Exploring druglike chemical spaces with artificial intelligence (DrugSpaceAI)
The exponential growth of the synthesizable chemical space represents a bottleneck in modern drug discovery, even among in silico methods. The rapid development of artificial intelligence has brought a drastic reduction in drug development times, but the most impactful advances, often outsourced to leading tech companies, typically remain behind the walls of big pharma. The goal of this project is to develop an open, AI-driven computational drug discovery ecosystem that (i) enables fast, preferably realtime access to large chemical spaces, (ii) democratizes rational drug design for researchers without programming expertise, and (iii) keeps pace with the explosive growth of the chemical universe. We have previously shown that protein-ligand binding can be characterized by a finite number of unique interaction patterns (pharmacophores). At this level, we hypothesize that virtual screening results are highly reusable and can be queried in real time after a single calculation. Building on this, the project will expand our existing pharmacophore database with support for non-enumerated chemical spaces and dynamic pharmacophore representations that capture protein conformational flexibility. We will develop AI-driven interfaces to this database, including fine-tuned chemical language models, natural language querying via Retrieval-Augmented Generation (RAG), and multi-agent systems to decompose and execute arbitrarily complex tasks. These components will be integrated into cascaded discovery workflows with AI-assisted docking workflows and state-of-the-art co-folding models such as AlphaFold3, and ultimately validated with biochemical experiments targeting oncology, neuropharmacology, and protein-protein interactions.
Head: Dávid Bajusz (bajusz.david@ttk.hu)
Grants: HAS Lendület research grant LP2026-1/2026