In silico prediction of chemical aquatic toxicity for marine crustaceans via machine learning

Multi-task aquatic toxicity prediction model based on multi-level features fusion

INTRODUCTION

With the escalating menace of organic compounds in environmental pollution imperiling the survival of aquatic organisms, the investigation of organic compound toxicity across diverse aquatic species assumes paramount significance for environmental protection. Understanding how different species respond to these compounds helps assess the potential ecological impact of pollution on aquatic ecosystems as a whole. Compared with traditional experimental methods, deep learning methods have higher accuracy in predicting aquatic toxicity, faster data processing speed and better generalization ability.

OBJECTIVES

This article presents ATFPGT-multi, an advanced multi-task deep neural network prediction model for organic toxicity.

METHODS

The model integrates molecular fingerprints and molecule graphs to characterize molecules, enabling the simultaneous prediction of acute toxicity for the same organic compound across four distinct fish species. Furthermore, to validate the advantages of multi-task learning, we independently construct prediction models, named ATFPGT-single, for each fish species. We employ cross-validation in our experiments to assess the performance and generalization ability of ATFPGT-multi.

RESULTS

The experimental results indicate, first, that ATFPGT-multi outperforms ATFPGT-single on four fish datasets with AUC improvements of 9.8%, 4%, 4.8%, and 8.2%, respectively, demonstrating the superiority of multi-task learning over single-task learning. Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. Moreover, ATFPGT-multi utilizes attention scores to identify molecular fragments associated with fish toxicity in organic molecules, as demonstrated by two organic molecule examples in the main text, demonstrating the interpretability of ATFPGT-multi.

CONCLUSION

In summary, ATFPGT-multi provides important support and reference for the further development of aquatic toxicity assessment. All of codes and datasets are freely available online at https://github.com/zhaoqi106/ATFPGT-multi.

Accumulation characteristics of liquid crystal monomers in plants: A multidimensional analysis

Liquid crystal monomers (LCMs), identified as emerging contaminations, have been detected in soils and plants, but their accumulation characteristics in plants haven't been studied. Therefore, this study systematically investigated the accumulation characteristics of LCMs in plants from four dimensions (i.e., plant fruit species, soil types, plant growth stages, and LCMs categories) for the first time. The LCMs concentrations (9.96 × 10-4 to 114.608 ng/g) in 22 plant fruits were predicted by the partition-limited model. Grains with the highest lipid content showed the highest LCMs accumulation propensity. Plants grown in paddy soil showed a strong LCMs accumulation capacity. Results showed that the LCMs accumulation capacity in plants from soils decreased when the soil organic matter content increased. A preferential accumulation of LCMs in plant root systems during growth was found by the molecular dynamics simulations. Compared to polychlorinated biphenyls (as the reference contaminants of LCMs), LCMs exhibit higher accumulation in plant roots and lower translocation to shoots. For the fourth dimension, lipophilicity was found to be the main reason of LCMs accumulation by intergraded stepwise linear regression with sensitivity analysis. This is the inaugural research concentrating on LCMs accumulation in plants, providing insights and theoretical guidance for future LCMs management strategies multidimensionally.

Analysis of pharmacokinetic profile and ecotoxicological character of cefepime and its photodegradation products

An important problem is the impact of photodegradation on product toxicity in biological tests, which may be complex and context-dependent. Previous studies have described the pharmacology of cefepime, but the toxicological effects of its photodegradation products remain largely unknown. Therefore, photodegradation studies were undertaken in conditions similar to those occurring in biological systems insilico, in vitro, in vivo and ecotoxicological experiments. The structures of four cefepime photodegradation products were determined by UPLC-MS/MS method. The calculated in silico ADMET profile indicates that carcinogenic potential is expected for compounds CP-1, cefepime, CP-2 and CP-3. The Cell Line Cytomotovity Predictor 2.0 tool was used to predict the cytotoxic effects of cefepime and related compounds in non-transformed and cancer cell lines. The results indicate that possible actions include: non-small cell lung cancer, breast adenocarcinoma, prostate cancer and papillary renal cell carcinoma. OPERA models were used to predict absorption, distribution, metabolism and excretion (ADME) endpoints, and potential bioactivity of CP-2, cefepime and CP-4. The results obtained in silico show that after 96h of exposure, cefepime, CP-1, CP-2, and CP-3 are moderately toxic in the zebrafish model, while CP-4 is highly toxic. On the contrary, cefepime is more toxic to T. platyurus (highly toxic) compared to the zebrafish model, similar to products CP-4, CP-3 and CP-2. In vitro cytotoxicity studies were performed by MTT assay and in vivo acute embryo toxicity studies using Danio rerio embryos and larvae. In vitro showed an increase in the cytotoxicity of products with the longest exposure period i.e. for 8 h. Additionally, at a concentration of 200 μg/mL, statistically significant changes in metabolic activity were observed depending on the irradiation time. In vivo studies conducted with Zebrafish showed that both cefepime and its photodegradation products have only low toxicity. Assessment of potential ecotoxicity included Microbiotests on invertebrates (Thamnotoxkit F and Daphtoxkit F), and luminescence inhibition tests (LumiMara). The observed toxicity of the tested solutions towards both Thamnocephalus platyurus and Daphnia magna indicates that the parent substance (unexposed) has lower toxicity, which increases during irradiation. The acute toxicity (Lumi Mara) of nonirradiated cefepime solution is low for all tested strains (<10%), but mixtures of cefepime and its photoproducts showed growth inhibition against all tested strains (except #6, Photobacterium phoreum). Generally, it can be concluded that after UV-Vis irradiation, the mixture of cefepime phototransformation products shows a significant increase in toxicity.

