knowledge graphs.
Data inputs
Public chemical and bioactivity databases; structural data; the institution's own knowledge graph; target hypotheses from the other centres.
Outputs
Prioritised candidate and repurposing hypotheses; methods publications; IP where a method or candidate is defensible.
Funding route
AI-in-drug-discovery grants; biotech collaborations — the most fundable theme in the current Indian funding climate.
What has to be true first
Knowledge-graph and compute capability from Centre 06.
Themes this centre carries
Work with this centre
The research centres collaborate with funders, sponsors, hospitals and academic groups. Tell us what you are working on and the enquiry is routed to the centre lead.
Contact the research office