Eckmann Peter, Anderson Jake, Gilson Michael K, Yu Rose
ArXiv. 2023 Nov 27:arXiv:2311.16328v1.
Predicting the activities of compounds against protein-based or phenotypic assays using only a few known compounds and their activities is a common task in target-free drug discovery. Existing few-shot learning approaches are limited to predicting binary labels (active/inactive). However, in real-world drug discovery, degrees of compound activity are highly relevant. We study Few-Shot Compound Activity Prediction (FS-CAP) and design a novel neural architecture to meta-learn continuous compound activities across large bioactivity datasets. Our model aggregates encodings generated from the known compounds and their activities to capture assay information. We also introduce a separate encoder for the unknown compound. We show that FS-CAP surpasses traditional similarity-based techniques as well as other state of the art few-shot learning methods on a variety of target-free drug discovery settings and datasets.
仅使用少数已知化合物及其活性来预测化合物针对基于蛋白质或表型分析的活性，是无靶点药物发现中的一项常见任务。现有的少样本学习方法仅限于预测二元标签（活性/非活性）。然而，在实际的药物发现中，化合物活性的程度高度相关。我们研究了少样本化合物活性预测（FS-CAP），并设计了一种新颖的神经架构，以跨大型生物活性数据集元学习连续的化合物活性。我们的模型聚合了从已知化合物及其活性生成的编码，以捕获分析信息。我们还为未知化合物引入了一个单独的编码器。我们表明，在各种无靶点药物发现设置和数据集上，FS-CAP超越了传统的基于相似性的技术以及其他先进的少样本学习方法。
