Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866913566933647360 |
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| author | Li, Ruifeng Liu, Wei Zhou, Xiangxin Li, Mingqian Zhang, Qiang Chen, Hongyang Lin, Xuemin |
| author_facet | Li, Ruifeng Liu, Wei Zhou, Xiangxin Li, Mingqian Zhang, Qiang Chen, Hongyang Lin, Xuemin |
| contents | 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 $Δ$AUC-PR metrics, respectively, and exhibits superior generalization capabilities. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_20711 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery Li, Ruifeng Liu, Wei Zhou, Xiangxin Li, Mingqian Zhang, Qiang Chen, Hongyang Lin, Xuemin Machine Learning Artificial Intelligence Biomolecules 68U07 I.2.1 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 $Δ$AUC-PR metrics, respectively, and exhibits superior generalization capabilities. |
| title | Contextual Representation Anchor Network to Alleviate Selection Bias in Few-Shot Drug Discovery |
| topic | Machine Learning Artificial Intelligence Biomolecules 68U07 I.2.1 |
| url | https://arxiv.org/abs/2410.20711 |