Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives

Unveiling the systemic impact of airborne microplastics: Integrating breathomics and machine learning with dual-tissue transcriptomics

Airborne microplastics (MPs) pose significant respiratory and systemic health risks upon inhalation; however, current assessment methods remain inadequate. This study integrates breathomics and transcriptomics to establish a non-invasive approach for evaluating MP-induced damage to the lungs and heart. C57BL/6 mice were exposed to polystyrene MPs (0.1 μm, 2 μm, and 10 μm), and their exhaled volatile organic compounds (VOCs) were analyzed using photoinduced associative ionization time-of-flight mass spectrometry. Machine learning algorithms identified hydrogen sulfide, acetone, acrolein, propionitrile, and butyronitrile as key VOC biomarkers, linking MP exposure to oxidative stress and metabolic dysregulation. Transcriptomic analysis further revealed significant gene expression alterations in pulmonary and cardiac tissues, implicating immune dysregulation, metabolic disturbance, and cardiac dysfunction. Pathway enrichment analysis, supported by histological and immunohistochemical validation, confirmed pulmonary inflammation and cardiac injury. By integrating exhaled biomarker profiling with transcriptomic insights, this study advances non-invasive detection strategies for MP-related health effects, offering valuable prospects for public health monitoring and early diagnosis.

Web server-based deep learning-driven predictive models for respiratory toxicity of environmental chemicals: Mechanistic insights and interpretability

Respiratory toxicity of chemicals is a common clinical and environmental health concern. Currently, most in silico prediction models for chemical respiratory toxicity are often based on a single or vague toxicity endpoint, and machine learning models always lack interpretability. In this study, we developed eight interpretable deep learning models to predict respiratory toxicity of chemicals, focusing on specific respiratory diseases such as pneumonia, pulmonary edema, respiratory infections, pulmonary embolism and pulmonary arterial hypertension, asthma, bronchospasm, bronchitis, and pulmonary fibrosis. In addition, we integrated data from eight respiratory toxicity endpoints into a comprehensive dataset and developed an overall respiratory system model. Model performance was evaluated using 5-fold cross-validation and external validation, with area under the curve (AUC) and accuracy (ACC) values exceeding 0.85 for all eight toxicity endpoints. To enhance model interpretability, we employed the frequency ratio method to identify key structural fragments in Klekota-Roth fingerprints (KRFP) and utilized SHAP (SHapley Additive exPlanations) game theory analysis to visualize critical features driving model predictions. This study demonstrates the role of interpretable deep learning models in predicting the respiratory toxicity of drugs and their environmental metabolites, offering valuable tools and information for early detection and risk assessment of pharmaceutical compounds and environmental pollutants with respiratory toxicity potential.

Application of Bioinformatics and Machine Learning Tools in Food Safety

PURPOSE OF REVIEW

Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early and prevent disease outbreaks, while also optimizing food production and distribution processes.

RECENT FINDINGS

This article discusses the role of new bioinformatics and machine learning technologies in promoting food safety and contamination control, along with various related articles in this field. By analyzing genetic and proteomic data, bioinformatics helps to quickly and accurately identify pathogens and sources of contamination. Machine learning, as a powerful tool for massive data processing, also can discover hidden patterns in the food production and distribution chain, which helps to improve risk prediction and control processes. By reviewing previous research and providing new solutions, this article emphasizes the role of these technologies in identifying, preventing, and improving decisions related to food safety. This study comprehensively shows how the integration of bioinformatics and machine learning can help improve food quality and safety and prevent foodborne disease outbreaks.

Machine learning-optimized advanced oxidation for enhanced sludge dewatering: EPS mechanistic insights and predictive modeling

The recalcitrant nature of extracellular polymeric substances (EPS) in sewage sludge severely limits dewatering efficiency. While advanced oxidation processes (AOPs) disrupt EPS matrices, their optimization remains challenging. This study integrates machine learning (ML) with AOPs to establish predictive frameworks for parameter optimization. A Bayesian-optimized XGBoost model (test R² = 0.87, based on a 70/30 train-test split) outperformed other algorithms in predicting optimal AOP configurations, while an AdaBoost-based model (test R² = 0.81) provided mechanistic insights. Radical donor and catalyst concentrations exhibited synergistic effects (r > 0.8) in hydroxyl radical generation, with pH and VS/TS ratio critically influencing EPS dynamics. Soluble EPS (S-EPS) dominated dewaterability control, whereas tightly bound EPS showed negligible impact. SHAP (SHapley Additive exPlanations) analysis identified radical donor dosage, catalyst loading, and pH as pivotal operational parameters, with acidic conditions enhancing EPS disruption. This work advances data-driven AOP optimization for sludge management, highlighting the need for dynamic EPS transformation studies and adaptive control systems to achieve sustainable wastewater treatment.

