Think of the body as a city with many roads and intersections. Old-style drugs tried to fix a single broken traffic light and hoped the whole traffic jam would disappear. Network-based drug discovery uses computers to map the entire traffic system and find combinations of lights, roads, and junctions to adjust together, so the whole city flows better, not just one corner.
Traditional single-target drug discovery often fails in complex diseases (cancer, neurodegeneration, metabolic and immune disorders) where many genes, proteins, and pathways interact. This network-based, computational approach aims to improve hit discovery, reduce late-stage failures, and design more effective and safer multi-target or ‘network’ drugs by understanding disease as an interconnected system rather than a single target problem.
Access to high-quality proprietary omics data and curated biological networks, plus disease-specific know‑how and experimental feedback loops, can create a defensible advantage in building and validating network-based drug discovery platforms.
Classical-ML (Scikit/XGBoost)
Knowledge Graph
High (Custom Models/Infra)
Integration and cleaning of heterogeneous biological datasets (omics, pathways, clinical) and the computational cost of large-scale network/graph analyses and simulations.
Early Majority
Focus on network-level mechanisms of action and polypharmacology, shifting from ‘one drug–one target’ to multi-target, systems-oriented strategies in drug discovery and repositioning.