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<p>Human microorganisms are closely related to human health by regulating the immune system, producing hormones, and contributing to metabolism. Discovering potential associations between microbes and drugs aid drug research and development. This work proposes a novel computational framework called self-supervised metapath aggregated microbe-drug association prediction network (SMMDA-Net) to predict potential microbe-drug associations. Various biological datasets are leveraged to construct heterogeneous networks using microbe-drug associations and microbe-disease, disease-drug transitive associations. Feature matrices containing information on microbe functional similarity, microbe genome similarity, drug structure similarity and drug side-effect-based similarity are extracted. Then, a novel model called self-supervised metapath-based node embedding generator (SMNEG) is used to generate low-dimensional meaningful embeddings for nodes in the heterogeneous network. A CNN classifier is adopted and trained by the low-dimensional embeddings to predict potential microbe-drug associations. Extensive experiments on three datasets show that the proposed model outperforms six state-of-the-art methods. It achieved an AUC of 98.23% and an AUPR of 95.14% on the MDAD dataset, an AUC of 99.22% and an AUPR of 94.86% on the aBiofilm dataset, and an AUPR of 87.98% on the DrugVirus dataset. Furthermore, ablation and case studies were performed to evaluate the effectiveness of SMMDA-Net in predicting potential microbe-drug associations.</p>

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Microbe-drug association prediction using metapath aggregated node embeddings and self-supervised learning

  • 期刊论文

  • 发表日期：

    2025年4月29日

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  • K. Syama,
  • J. Angel Arul Jothi,
  • Anushka Sivakumar

摘要

Human microorganisms are closely related to human health by regulating the immune system, producing hormones, and contributing to metabolism. Discovering potential associations between microbes and drugs aid drug research and development. This work proposes a novel computational framework called self-supervised metapath aggregated microbe-drug association prediction network (SMMDA-Net) to predict potential microbe-drug associations. Various biological datasets are leveraged to construct heterogeneous networks using microbe-drug associations and microbe-disease, disease-drug transitive associations. Feature matrices containing information on microbe functional similarity, microbe genome similarity, drug structure similarity and drug side-effect-based similarity are extracted. Then, a novel model called self-supervised metapath-based node embedding generator (SMNEG) is used to generate low-dimensional meaningful embeddings for nodes in the heterogeneous network. A CNN classifier is adopted and trained by the low-dimensional embeddings to predict potential microbe-drug associations. Extensive experiments on three datasets show that the proposed model outperforms six state-of-the-art methods. It achieved an AUC of 98.23% and an AUPR of 95.14% on the MDAD dataset, an AUC of 99.22% and an AUPR of 94.86% on the aBiofilm dataset, and an AUPR of 87.98% on the DrugVirus dataset. Furthermore, ablation and case studies were performed to evaluate the effectiveness of SMMDA-Net in predicting potential microbe-drug associations.