Dataset Information

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

ABSTRACT: 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.

SUBMITTER: Hua Y

PROVIDER: S-EPMC9326022 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

Hua Yuqing Y Cui Xueyan X Liu Bo B Shi Yinping Y Guo Huizhu H Zhang Ruiqiu R Li Xiao X

Frontiers in chemistry 20220713

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 ...[more]

PMID: 35910729

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Project description:The article presents a Web-based platform for collecting and storing toxicological structural alerts from literature and for virtual screening of chemical libraries to flag potentially toxic chemicals and compounds that can cause adverse side effects. An alert is uniquely identified by a SMARTS template, a toxicological endpoint, and a publication where the alert was described. Additionally, the system allows storing complementary information such as name, comments, and mechanism of action, as well as other data. Most importantly, the platform can be easily used for fast virtual screening of large chemical datasets, focused libraries, or newly designed compounds against the toxicological alerts, providing a detailed profile of the chemicals grouped by structural alerts and endpoints. Such a facility can be used for decision making regarding whether a compound should be tested experimentally, validated with available QSAR models, or eliminated from consideration altogether. The alert-based screening can also be helpful for an easier interpretation of more complex QSAR models. The system is publicly accessible and tightly integrated with the Online Chemical Modeling Environment (OCHEM, http://ochem.eu). The system is open and expandable: any registered OCHEM user can introduce new alerts, browse, edit alerts introduced by other users, and virtually screen his/her data sets against all or selected alerts. The user sets being passed through the structural alerts can be used at OCHEM for other typical tasks: exporting in a wide variety of formats, development of QSAR models, additional filtering by other criteria, etc. The database already contains almost 600 structural alerts for such endpoints as mutagenicity, carcinogenicity, skin sensitization, compounds that undergo metabolic activation, and compounds that form reactive metabolites and, thus, can cause adverse reactions. The ToxAlerts platform is accessible on the Web at http://ochem.eu/alerts, and it is constantly growing.

Project description:Much research has assessed organic chemicals of concern (COCs) in municipal wastewater and receiving waters, but few studies have examined COCs in land treatment systems. Many prior studies have implemented targeted methods that quantify a relatively small fraction of COCs present in wastewater and receiving waters. This study used suspect screening to assess chemical features in ground- and surface waters from a watershed where secondary-treated wastewater is irrigated onto 900 ha of temperate forest, offering a more holistic view of chemicals that contribute to the exposome. Chemical features were prioritized by abundance and ToxPi scoring across seasonal sampling events to determine if the forest-water reuse system contributed to the chemical exposome of ground- and surface waters. The number of chemical features detected in wastewater was usually higher than on- and off-site ground- and surface waters; in wastewater, chemical features trended with precipitation in which greater numbers of features were detected in months with low precipitation. The number of chemical features detected in off- and on-site waters was similar. The lower overlap between chemical features found in wastewater and downstream surface waters, along with the similar numbers of features being detected in upstream and downstream surface waters, suggests that though wastewater may be a source of chemicals to ground and surface waters on-site, dissipation of wastewater-derived features (in number and peak area abundance) likely occurs with limited off-site surface water export by the forested land treatment system. Further, the numbers of features detected on site and the overlap between wastewater and surface waters did not increase during periods of low rainfall, counter to our initial expectations. The chemical features tentatively identified in this watershed appear common to features identified in other studies, warranting further examination on the potential for resulting impacts of these on humans and the environment.

Project description:Structural alerts are commonly used in drug discovery to identify molecules likely to form reactive metabolites and thereby become toxic. Unfortunately, as useful as structural alerts are, they do not effectively model if, when, and why metabolism renders safe molecules toxic. Toxicity due to a specific structural alert is highly conditional, depending on the metabolism of the alert, the reactivity of its metabolites, dosage, and competing detoxification pathways. A systems approach, which explicitly models these pathways, could more effectively assess the toxicity risk of drug candidates. In this study, we demonstrated that mathematical models of P450 metabolism can predict the context-specific probability that a structural alert will be bioactivated in a given molecule. This study focuses on the furan, phenol, nitroaromatic, and thiophene alerts. Each of these structural alerts can produce reactive metabolites through certain metabolic pathways but not always. We tested whether our metabolism modeling approach, XenoSite, can predict when a given molecule's alerts will be bioactivated. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. Metabolism models accurately predict whether alerts are bioactivated and thus serve as a practical approach to improve the interpretability and usefulness of structural alerts. We expect that this same computational approach can be extended to most other structural alerts and later integrated into toxicity risk models. This advance is one necessary step toward our long-term goal of building comprehensive metabolic models of bioactivation and detoxification to guide assessment and design of new therapeutic molecules.

Dataset Information

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

Publications

SApredictor: An Expert System for Screening Chemicals Against Structural Alerts.

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