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arXiv · 2311.16328

Target-Free Compound Activity Prediction via Few-Shot Learning

Peter Eckmann·Jake Anderson·Michael K. Gilson·Rose Yu
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Abstract

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.

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Peter Eckmann, Jake Anderson, Michael K. Gilson, Rose Yu. 2023-11-27. Target-Free Compound Activity Prediction via Few-Shot Learning. https://arxiv.org/abs/2311.16328

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