The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

Choi, Kwang-Eun; Balupuri, Anand; Kang, Nam Sook

doi:10.3390/molecules25112615

Cited by 27 publications

(20 citation statements)

References 32 publications

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“…Here, we have collected 15 works from the past five years that employ machine learning-based classification approaches to predict hERG inhibition [38][39][40][41][42][43][44][45][46][47][48][49][50][51]. All of these works apply training datasets of more than 1,000 molecules (and up to tens of thousands in some cases [47,48]), and an overall majority presents two-class (active vs. inactive) classification (with the notable example of the 2015 study of Braga et al, who have introduced a third class of "weak blockers") [38].…”

Section: Herg-mediated Cardiotoxicitymentioning

confidence: 99%

Machine learning models for classification tasks related to drug safety

et al. 2021

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In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015–2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood–brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts. Graphical abstract

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Section: Herg-mediated Cardiotoxicitymentioning

confidence: 99%

Machine learning models for classification tasks related to drug safety

et al. 2021

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“…The IC50 cutoff value of antibacterial activity was defined by curve fitting the IC50 values of all compounds. Compounds with IC50 less than 10 μmol/L were generally considered as active antibacterial compounds [ 53 – 57 ], the curve fitting results also suggest that this cutoff is reasonable ( Supplementary Figure 8 ). Based on the curve fitting results, compounds with IC50 higher than 10 μmol/L were considered inactive antibacterial compounds.…”

Section: Methodsmentioning

confidence: 99%

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods

Tong

Yang

et al. 2022

Aging

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Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showed that 1087 small-molecule drugs have potential antibacterial activity and 154 of them are FDA-approved antibacterial drugs, which accounts for 76.2% of the approved antibacterial drugs collected in this study. Through molecular fingerprint similarity analysis and common substructure analysis, we screened 8 predicted antibacterial small-molecule compounds with novel structures compared with known antibacterial drugs, and 5 of them are widely used in the treatment of various tumors. This study provides a new insight for predicting antibacterial compounds by using approved drugs, the predicted compounds might be used to treat bacterial infections and extend lifespan.

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“…Individual cardiotoxicities for specific drug types were also predicted with high accuracy (AUC = 80%) as well as heart failures with the potential for antineoplastic agents (AUC = 76%); Lee et al 73 generated a reliable hERG‐related cardiotoxicity data set composed of more than 2000 compounds, evaluating toxicities as IC 50 values, and developed a computational hERG‐related cardiotoxicity prediction neural network model (available as a web tool at http://ssbio.cau.ac.kr/CardPred) achieving an AUC of 76%, accuracy of 90%, MCC of 0.368, sensitivity of 32% and a specificity of 97%, when 10‐fold cross‐validation was performed. Choi et al 74 collected more than 5000 hERG inhibitors from the ChEMBL database and evaluated different ML and DL algorithms. The performance of the developed models was evaluated using a test set of 998 compounds.…”

Section: Ai‐based Toxicity Predictionmentioning

confidence: 99%

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

Pérez‐Santín

Rodríguez‐Solana

García

et al. 2021

WIREs Comput Mol Sci

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The use and production of chemical compounds are subjected to strong legislative pressure. Chemical toxicity and adverse effects derived from exposure to chemicals are key regulatory aspects for a multitude of industries, such as chemical, pharmaceutical, or food, due to direct harm to humans, animals, plants, or the environment. Simultaneously, there are growing demands on the authorities to replace traditional in vivo toxicity tests carried out on laboratory animals (e.g., European Union REACH/3R principles, Tox21 and ToxCast by the U.S. government, etc.) with in silica computational models. This is not only for ethical aspects, but also because of its greater economic and time efficiency, as well as more recently because of their superior reliability and robustness than in vivo tests, mainly since the entry into the scene of artificial intelligence (AI)‐based models, promoting and setting the necessary requirements that these new in silico methodologies must meet. This review offers a multidisciplinary overview of the state of the art in the application of AI‐based methodologies for the fulfillment of regulatory‐related toxicological issues. This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

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The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

Cited by 27 publications

References 32 publications

Machine learning models for classification tasks related to drug safety

Machine learning models for classification tasks related to drug safety

Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods

Toxicity prediction based on artificial intelligence: A multidisciplinary overview

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