Revisiting the approaches to DNA damage detection in genetic toxicology: insights and regulatory implications

Genetic toxicology is crucial for evaluating the potential risks of chemicals and drugs to human health and the environment. The emergence of high-throughput technologies has transformed this field, providing more efficient, cost-effective, and ethically sound methods for genotoxicity testing. It utilizes advanced screening techniques, including automated in vitro assays and computational models to rapidly assess the genotoxic potential of thousands of compounds simultaneously. This review explores the transformation of traditional in vitro and in vivo methods into computational models for genotoxicity assessment. By leveraging advances in machine learning, artificial intelligence, and high-throughput screening, computational approaches are increasingly replacing conventional methods. Coupling conventional screening with artificial intelligence (AI) and machine learning (ML) models has significantly enhanced their predictive capabilities, enabling the identification of genotoxicity signatures tied to molecular structures and biological pathways. Regulatory agencies increasingly support such methodologies as humane alternatives to traditional animal models, provided they are validated and exhibit strong predictive power. Standardization efforts, including the establishment of common endpoints across testing approaches, are pivotal for enhancing comparability and fostering consensus in toxicological assessments. Initiatives like ToxCast exemplify the successful incorporation of HTS data into regulatory decision-making, demonstrating that well-interpreted in vitro results can align with in vivo outcomes. Innovations in testing methodologies, global data sharing, and real-time monitoring continue to refine the precision and personalization of risk assessments, promising a transformative impact on safety evaluations and regulatory frameworks.

Unraveling the complexity of organophosphorus pesticides: Ecological risks, biochemical pathways and the promise of machine learning

Organophosphorus pesticides (OPPs) are widely used in agriculture but pose significant ecological and human health risks due to their persistence and toxicity in the environment. While microbial degradation offers a promising solution, gaps remain in understanding the enzymatic mechanisms, degradation pathways, and ecological impacts of OPP transformation products. This review aims to bridge these gaps by integrating traditional microbial degradation research with emerging machine learning (ML) technologies. We hypothesize that ML can enhance OPP degradation studies by improving the efficiency of enzyme discovery, pathway prediction, and ecological risk assessment. Through a comprehensive analysis of microbial degradation mechanisms, environmental factors, and ML applications, we propose a novel framework that combines biochemical insights with data-driven approaches. Our review highlights the potential of ML to optimize microbial strain screening, predict degradation pathways, and identify key active sites, offering innovative strategies for sustainable pesticide management. By integrating traditional research with cutting-edge ML technologies, this work contributes to the journal's scope by promoting eco-friendly solutions for environmental protection and pesticide pollution control.

A Review of the Applications, Benefits, and Challenges of Generative AI for Sustainable Toxicology

Sustainable toxicology is vital for living species and the environment because it guarantees the safety, efficacy, and regulatory compliance of drugs, treatments, vaccines, and chemicals in living organisms and the environment. Conventional toxicological methods often lack sustainability as they are costly, time-consuming, and sometimes inaccurate. It means delays in producing new drugs, vaccines, and treatments and understanding the adverse effects of the chemicals on the environment. To address these challenges, the healthcare sector must leverage the power of the Generative-AI (GenAI) paradigm. This paper aims to help understand how the healthcare field can be revolutionized in multiple ways by using GenAI to facilitate sustainable toxicological developments. This paper first reviews the present literature and identifies the possible classes of GenAI that can be applied to toxicology. A generalized and holistic visualization of various toxicological processes powered by GenAI is presented in tandem. The paper discussed toxicological risk assessment and management, spotlighting how global agencies and organizations are forming policies to standardize and regulate AI-related development, such as GenAI, in these fields. The paper identifies and discusses the advantages and challenges of GenAI in toxicology. Further, the paper outlines how GenAI empowers Conversational-AI, which will be critical for highly tailored toxicological solutions. This review will help to develop a comprehensive understanding of the impacts and future potential of GenAI in the field of toxicology. The knowledge gained can be applied to create sustainable GenAI applications for various problems in toxicology, ultimately benefiting our societies and the environment.

