  The depth of knowledge offered by post-genomic medicine has carried the
promise of new drugs, and cures for multiple diseases. To explore the degree to
which this capability has materialized, we extract meta-data from 356,403
clinical trials spanning four decades, aiming to offer mechanistic insights
into the innovation practices in drug discovery. We find that convention
dominates over innovation, as over 96% of the recorded trials focus on
previously tested drug targets, and the tested drugs target only 12% of the
human interactome. If current patterns persist, it would take 170 years to
target all druggable proteins. We uncover two network-based fundamental
mechanisms that currently limit target discovery: preferential attachment,
leading to the repeated exploration of previously targeted proteins; and local
network effects, limiting exploration to proteins interacting with highly
explored proteins. We build on these insights to develop a quantitative
network-based model of drug discovery. We demonstrate that the model is able to
accurately recreate the exploration patterns observed in clinical trials. Most
importantly, we show that a network-based search strategy can widen the scope
of drug discovery by guiding exploration to novel proteins that are part of
under explored regions in the human interactome.

Quantitative Methods;Social and Information Networks