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.

模型利用异质性药效数据的信息协同，成功应对了试剂异质性，极大地提高了不同蛋白质靶点和试剂类型的亲和力预测，从而在早期药物发现中实现了大规模虚拟筛选。

利用神经过程应对试验异质性，显著提高生物活性预测

Embracing assay heterogeneity with neural processes for markedly  improved bioactivity predictions

We propose a hierarchical meta-learning-inspired model for music source
separation (Meta-TasNet) in which a generator model is used to predict the
weights of individual extractor models. This enables efficient
parameter-sharing, while still allowing for instrument-specific
parameterization. Meta-TasNet is shown to be more effective than the models
trained independently or in a multi-task setting, and achieve performance
comparable with state-of-the-art methods. In comparison to the latter, our
extractors contain fewer parameters and have faster run-time performance. We
discuss important architectural considerations, and explore the costs and
benefits of this approach.

我们提出了一种层次化元学习启发的模型（Meta-TasNet）用于音乐源分离，其中发生器模型用于预测单个提取器模型的权重，实现高效参数共享，同时仍允许特定于乐器的参数化，比独立训练或多任务设置的模型更有效，实现与最先进方法相当的性能。与后者相比，我们的提取器参数更少，运行时间更快。我们讨论了重要的架构考虑因素，并探讨了这种方法的成本和收益。