Modeling and insights into molecular basis of low molecular weight respiratory sensitizers

Cui, Xueyan; Rui, Yang; Li, Siwen; Liu, Juan; Wu, Qiuyun; Li, Xiao

doi:10.1007/s11030-020-10069-3

Cited by 14 publications

(9 citation statements)

References 55 publications

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“…The model building was performed on the online chemical database and modeling environment (OCHEM), which is a user friendly web-based platform for automatic and simple QSAR modeling ( Sushko et al, 2011 ). OCHEM supports the typical steps of QSAR modeling, and the models can be published and publicly used on the web ( Oprisiu et al, 2013 ; Cui et al, 2019 ; Pawar et al, 2019 ; Cui et al, 2021 ; Hua et al, 2021 ; Huang et al, 2021 ; Ta et al, 2021 ). Among the many state-of-the-art modeling methods available on OCHEM, we applied five widely used traditional machine learning (ML) approaches and five different deep learning (DL) algorithms.…”

Section: Methodsmentioning

confidence: 99%

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

et al. 2022

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Drug induced nephrotoxicity is a major clinical challenge, and it is always associated with higher costs for the pharmaceutical industry and due to detection during the late stages of drug development. It is desirable for improving the health outcomes for patients to distinguish nephrotoxic structures at an early stage of drug development. In this study, we focused on in silico prediction and insights into the structural basis of drug induced nephrotoxicity, based on reliable data on human nephrotoxicity. We collected 565 diverse chemical structures, including 287 nephrotoxic drugs on humans in the real world, and 278 non-nephrotoxic approved drugs. Several different machine learning and deep learning algorithms were employed for in silico model building. Then, a consensus model was developed based on three best individual models (RFR_QNPR, XGBOOST_QNPR, and CNF). The consensus model performed much better than individual models on internal validation and it achieved prediction accuracy of 86.24% external validation. The results of analysis of molecular properties differences between nephrotoxic and non-nephrotoxic structures indicated that several key molecular properties differ significantly, including molecular weight (MW), molecular polar surface area (MPSA), AlogP, number of hydrogen bond acceptors (nHBA), molecular solubility (LogS), the number of rotatable bonds (nRotB), and the number of aromatic rings (nAR). These molecular properties may be able to play an important part in the identification of nephrotoxic chemicals. Finally, 87 structural alerts for chemical nephrotoxicity were mined with f-score and positive rate analysis of substructures from Klekota-Roth fingerprint (KRFP). These structural alerts can well identify nephrotoxic drug structures in the data set. The in silico models and the structural alerts could be freely accessed via https://ochem.eu/article/140251 and http://www.sapredictor.cn, respectively. We hope the results should provide useful tools for early nephrotoxicity estimation in drug development.

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Section: Methodsmentioning

confidence: 99%

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

et al. 2022

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“…The data for identification of structural alerts were collected from 1) the databases such as ChEMBL ( Gaulton et al, 2011 ), ChemIDplus ( Tomasulo, 2002 ), Comparative Toxicogenomics Database (CTD) ( Davis et al, 2018 ), Carcinogenic Potency Database (CPDB) ( Gold et al, 1984 ) and DrugBank ( Wishart et al, 2017 ) and 2) peer-reviewed publications through manually filtering and processing. We focused on 22 toxicity endpoints which are of most concern in environmental toxicology and drug discovery, including acute oral toxicity ( Li et al, 2014 ), chemical aquatic toxicity [ Tetrahymena pyriformis ( Cheng et al, 2011 ), Daphnia magna ( Gajewicz-Skretna et al, 2021 ), and fathead minnow ( Sun et al, 2015 )], chemical-induced hematotoxicity ( Hua et al, 2021 ), drug-induced neurotoxicity ( Jiang et al, 2020 ), drug-induced autoimmune diseases ( Wu et al, 2021 ), drug-induced ototoxicity ( Huang et al, 2021 ), drug-induced rhabdomyolysis ( Cui et al, 2019 ), endocrine disruption ( Chen et al, 2014 ), eye irritation ( Wang et al, 2017 ), hepatotoxicity ( Li et al, 2018 ), hERG inhibition ( Li et al, 2017c ), honey bee toxicity ( Li et al, 2017b ), inhalation toxicity ( Cui et al, 2021 ), mitochondrial toxicity ( Nelms et al, 2015 ), mutagenicity ( Yang et al, 2017 ), nephrotoxicity ( Shi et al, 2022 ), non-genotoxic carcinogenicity ( Benigni et al, 2013 ), reproductive and development toxicity ( Fan et al, 2018 ; Jiang et al, 2019 ), skin sensitization ( Di et al, 2019 ), and toxicity on avian species ( Zhang et al, 2015 ). For each toxicity endpoint, we searched the literature separately and included the publications with the same definition of the toxicity endpoint and consistent toxic/non-toxic classification criteria.…”

