AI Drug Discovery & Molecular Design

This research area leverages artificial intelligence to accelerate drug discovery and development processes, addressing the significant time and cost challenges in pharmaceutical research. The work encompasses developing novel neural architectures for molecular property prediction, drug-target interaction modeling, and molecular optimization.

Key contributions include the creation of comprehensive drug discovery platforms, reinforcement learning approaches for structure-based drug design, and foundation models for therapeutic science. The research tackles critical challenges in drug discovery including molecular representation learning, multi-objective optimization for drug properties, and the integration of diverse biological and chemical data sources.

The work has resulted in widely-adopted open-source tools and has influenced both academic research and industry applications in pharmaceutical development.