Challenge — Drug Discovery Bottlenecks & Manual Lab-Driven Screening
Discovering potential drugs is currently a largely manual, lab-centric, and extremely time-consuming process. The search space of possible molecules and proteins is immense, which makes discovery work labor-intensive and slows down progress across R&D and pre-clinical stages.
Solution — Graph-Based Molecular Generation with Graph Neural Networks
We model molecules as graphs and use Graph Neural Networks (GNNs) in a generative setup to learn molecular structure and propose new candidate molecules. This enables systematic exploration of the chemical space and supports faster iteration in early discovery.
Impact — Faster R&D, Better Molecules & Higher Discovery Success
- Improved molecule performance (e.g., blood–brain barrier, oral availability) supporting high-performance products
- 60% reduction in time required for the R&D and pre-clinical phases
- Higher clinical trial success rate and 25% savings in the discovery process
Topic
Digital Transformation