Machine learning in natural and engineered water systems

R Huang, C Ma, J Ma, X Huangfu, Q He - Water Research, 2021 - Elsevier
Water resources of desired quality and quantity are the foundation for human survival and
sustainable development. To better protect the water environment and conserve water …

Qsar in natural non-peptidic food-related compounds: current status and future perspective

Y Zhao, Y Xia, Y Yu, G Liang - Trends in Food Science & Technology, 2023 - Elsevier
Background Bioactive factors in functional foods play a crucial role in performing their
specific functions. These factors have their own specific physical and chemical properties …

Prediction of groundwater quality using efficient machine learning technique

S Singha, S Pasupuleti, SS Singha, R Singh, S Kumar - Chemosphere, 2021 - Elsevier
To ensure safe drinking water sources in the future, it is imperative to understand the quality
and pollution level of existing groundwater. The prediction of water quality with high …

Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets

Z Wu, M Zhu, Y Kang, ELH Leung, T Lei… - Briefings in …, 2021 - academic.oup.com
Although a wide variety of machine learning (ML) algorithms have been utilized to learn
quantitative structure–activity relationships (QSARs), there is no agreed single best …

Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method

Z Wu, D Jiang, CY Hsieh, G Chen, B Liao… - Briefings in …, 2021 - academic.oup.com
Accurate predictions of druggability and bioactivities of compounds are desirable to reduce
the high cost and time of drug discovery. After more than five decades of continuing …

Current applications and future impact of machine learning in emerging contaminants: A review

L Lei, R Pang, Z Han, D Wu, B Xie… - Critical Reviews in …, 2023 - Taylor & Francis
With the continuous release into environments, emerging contaminants (ECs) have attracted
widespread attention for the potential risks, and numerous studies have been conducted on …

Effectiveness of groundwater heavy metal pollution indices studies by deep-learning

S Singha, S Pasupuleti, SS Singha, S Kumar - Journal of Contaminant …, 2020 - Elsevier
Globally, groundwater heavy metal (HM) pollution is a serious concern, threatening drinking
water safety as well as human and animal health. Therefore, evaluation of groundwater HM …

Investigation on toxicity mechanism of halogenated aromatic disinfection by-products to zebrafish based on molecular docking and QSAR model

JJ Li, YX Yue, SJ Shi, JZ Xue - Chemosphere, 2023 - Elsevier
Halogenated aromatic disinfection by-products (DBPs) are a new type of DBPs that have
been detected in various water bodies. Previous studies have shown that most of them can …

Nationwide policymaking strategies to prevent future electricity crises in developing countries using data-driven forecasting and fuzzy-SWOT analyses

U Safder, TN Hai, J Loy-Benitez, CK Yoo - Energy, 2022 - Elsevier
Over the past decades, forecasting electricity demand has been crucial in energy planning
and power distribution management to establish sustainable strategies in undeveloped …

Knowledge-guided deep learning models of drug toxicity improve interpretation

Y Hao, JD Romano, JH Moore - Patterns, 2022 - cell.com
In drug development, a major reason for attrition is the lack of understanding of cellular
mechanisms governing drug toxicity. The black-box nature of conventional classification …