1. 學術科研

        我校在人工智能藥物發現領域取得新進展

        發布日期:2022-11-21 發表者:陳治國 瀏覽次數:



           (圖文|熊展坤  編輯|劉世超  審核|章文)近日,我校信息學院人工智能與知識發現團隊以“Multi-relational Contrastive Learning Graph Neural Network for Drug-drug Interaction Event Prediction”為題的論文被國際人工智能領域會議AAAI-2023(The 37th AAAI Conference on Artificial Intelligence)錄用。研究團隊提出一種新的藥物反應事件預測方法,解決了現有方法藥物信息整合不全,罕見藥物反應事件預測精度低的問題。


           在同時使用多種藥物來聯合治療復雜疾病時,藥物間的相互作用可能會帶來意想不到的不良后果,如藥物療效降低或藥物毒性增加等,這些不良后果稱為藥物反應事件。如何精準預測藥物反應事件,避免對病人造成傷害和產生巨額醫療費用,是近年來人工智能與藥物發現領域的熱點研究問題。當前藥物反應事件預測方法通常單一地考慮藥物分子結構信息或藥物交互信息,且對于一些發生率較低的藥物反應事件(稱為罕見藥物反應事件)的預測精度較低,這些都限制了藥物反應事件預測模型的性能。


           為解決上述問題,我校研究團隊提出了一種名為多關系對比學習圖神經網絡的藥物反應事件預測方法MRCGNN。該方法將藥物分子結構信息和藥物交互信息進行分層整合,并在藥物反應事件關聯圖上使用新設計的、基于雙視圖負對應增強策略的多關系對比學習來捕獲關于罕見藥物反應事件的隱含信息。


           研究團隊將MRCGNN與僅使用藥物分子結構信息和僅使用藥物交互信息的基線方法進行對比,預測準確度分別提升6.10%和3.99%。此外,在罕見事件預測任務中,MRCGNN較之性能表現最好的對比方法,預測準確度提升30.75%。結果表明,整合藥物分子結構信息和藥物交互信息能提升模型性能,研究團隊所設計的多關系對比學習框架能有效增強對罕見藥物反應事件的表征和預測能力。


           AAAI會議(CCF A類)在人工智能領域具有重要影響力,在業內具有極高評價。信息學院博士生熊展坤、劉世超老師和博士生黃鋒為該研究論文共同第一作者,信息學院章文教授為論文通訊作者,信息學院博士生王紫嫣、博士生劉旋和紐約州立大學Binghamton分校張仲非教授(IEEE Fellow)也參與了該研究工作。


           會議鏈接:https://aaai.org/Conferences/AAAI-23/

        【英文摘要】
        Drug-drug interactions (DDIs) could lead to various unexpected adverse consequences, so-called DDI events. Predicting DDI events can reduce the potential risk of combinatorial therapy and improve the safety of medication use, and has attracted much attention in the deep learning community. Recently, graph neural network (GNN)-based models have aroused broad interest and achieved satisfactory results in the DDI event prediction. Most existing GNN-based models ignore either drug structural information or drug interactive information, but both aspects of information are important for DDI event prediction. Furthermore, accurately predicting rare DDI events is hindered by their inadequate labeled instances. In this paper, we propose a new method, Multi-Relational Contrastive learning Graph Neural Network, MRCGNN for brevity, to predict DDI events. Specifically, MRCGNN integrates the two aspects of information by deploying a GNN on the multi-relational DDI event graph attributed with the drug features extracted from drug molecular graphs. Moreover, we implement a multi-relational graph contrastive learning with a designed dual-view negative counterpart augmentation strategy, to capture implicit information about rare DDI events. Extensive experiments on two datasets show that MRCGNN outperforms the state-of-the-art methods. Besides, we observe that MRCGNN achieves satisfactory performance when predicting rare DDI events.

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