Marine Heatwaves and Iceberg Melting in Polar Areas Intensify Phytoplankton Blooms

Climate change has led to increases in the intensity and frequency of marine heatwaves (MHWs). However, the impact of MHWs on phytoplankton at the global scale remains unclear. The metaheuristic superlearner proposed in this research indicates that the occurrence of MHWs weakens the Fe limitation of phytoplankton growth, leading to intensified phytoplankton blooms. The shock transmission effect analysis further reveals the interactions among sea surface temperature (SST), iceberg melting, Fe, ammonium (NH 4 + $$ {\mathrm{NH}}_4^{+} $$ ) and nitrate (NO 3 - $$ {\mathrm{NO}}_3^{-} $$ ); namely, the occurrence of MHWs in polar regions has led to iceberg melting, triggering a derivative shock of iceberg melting. Compared with a single MHWs event, the dual shock disrupted the effects of Fe,NH 4 + $$ {\mathrm{NH}}_4^{+} $$ andNO 3 - $$ {\mathrm{NO}}_3^{-} $$ on limiting the growth of phytoplankton, resulting in a 54.90% increase in the growth rate of phytoplankton and leading to the massive reproduction of phytoplankton in polar regions. In addition, compared with that in the low-emission scenario (SSP126), the coverage area of globally fragile marine regions with respect to intensified phytoplankton blooms will increase by 5.84% under the medium-emission scenario (SSP245) and by 9.29% under the high-emission scenario (SSP585). Specifically, the Global South and developing Pacific island countries are fragile regions that need scientific (marine protected area guidance) and financial (such as a foundation for marine protection) assistance to resist the increasing intensity and expansion of phytoplankton blooms under climate change.

Unveiling the effect of urinary xenoestrogens on chronic kidney disease in adults: A machine learning model

Exposure to three primary xenoestrogens (XEs), including phthalates, parabens, and phenols, has been strongly associated with chronic kidney disease (CKD). An interpretable machine learning (ML) model was developed to predict CKD using data from the National Health and Nutrition Examination Survey (NHANES) database spanning from 2007 to 2016. Four ML algorithms-random forest classifier (RF), XGBoost (XGB), k-nearest neighbors (KNN), and support vector machine (SVM)-were used alongside traditional logistic regression to predict CKD. The study included 6910 U.S. adults, with XGB showing the highest predictive accuracy, achieving an area under the curve (AUC) of 0.817 (95 % CI: 0.789, 0.844). The selected model was interpreted using Shapley additive explanations (SHAP) and partial dependence plot (PDP). The SHAP method identified key predictive features for CKD in urinary metabolites of XEs-methyl paraben (MeP), mono-(carboxynonyl) phthalate (MCNP), and triclosan (TCS)-and suggested personalized CKD care should focus on XE control. PDP results confirmed that, within certain ranges, MeP levels positively impacted the model, MCNP levels negatively impacted it, and TCS had a mixed effect. The synergistic effects suggested that managing urinary MeP levels could be essential for the effective control of CKD. In summary, our research highlights the significant predictive potential of XEs for CKD, especially MeP, MCNP, and TCS.

Machine learning-based prediction and model interpretability analysis for algal growth affected by microplastics

Microplastics (MPs), the plastic debris smaller than 5 mm, are ubiquitous in waterbodies and have been shown to be toxic to aquatic organisms, especially to microalgae. The aim of this study is to use machine learning models to predict the effects of MPs on algal growth and to evaluate the relative importance of different features (MP properties, algal characteristics, and experimental conditions) through model interpretability analysis. Based on literature search, 408 samples were collected as inputs for the models. Three integrated machine learning algorithms, Random Forest (RF), Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM), were used to construct classification prediction models for algal growth. Our results show that the LightGBM model yields the best performance, with a total accuracy rate of 0.8305 and a Kappa value of 0.7165. The model interpretability analysis indicates that "Exposure time", "MP concentrations", and "MP size" are the most influential features affecting algal growth. For "Exposure time", which belongs to experimental conditions, 72-216 h of MP exposure was found to exert the greatest effects on algal growth. The impact of MPs on algal growth increases with increasing MP concentrations over the range of 0 to 300 mg/L. Smaller MPs exert more effects on algal growth, and MPs are more likely to inhibit algal growth when the ratio of algal cell size to MP size is higher. Our study successfully established prediction models for evaluating the effects of various MP properties on algal growth. This study also provides insights into the prediction of MP toxicity in organisms.

