Publications

2023

1. iScience

   Supervised chemical graph mining improves drug-induced liver injury prediction

   Sangsoo Lim, Youngkuk Kim, Jeonghyeon Gu, Sunho Lee, Wonseok Shin, and Sun Kim

   iScience, Jan 2023

   Abs DOI HTML

   

   Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs’ ATC code.

2022

1. Journal of KIISE

   부분 그래프 매칭 문제를 위한 새로운 동적 매칭 순서와 성능 비교

   Seunghwan Min, Wonseok Shin, Chaewon Kim, and Kunsoo Park

   정보과학회논문지, Jan 2022

2022

1. Preprint

   Triangular Contrastive Learning on Molecular Graphs

   MinGyu Choi, Wonseok Shin, Yijingxiu Lu, and Sun Kim

   In Preprint., 2022

   Abs arXiv

   Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances. Regarding the multifaceted nature of large unlabeled data used in self-supervised learning while majority of real-word downstream tasks use single format of data, a multimodal framework that can train single modality to learn diverse perspectives from other modalities is an important challenge. In this paper, we propose TriCL (Triangular Contrastive Learning), a universal framework for trimodal contrastive learning. TriCL takes advantage of Triangular Area Loss, a novel intermodal contrastive loss that learns the angular geometry of the embedding space through simultaneously contrasting the area of positive and negative triplets. Systematic observation on embedding space in terms of alignment and uniformity showed that Triangular Area Loss can address the line-collapsing problem by discriminating modalities by angle. Our experimental results also demonstrate the outperformance of TriCL on downstream task of molecular property prediction which implies that the advantages of the embedding space indeed benefits the performance on downstream tasks.