Sixteen Compounds, One Forward Pass
TL;DR for operators Molecular Prior Networks (MPNs), introduced by Pawel Gomoluch and Anthony Bradley, offer drug-discovery teams a way to rank candidate molecules using a handful of experimental measurements without fitting a new model for every protein target.1 On the FS-Mol benchmark, a four-layer MPN achieves Kendall’s tau of 0.299 with 16 labelled molecules, nearly identical to the 0.298 achieved by ADKF-IFT, a leading few-shot molecular prediction method. But this numerical match does not persist as more measurements become available: with 128 labelled molecules, the sixteen-layer MPN reaches 0.398 while ADKF-IFT reaches 0.434. ...