Effects of Various Heavy Metal Exposures on Insulin Resistance in Non-diabetic Populations: Interpretability Analysis from Machine Learning Modeling Perspective

Increasing and compelling evidence has been proved that heavy metal exposure is involved in the development of insulin resistance (IR). We trained an interpretable predictive machine learning (ML) model for IR in the non-diabetic populations based on levels of heavy metal exposure. A total of 4354 participants from the NHANES (2003-2020) with complete information were randomly divided into a training set and a test set. Twelve ML algorithms, including random forest (RF), XGBoost (XGB), logistic regression (LR), GaussianNB (GNB), ridge regression (RR), support vector machine (SVM), multilayer perceptron (MLP), decision tree (DT), AdaBoost (AB), Gradient Boosting Decision Tree (GBDT), Voting Classifier (VC), and K-Nearest Neighbour (KNN), were constructed for IR prediction using the training set. Among these models, the RF algorithm had the best predictive performance, showing an accuracy of 80.14%, an AUC of 0.856, and an F1 score of 0.74 in the test set. We embedded three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) in RF model for model interpretation. Urinary Ba, urinary Mo, blood Pb, and blood Cd levels were identified as the main influencers of IR. Within a specific range, urinary Ba (0.56-3.56 µg/L) and urinary Mo (1.06-20.25 µg/L) levels exhibited the most pronounced upwards trend with the risk of IR, while blood Pb (0.05-2.81 µg/dL) and blood Cd (0.24-0.65 µg/L) levels showed a declining trend with IR. The findings on the synergistic effects demonstrated that controlling urinary Ba levels might be more crucial for the management of IR. The SHAP decision plot offered personalized care for IR based on heavy metal control. In conclusion, by utilizing interpretable ML approaches, we emphasize the predictive value of heavy metals for IR, especially Ba, Mo, Pb, and Cd.

Transferring knowledge across aquatic species via clustering techniques to unravel patterns of pesticide toxicity

In silico modelling takes the advantage of accelerating ecotoxicological assessments on hazardous chemicals without conducting risky in vivo experiments under ethic regulation. To date, the prevailing strategy of one model for one species cannot be well generalized to multi-species modelling. In this work, we propose a new strategy of one model for multiple species to facilitate knowledge transfer across aquatic species. The available lethal concentration values of 4952 pesticides on 651 fish species are aggregated into one toxicity response matrix, purely through which we attempt to unravel fish toxicosis-phylogenesis relationships and pesticide toxicity-structure relationships via clustering techniques including non-negative matrix factorization (NMF) and hierarchical clustering. The clustering results suggest that (1) close NMF weights indicate close species-toxicosis and pesticide-toxicity profiles; (2) and that species toxicosis patterns are related with species phylogenetic relationships; (3) and that close pesticide-toxicity profiles indicate similar atom-pair structural fingerprints. These environmental, chemical and biological insights can be used as expert knowledge for environmentalists to manually gain knowledge about untested species/pesticides from tested species/pesticides, and meanwhile provide support for us to build in silico models from species phylogenetic and pesticide structural points of view. Besides unravelling the mechanisms behind toxicity response, we also adopt stratified cross validation and external test to validate the reliability of using NMF to predict missing toxicity values. Independent test on external data shows that NMF achieves 0.8404-0.9397 R2 on four fish species. In the context of toxicity prediction, non-negative matrix factorization can be viewed as a model based on quantitative activity-activity relationships (QAAR), and provides an alternative approach of inferring toxicity values on untested species from tested species.

Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish

Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of various pollutants. However, toxicity testing on organisms may cause significant harm, consume considerable time and resources, and raise ethical concerns. Therefore, ML is used in related research to reduce animal experiments and assist researchers in conducting toxicological research. Although ML techniques have matured in various fields, research on ML-based aquatic toxicology is still in its infancy due to the lack of comprehensive large-scale toxicity databases for environmental pollutants and model organisms. Therefore, to better understand the recent research progress of ML in studying the development, behavior, nerve, and genotoxicity of zebrafish, this review mainly focuses on using ML modeling to assess and predict the toxic effects of zebrafish exposure to different toxic chemicals. Meanwhile, the opportunities and challenges faced by ML in the field of toxicology were analyzed. Finally, suggestions and perspectives were proposed for the toxicity studies of ML on zebrafish in future applications.

