@article{nokeye,

title = {Prediction of bioactive compounds hepatotoxicity using in silico and in vitro analysis},

author = {Kwanyong Choi and Soyeon Lee and Sunyong Yoo and Hyoung-Yun Han and Soo-yeon Park and Ji Yeon Kim},

doi = {10.1186/s13765-024-00961-z},

year  = {2024},

date = {2024-12-17},

urldate = {2024-12-17},

journal = {Applied Biological Chemistry},

volume = {67},

number = {107},

abstract = {The leading safety issue and side effect associated with natural herb products is drug-induced liver injury (DILI) caused by bioactive compounds derived from the herb products. Herein, in silico and in vitro analyses were compared to determine the hepatotoxicity of compounds. The results of in silico analyses, which included an integrated database and an interpretable DILI prediction model, identified calycosin, biochanin_A, xanthatin, piperine, and atractyloside as potential hepatotoxic compounds and tenuifolin as a non-hepatotoxic compound. To evaluate the viability of HepG2 cells exposed to the selected compounds, we determined the IC50 and IC20 values of viability using MTT assays. For in-depth screening, we performed hematoxylin and eosin-stained morphological screens, JC-1 mitochondrial assays, and mRNA microarrays. The results indicated that calycosin, biochanin_A, xanthatin, piperine, and atractyloside were potential hepatotoxicants that caused decreased viability and an apoptotic phase in morphology, while these effects were not observed for tenuifolin, a non-hepatotoxicant. In the JC-1 assay, apoptosis was induced by all the predicted hepatotoxicants except atractyloside. According to transcriptomic analysis, all the compounds predicted to induce DILI showed hepatotoxic effects. These results highlighted the importance of using in vitro assays to validate predictive in silico models and determine the potential of bioactive compounds to induce hepatotoxicity in humans.},

note = {Correspondence to Ji Yeon Kim},

keywords = {Drug-induced liver injury, in silico, in vitro},

pubstate = {published},

tppubtype = {article}

}

@article{jeong2024fetoml,

title = {FetoML: Interpretable predictions of the fetotoxicity of drugs based on machine learning approaches},

author = {Myeonghyeon Jeong and Sunyong Yoo},

url = {https://onlinelibrary.wiley.com/doi/full/10.1002/minf.202300312},

doi = {10.1002/minf.202300312},

issn = {1868-1743},

year  = {2024},

date = {2024-03-03},

urldate = {2024-03-03},

journal = {Molecular Informatics},

volume = {43},

number = {6},

pages = {e202300312},

publisher = {Wiley Online Library},

abstract = {Pregnant females may use medications to manage health problems that develop during pregnancy or that they had prior to pregnancy. However, using medications during pregnancy has a potential risk to the fetus. Assessing the fetotoxicity of drugs is essential to ensure safe treatments, but the current process is challenged by ethical issues, time, and cost. Therefore, the need for in silico models to efficiently assess the fetotoxicity of drugs has recently emerged. Previous studies have proposed successful machine learning models for fetotoxicity prediction and even suggest molecular substructures that are possibly associated with fetotoxicity risks or protective effects. However, the interpretation of the decisions of the models on fetotoxicity prediction for each drug is still insufficient. This study constructed machine learning-based models that can predict the fetotoxicity of drugs while providing explanations for the decisions. For this, permutation feature importance was used to identify the general features that the model made significant in predicting the fetotoxicity of drugs. In addition, features associated with fetotoxicity for each drug were analyzed using the attention mechanism. The predictive performance of all the constructed models was significantly high (AUROC: 0.854-0.974, AUPR: 0.890-0.975). Furthermore, we conducted literature reviews on the predicted important features and found that they were highly associated with fetotoxicity. We expect that our model will benefit fetotoxicity research by providing an evaluation of fetotoxicity risks for drugs or drug candidates, along with an interpretation of that prediction.},

note = {Correspondence to Sunyong Yoo},

keywords = {Attention mechanism, Bioinformatics, Deep learning, Fetotoxicity, in silico, Interpretability},

pubstate = {published},

tppubtype = {article}

}

@article{lee2024interdili,

title = {InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism},

author = {Soyeon Lee and Sunyong Yoo},

url = {https://link.springer.com/article/10.1186/s13321-023-00796-8},

doi = {10.1186/s13321-023-00796-8},

year  = {2024},

date = {2024-01-03},

urldate = {2024-01-03},

journal = {Journal of Cheminformatics},

volume = {16},

number = {1},

pages = {1},

publisher = {Springer},

abstract = {Safety is one of the important factors constraining the distribution of clinical drugs on the market. Drug-induced liver injury (DILI) is the leading cause of safety problems produced by drug side effects. Therefore, the DILI risk of approved drugs and potential drug candidates should be assessed. Currently, in vivo and in vitro methods are used to test DILI risk, but both methods are labor-intensive, time-consuming, and expensive. To overcome these problems, many in silico methods for DILI prediction have been suggested. Previous studies have shown that DILI prediction models can be utilized as prescreening tools, and they achieved a good performance. However, there are still limitations in interpreting the prediction results. Therefore, this study focused on interpreting the model prediction to analyze which features could potentially cause DILI. For this, five publicly available datasets were collected to train and test the model. Then, various machine learning methods were applied using substructure and physicochemical descriptors as inputs and the DILI label as the output. The interpretation of feature importance was analyzed by recognizing the following general-to-specific patterns: (i) identifying general important features of the overall DILI predictions, and (ii) highlighting specific molecular substructures which were highly related to the DILI prediction for each compound. The results indicated that the model not only captured the previously known properties to be related to DILI but also proposed a new DILI potential substructural of physicochemical properties. The models for the DILI prediction achieved an area under the receiver operating characteristic (AUROC) of 0.88–0.97 and an area under the Precision-Recall curve (AUPRC) of 0.81–0.95. From this, we hope the proposed models can help identify the potential DILI risk of drug candidates at an early stage and offer valuable insights for drug development.},

note = {Correspondence to Sunyong Yoo},

keywords = {Artificial Intelligence, Attention mechanism, Bioinformatics, Deep learning, Drug-induced liver injury, Feature importance, Hepatotoxicity, in silico},

pubstate = {published},

tppubtype = {article}

}