For a week we’ve modeled molecules one at a time — designed, evolved, force-fielded, screened. Today the lens widens: biology is a web of relationships, and machine learning on that web predicts the connections we haven’t drawn. Himmelstein et al. fused biomedical knowledge into Hetionet — ‘47,031 nodes of 11 types and 2,250,197 relationships of 24 types’ — and scored ‘209,168 compound-disease pairs’ for repurposing, ’entirely open.’ Zitnik et al.’s Decagon brought graph neural networks: ‘a new graph convolutional neural network for multirelational link prediction,’ predicting ’the exact side effect’ of a drug pair and ‘outperforming baselines by up to 69%.’ When COVID hit, a network-medicine consensus ranked 6,340 drugs and screened the top ones at a ‘62% success rate, in contrast to the 0.8% hit rate of nonguided screenings’ — and ‘76 of the 77’ hits act through mechanisms ’that cannot be identified using docking-based strategies.’ PrimeKG released an open precision-medicine graph of ‘17,080 diseases with 4,050,249 relationships,’ and TxGNN turned it into ‘a graph foundation model for zero-shot drug repurposing,’ finding candidates ’even for diseases with … no existing drugs,’ with ‘multi-hop’ explanations. As Li, Huang & Zitnik put it, ‘graphs are universal descriptors of systems of interacting elements.’ Reasoning over the web of biology.