Understanding the phytotoxic effects of organic contaminants on rice through predictive modeling with molecular descriptors: A data-driven analysis

The widespread introduction of organic compounds into environments poses significant risks to ecosystems. Assessing the adverse effects of organic contaminants on crops is crucial for ensuring food safety. However, laboratory research is often time-consuming and costly, and machine learning (ML) methods can offer a viable solution to address these challenges. This study aimed at developing a ML model that incorporates chemical descriptors to predict the phytotoxicity of organic contaminants on rice. A dataset was compiled by gathering published experimental data on the phytotoxicity of 60 organic compounds, with a focus on morphological inhibition, photosynthesis perturbation, and oxidative stress. Four ML models (RF, SVM, GBM, ANN) were developed using chemical molecular descriptors (CMD) and the Molecular ACCess System (MACCS) keys. RF-MACCS model demonstrated the highest fitness, achieving an R2 value of 0.79 and an RMSE of 0.14. Feature importance analysis highlighted nAtom, HBA, logKow, and TPSA as the most influential CMDs in our model. Additionally, substructures containing oxygen atoms, carbonyl group and carbon chains with nitrogen and oxygen atoms were identified as significant factors associated with phytotoxicity. This data-driven study could aid in predicting the phytotoxicity of organic contaminants on crops and evaluating the potential risks of emerging contaminants in agroecosystems.

Machine learning-derived dose-response relationships considering interactions in mixtures: Applications to the oxidative potential of particulate matter

Conventional environmental health research is primarily focused on isolated chemical exposures, neglecting the complex interactions between multiple pollutants that may synergistically or antagonistically influence toxicity, thereby posing unexpected health risks. In this study, we address this knowledge gap by introducing an explainable machine learning (ML) approach with Feature Localized Intercept Transformed-Shapley Additive Explanations (FLIT-SHAP) designed to extract the dose-response relationships of specific pollutants in mixtures. In contrast to traditional SHAP, FLIT-SHAP can localize the global intercept to elucidate mixture effects, which is crucial for understanding the oxidative potential (OP) of ambient particulate matter (PM). Assessing multi-pollutant OP using FLIT-SHAP revealed both synergistic (55-63 %) and antagonistic (25-42 %) effects in laboratory-controlled OP data, but an antagonistic (33-66 %; lower OP) effect in ambient PM. Notably, the FLIT-SHAP approach demonstrated higher prediction accuracy (R2 = 0.99) compared to the additive model (R2 = 0.89) when evaluated against real-world PM samples. Quinones, such as phenanthrenequinone, play a more significant role in PM2.5 than previously recognized. Through this study, we highlighted the potential of FLIT-SHAP to enhance toxicity predictions and aid decision-making in the field of environmental health.

Assessment of estrogenic potential from exudates of microcystin-producing and non-microcystin-producing Microcystis by metabolomics, machine learning and E-screen assay

Cyanobacterial blooms, often dominated by Microcystis aeruginosa, are capable of producing estrogenic effects. It is important to identify specific estrogenic compounds produced by cyanobacteria, though this can prove challenging owing to the complexity of exudate mixtures. In this study, we used untargeted metabolomics to compare components of exudates from microcystin-producing and non-microcystin-producing M. aeruginosa strains that differed with respect to their ability to produce microcystins, and across two growth phases. We identified 416 chemicals and found that the two strains produced similar components, mainly organoheterocyclic compounds (20.2%), organic acids and derivatives (17.3%), phenylpropanoids and polyketides (12.7%), benzenoids (12.0%), lipids and lipid-like molecules (11.5%), and organic oxygen compounds (10.1%). We then predicted estrogenic compounds from this group using random forest machine learning. Six compounds (daidzin, biochanin A, phenylethylamine, rhein, o-Cresol, and arbutin) belonging to phenylpropanoids and polyketides (3), benzenoids (2), and organic oxygen compound (1) were tested and exhibited estrogenic potency based upon the E-screen assay. This study confirmed that both Microcystis strains produce exudates that contain compounds with estrogenic properties, a growing concern in cyanobacteria management.

