Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery

• URL: http://arxiv.org/abs/2410.20711v2
• Date: Tue, 29 Oct 2024 06:40:41 GMT
• Title: Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
• Authors: Ruifeng Li, Wei Liu, Xiangxin Zhou, Mingqian Li, Qiang Zhang, Hongyang Chen, Xuemin Lin,
• Abstract summary: We present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules. CRA introduces a dual-augmentation mechanism that includes context augmentation, which dynamically retrieves analogous unlabeled molecules. We evaluate our approach on the MoleculeNet and FS-Mol benchmarks, as well as in domain transfer experiments.
• Score: 34.32009652184957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the drug discovery process, the low success rate of drug candidate screening often leads to insufficient labeled data, causing the few-shot learning problem in molecular property prediction. Existing methods for few-shot molecular property prediction overlook the sample selection bias, which arises from non-random sample selection in chemical experiments. This bias in data representativeness leads to suboptimal performance. To overcome this challenge, we present a novel method named contextual representation anchor Network (CRA), where an anchor refers to a cluster center of the representations of molecules and serves as a bridge to transfer enriched contextual knowledge into molecular representations and enhance their expressiveness. CRA introduces a dual-augmentation mechanism that includes context augmentation, which dynamically retrieves analogous unlabeled molecules and captures their task-specific contextual knowledge to enhance the anchors, and anchor augmentation, which leverages the anchors to augment the molecular representations. We evaluate our approach on the MoleculeNet and FS-Mol benchmarks, as well as in domain transfer experiments. The results demonstrate that CRA outperforms the state-of-the-art by 2.60% and 3.28% in AUC and $\Delta$AUC-PR metrics, respectively, and exhibits superior generalization capabilities.

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