Embracing assay heterogeneity with neural processes for markedly
  improved bioactivity predictions
Lucian Chan Marcel Verdonk Carl Poelking
Predicting the bioactivity of a ligand is one of the hardest and most
important challenges in computer-aided drug discovery. Despite years of data
collection and curation efforts by research organizations worldwide,
bioactivity data remains sparse and heterogeneous, thus hampering efforts to
build predictive models that are accurate, transferable and robust. The
intrinsic variability of the experimental data is further compounded by data
aggregation practices that neglect heterogeneity to overcome sparsity. Here we
discuss the limitations of these practices and present a hierarchical
meta-learning framework that exploits the information synergy across disparate
assays by successfully accounting for assay heterogeneity. We show that the
model achieves a drastic improvement in affinity prediction across diverse
protein targets and assay types compared to conventional baselines. It can
quickly adapt to new target contexts using very few observations, thus enabling
large-scale virtual screening in early-phase drug discovery.