Exploring pollutant joint effects in disease through interpretable machine learning

Identifying the impact of pollutants on diseases is crucial. However, assessing the health risks posed by the interplay of multiple pollutants is challenging. This study introduces the concept of Pollutants Outcome Disease, integrating multidisciplinary knowledge and employing explainable artificial intelligence (AI) to explore the joint effects of industrial pollutants on diseases. Using lung cancer as a representative case study, an extreme gradient boosting predictive model that integrates meteorological, socio-economic, pollutants, and lung cancer statistical data is developed. The joint effects of industrial pollutants on lung cancer are identified and analyzed by employing the SHAP (Shapley Additive exPlanations) interpretable machine learning technique. Results reveal substantial spatial heterogeneity in emissions from CPG and ILC, highlighting pronounced nonlinear relationships among variables. The model yielded strong predictions (an R of 0.954, an RMSE of 4283, and an R2 of 0.911) and emphasized the impact of pollutant emission amounts on lung cancer responses. Diverse joint effects patterns were observed, varying in terms of patterns, regions (frequency), and the extent of antagonistic and synergistic effects among pollutants. The study provides a new perspective for exploring the joint effects of pollutants on diseases and demonstrates the potential of AI technology to assist scientific discovery.

Predicting thermal desorption efficiency of PAHs in contaminated sites based on an optimized machine learning approach

Thermal desorption (TD) remediation of polycyclic aromatic hydrocarbon (PAH)-contaminated sites is known for its high energy consumption and cost implications. The key to solving this issue lies in analyzing the PAHs desorption process, defining remediation endpoints, and developing prediction models to prevent excessive remediation. Establishing an accurate prediction model for remediation efficiency, which involves a systematic consideration of soil properties, TD parameters, and PAH characteristics, poses a significant challenge. This study employed a machine learning approach for predicting the remediation efficiency based on batch experiment results. The results revealed that the extreme gradient boosting (XGB) model yielded the most accurate predictions (R2 = 0.9832). The importance of features in the prediction process was quantified. A model optimization scheme was proposed, which involved integrating features based on their relevance, importance, and partial dependence. This integration not only reduced the number of input features but also enhanced prediction accuracy (R2 = 0.9867) without eliminating any features. The optimized XGB model was validated using soils from sites, demonstrating a prediction error of less than 30%. The optimized XGB model aids in identifying the most optimal conditions for thermal desorption to maximize the remediation efficiency of PAH-contaminated sites under relative cost and energy-saving conditions.

Prediction of molecular-specific mutagenic alerts and related mechanisms of chemicals by a convolutional neural network (CNN) model based on SMILES split

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) that was trained and verified with 5850 chemicals from the ISSSTY database and 384 external test chemicals from published papers. The training accuracy was above 0.90 and the evaluation metrics (precision, recall and F1-score) all reached 0.78 or above on both internal and external test chemicals. In this model, the molecular-specific fragment importance of chemicals was first quantified independently. Then, the SA identification method based on the importance of these fragments was statistically analyzed and verified with the ISSSTY test and external test chemicals containing one of 28 typical SAs, and most of the performances were better than that of expert rules. Furthermore, a mutagenicity mechanism prediction method was developed using 237 chemicals with four known mutagenic mechanisms based on molecular similarity calibrated by the SSDL method and fragment importance, which significantly improved accuracy in three mechanisms and had comparable accuracy in the other one compared to traditional methods. Overall, the SSDL model quantifying fragment toxicity within molecules would be a novel potentially powerful tool in the determination and visualization of molecular-specific SAs and the prediction of mutagenicity mechanisms for environmental or industrial compounds and drugs.

Advancing Computational Toxicology by Interpretable Machine Learning

Chemical toxicity evaluations for drugs, consumer products, and environmental chemicals have a critical impact on human health. Traditional animal models to evaluate chemical toxicity are expensive, time-consuming, and often fail to detect toxicants in humans. Computational toxicology is a promising alternative approach that utilizes machine learning (ML) and deep learning (DL) techniques to predict the toxicity potentials of chemicals. Although the applications of ML- and DL-based computational models in chemical toxicity predictions are attractive, many toxicity models are "black boxes" in nature and difficult to interpret by toxicologists, which hampers the chemical risk assessments using these models. The recent progress of interpretable ML (IML) in the computer science field meets this urgent need to unveil the underlying toxicity mechanisms and elucidate the domain knowledge of toxicity models. In this review, we focused on the applications of IML in computational toxicology, including toxicity feature data, model interpretation methods, use of knowledge base frameworks in IML development, and recent applications. The challenges and future directions of IML modeling in toxicology are also discussed. We hope this review can encourage efforts in developing interpretable models with new IML algorithms that can assist new chemical assessments by illustrating toxicity mechanisms in humans.