Section: Methodsmentioning

confidence: 99%

“…Structural alert (SA) is another widely accepted tool for toxicity prediction in recent years, which can be defined as the key substructure which can cause specific toxicity. SA has been commonly used for assessment of many toxicity endpoints ( Benigni et al, 2013 ; Li et al, 2017a ; Limban et al, 2018 ; Kalgutkar, 2020 ; Cui et al, 2021 ; Huang et al, 2021 ; Shi et al, 2022 ) since Ashby and Tennant (1988) proposed the concept in 1985. The SAs can visually alert the toxicity of chemicals by displaying the key fragments responsible for drug toxicity because of the direct derivation from mechanistic knowledge.…”

Section: Introductionmentioning

confidence: 99%

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

et al. 2022

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The rapid and accurate evaluation of chemical toxicity is of great significance for estimation of chemical safety. In the past decades, a great number of excellent computational models have been developed for chemical toxicity prediction. But most machine learning models tend to be “black box”, which bring about poor interpretability. In the present study, we focused on the identification and collection of structural alerts (SAs) responsible for a series of important toxicity endpoints. Then, we carried out effective storage of these structural alerts and developed a web-server named SApredictor (www.sapredictor.cn) for screening chemicals against structural alerts. People can quickly estimate the toxicity of chemicals with SApredictor, and the specific key substructures which cause the chemical toxicity will be intuitively displayed to provide valuable information for the structural optimization by medicinal chemists.

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“…LC 50 is the concentration of the chemical in the air or water that kills 50% of the test animals with a single exposure. Chronic toxicity includes toxicity to reproduction, mutagenicity, and carcinogenicity. − Other than toxicology, there are also QSAR studies about skin sensitization and respiratory sensitization which are defined as the allergic response to a substance after skin contact/inhalation. − Compared to irritation, sensitization is an immunological response to exposure to chemicals. It also worth noting that the QSAR analysis on acute toxicity is mostly regression analysis based on the dependent variable, LC 50 or LD 50 .…”

Section: Applicationmentioning

confidence: 99%

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

Jiao

et al. 2020

ACS Chem. Health Saf.

118

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Machine learning (ML) and deep learning (DL) are a subset of artificial intelligence (AI) that can automatically learn from data and can perform tasks such as predictions and decisionmaking. Interdisciplinary studies combining ML/DL with chemical health and safety have demonstrated their unparalleled advantages in identifying trend and prediction assistance, which can greatly save manpower, material resources, and financial resources. In this Review, commonly used ML/DL tools and concepts as well as popular ML/DL algorithms are introduced and discussed. More than 100 papers have been categorized and summarized to present the current development of ML/DL-based research in the area of chemical health and safety. In addition, the limitation of current studies and prospects of ML/DL-based study are also discussed. This Review can serve as useful guidance for researchers who are interested in implementing ML/DL into chemical health and safety research and for readers who try to learn more information about novel ML/DL techniques and applications.

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Modeling and insights into molecular basis of low molecular weight respiratory sensitizers

Cited by 14 publications

References 55 publications

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

In Silico Prediction and Insights Into the Structural Basis of Drug Induced Nephrotoxicity

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts

Machine Learning and Deep Learning in Chemical Health and Safety: A Systematic Review of Techniques and Applications

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