A benchmark dataset for machine learning in ecotoxicology

The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.

Advances and applications of machine learning and deep learning in environmental ecology and health

Machine learning (ML) and deep learning (DL) possess excellent advantages in data analysis (e.g., feature extraction, clustering, classification, regression, image recognition and prediction) and risk assessment and management in environmental ecology and health (EEH). Considering the rapid growth and increasing complexity of data in EEH, it is of significance to summarize recent advances and applications of ML and DL in EEH. This review summarized the basic processes and fundamental algorithms of the ML and DL modeling, and indicated the urgent needs of ML and DL in EEH. Recent research hotspots such as environmental ecology and restoration, environmental fate of new pollutants, chemical exposures and risks, chemical hazard identification and control were highlighted. Various applications of ML and DL in EEH demonstrate their versatility and technological revolution, and present some challenges. The perspective of ML and DL in EEH were further outlined to promote the innovative analysis and cultivation of the ML-driven research paradigm.

Digital mapping of soil organic carbon density in China using an ensemble model

The soil organic carbon stock (SOCS) is considered as one of the largest carbon reservoirs in terrestrial ecosystems, and small changes in soil can cause significant changes in atmospheric CO2 concentration. Understanding organic carbon accumulation in soils is crucial if China is to meet its dual carbon target. In this study, the soil organic carbon density (SOCD) in China was digitally mapped using an ensemble machine learning (ML) model. First, based on SOCD data obtained at depths of 0-20 cm from 4356 sampling points (15 environmental covariates), we compared the performance of four ML models, namely random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), and artificial neural network (ANN) models, in terms of coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values. Then, we ensembled four models using Voting Regressor and the principle of stacking. The results showed that ensemble model (EM) accuracy was high (RMSE = 1.29, R2 = 0.85, MAE = 0.81), so that it could be a good choice for future research. Finally, the EM was used to predict the spatial distribution of SOCD in China, which ranged from 0.63 to 13.79 kg C/m2 (average = 4.09 (±1.90) kg C/m2). The SOC storage amount in surface soil (0-20 cm) was 39.40 Pg C. This study developed a novel, ensemble ML model for SOC prediction, and improved our understanding of the spatial distribution of SOC in China.

Effects of heavy metal exposure on hypertension: A machine learning modeling approach

Heavy metal exposure is a common risk factor for hypertension. To develop an interpretable predictive machine learning (ML) model for hypertension based on levels of heavy metal exposure, data from the NHANES (2003-2016) were employed. Random forest (RF), support vector machine (SVM), decision tree (DT), multilayer perceptron (MLP), ridge regression (RR), AdaBoost (AB), gradient boosting decision tree (GBDT), voting classifier (VC), and K-nearest neighbour (KNN) algorithms were utilized to generate an optimal predictive model for hypertension. Three interpretable methods, the permutation feature importance analysis, partial dependence plot (PDP), and Shapley additive explanations (SHAP) methods, were integrated into a pipeline and embedded in ML for model interpretation. A total of 9005 eligible individuals were randomly allocated into two distinct sets for predictive model training and validation. The results showed that among the predictive models, the RF model demonstrated the highest performance, achieving an accuracy rate of 77.40% in the validation set. The AUC and F1 score for the model were 0.84 and 0.76, respectively. Blood Pb, urinary Cd, urinary Tl, and urinary Co levels were identified as the main influencers of hypertension, and their contribution weights were 0.0504 ± 0.0482, 0.0389 ± 0.0256, 0.0307 ± 0.0179, and 0.0296 ± 0.0162, respectively. Blood Pb (0.55-2.93 μg/dL) and urinary Cd (0.06-0.15 μg/L) levels exhibited the most pronounced upwards trend with the risk of hypertension within a specific value range, while urinary Tl (0.06-0.26 μg/L) and urinary Co (0.02-0.32 μg/L) levels demonstrated a declining trend with hypertension. The findings on the synergistic effects indicated that Pb and Cd were the primary determinants of hypertension. Our findings underscore the predictive value of heavy metals for hypertension. By utilizing interpretable methods, we discerned that Pb, Cd, Tl, and Co emerged as noteworthy contributors within the predictive model.

Addressing chemical pollution in biodiversity research

Climate change, biodiversity loss, and chemical pollution are planetary-scale emergencies requiring urgent mitigation actions. As these "triple crises" are deeply interlinked, they need to be tackled in an integrative manner. However, while climate change and biodiversity are often studied together, chemical pollution as a global change factor contributing to worldwide biodiversity loss has received much less attention in biodiversity research so far. Here, we review evidence showing that the multifaceted effects of anthropogenic chemicals in the environment are posing a growing threat to biodiversity and ecosystems. Therefore, failure to account for pollution effects may significantly undermine the success of biodiversity protection efforts. We argue that progress in understanding and counteracting the negative impact of chemical pollution on biodiversity requires collective efforts of scientists from different disciplines, including but not limited to ecology, ecotoxicology, and environmental chemistry. Importantly, recent developments in these fields have now enabled comprehensive studies that could efficiently address the manifold interactions between chemicals and ecosystems. Based on their experience with intricate studies of biodiversity, ecologists are well equipped to embrace the additional challenge of chemical complexity through interdisciplinary collaborations. This offers a unique opportunity to jointly advance a seminal frontier in pollution ecology and facilitate the development of innovative solutions for environmental protection.

Computational Exploration of Bio-Degradation Patterns of Various Plastic Types

Plastic materials are recalcitrant in the open environment, surviving for longer without complete remediation. The current disposal methods of used plastic material are inefficient; consequently, plastic wastes are infiltrating the natural resources of the biosphere. The mixed composition of urban domestic waste with different plastic types makes them unfavorable for recycling; however, natural assimilation in situ is still an option to explore. In this research work, we have utilized previously published reports on the biodegradation of various plastics types and analyzed the pattern of microbial degradation. Our results demonstrate that the biodegradation of plastic material follows the chemical classification of plastic types based on their main molecular backbone. The clustering analysis of various plastic types based on their biodegradation reports has grouped them into two broad categories of C-C (non-hydrolyzable) and C-X (hydrolyzable). The C-C and C-X groups show a statistically significant difference in their biodegradation pattern at the genus level. The Bacilli class of bacteria is found to be reported more often in the C-C category, which is challenging to degrade compared to C-X. Genus enrichment analysis suggests that Pseudomonas and Bacillus from bacteria and Aspergillus and Penicillium from fungi are potential genera for the bioremediation of mixed plastic waste. The lack of uniformity in reporting the results of microbial degradation of plastic also needs to be addressed to enable productive growth in the field. Overall, the result points towards the feasibility of a microbial-based biodegradation solution for mixed plastic waste.

Modeling and insights into the structural characteristics of drug-induced autoimmune diseases

The incidence and complexity of drug-induced autoimmune diseases (DIAD) have been on the rise in recent years, which may lead to serious or fatal consequences. Besides, many environmental and industrial chemicals can also cause DIAD. However, there are few effective approaches to estimate the DIAD potential of drugs and other chemicals currently, and the structural characteristics and mechanism of action of DIAD compounds have not been clarified. In this study, we developed the in silico models for chemical DIAD prediction and investigated the structural characteristics of DIAD chemicals based on the reliable drug data on human autoimmune diseases. We collected 148 medications which were reported can cause DIAD clinically and 450 medications that clearly do not cause DIAD. Several different machine learning algorithms and molecular fingerprints were combined to develop the in silico models. The best performed model provided the good overall accuracy on validation set with 76.26%. The model was made freely available on the website http://diad.sapredictor.cn/. To further investigate the differences in structural characteristics between DIAD chemicals and non-DIAD chemicals, several key physicochemical properties were analyzed. The results showed that AlogP, molecular polar surface area (MPSA), and the number of hydrogen bond donors (nHDon) were significantly different between the DIAD and non-DIAD structures. They may be related to the DIAD toxicity of chemicals. In addition, 14 structural alerts (SA) for DIAD toxicity were detected from predefined substructures. The SAs may be helpful to explain the mechanism of action of drug induced autoimmune disease, and can used to identify the chemicals with potential DIAD toxicity. The structural alerts have been integrated in a structural alert-based web server SApredictor (http://www.sapredictor.cn). We hope the results could provide useful information for the recognition of DIAD chemicals and the insights of structural characteristics for chemical DIAD toxicity.