Towards safer pesticide management: A quantitative structure-activity relationship based hazard prediction model

Pesticides are recognized as common environmental contaminants. The potential pesticide hazard to non-target organisms, including various mammal species, is a global concern. The global problem requires a comprehensive risk assessment. To assess the toxic effects of pesticides at the early stage, a toxicological risk analysis is conducted to determine pesticide hazard levels. World Health Organization (WHO) has established five pesticide hazard classes based on lethal dose (LD50) values to perform these assessments. In this paper, we have developed one-vs-all quantitative structure-activity relationship (OvA-QSAR) models using five machine-learning techniques with the selected optimum molecular descriptors. Descriptor selection was conducted based on correlation to evaluate the relevance and significance of individual features in our dataset. Our OvA-QSAR model was built using a dataset obtained from the WHO, covering a wide range of chemical pesticides. These models can predict the hazard category for a pesticide within the five available categories. Notably, our experiments demonstrate the outstanding performance and robustness of the Random Forest (RF) model in addressing the challenge of multi-class classification with the selected descriptors.

Physiological variables in machine learning QSARs allow for both cross-chemical and cross-species predictions

A major challenge in ecological risk assessment is estimating chemical-induced effects across taxa without species-specific testing. Where ecotoxicological data may be more challenging to gather, information on species physiology is more available for a broad range of taxa. Physiology is known to drive species sensitivity but understanding about the relative contribution of specific underlying processes is still elusive. Consequently, there remains a need to understand which physiological processes lead to differences in species sensitivity. The objective of our study was to utilize existing knowledge about organismal physiology to both understand and predict differences in species sensitivity. Machine learning models were trained to predict chemical- and species-specific endpoints as a function of both chemical fingerprints/descriptors and physiological properties represented by dynamic energy budget (DEB) parameters. We found that random forest models were able to predict chemical- and species-specific endpoints, and that DEB parameters were relatively important in the models, particularly for invertebrates. Our approach illuminates how physiological properties may drive species sensitivity, which will allow more realistic predictions of effects across species without the need for additional animal testing.

In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC₅₀) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R² values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules.

Using Machine Learning to make nanomaterials sustainable

Sustainable development is a key challenge for contemporary human societies; failure to achieve sustainability could threaten human survival. In this review article, we illustrate how Machine Learning (ML) could support more sustainable development, covering the basics of data gathering through each step of the Environmental Risk Assessment (ERA). The literature provides several examples showing how ML can be employed in most steps of a typical ERA.A key observation is that there are currently no clear guidance for using such autonomous technologies in ERAs or which standards/checks are required. Steering thus seems to be the most important task for supporting the use of ML in the ERA of nano- and smart-materials. Resources should be devoted to developing a strategy for implementing ML in ERA with a strong emphasis on data foundations, methodologies, and the related sensitivities/uncertainties. We should recognise historical errors and biases (e.g., in data) to avoid embedding them during ML programming.

Prior Knowledge for Predictive Modeling: The Case of Acute Aquatic Toxicity

Early assessment of the potential impact of chemicals on health and the environment requires toxicological properties of the molecules. Predictive modeling is often used to estimate the property values in silico from pre-existing experimental data, which is often scarce and uncertain. One of the ways to advance the predictive modeling procedure might be the use of knowledge existing in the field. Scientific publications contain a vast amount of knowledge. However, the amount of manual work required to process the enormous volumes of information gathered in scientific articles might hinder its utilization. This work explores the opportunity of semiautomated knowledge extraction from scientific papers and investigates a few potential ways of its use for predictive modeling. The knowledge extraction and predictive modeling are applied to the field of acute aquatic toxicity. Acute aquatic toxicity is an important parameter of the safety assessment of chemicals. The extensive amount of diverse information existing in the field makes acute aquatic toxicity an attractive area for investigation of knowledge use for predictive modeling. The work demonstrates that the knowledge collection and classification procedure could be useful in hybrid modeling studies concerning the model and predictor selection, addressing data gaps, and evaluation of models’ performance.

Critical features identification for chemical chronic toxicity based on mechanistic forecast models

Facing billions of tons of pollutants entering the ocean each year, aquatic toxicity is becoming a crucial endpoint for evaluating chemical adverse effects on ecosystems. Notably, huge amount of toxic chemicals at environmental relevant doses can cause potential adverse effects. However, chronic aquatic toxicity effects of chemicals are much scarcer, especially at population level. Rotifers are highly sensitive to toxicants even at chronic low-doses and their communities are usually considered as effective indicators for assessing the status of aquatic ecosystems. Therefore, the no observed effect concentration (NOEC) for population abundance of rotifers were selected as endpoints to develop machine learning models for the prediction of chemical aquatic chronic toxicity. In this study, forty-eight binary models were built by eight types of chemical descriptors combined with six machine learning algorithms. The best binary model was 1D & 2D molecular descriptors - random trees model (RT) with high balanced accuracy (BA) (0.83 for training and 0.83 for validation set), and Matthews correlation coefficient (MCC) (0.72 for training set and 0.67 for validation set). Moreover, the optimal model identified the primary factors (SpMAD_Dzp, AMW, MATS2v) and filtered out three high alerting substructures [c1cc(Cl)cc1, CNCO, CCOP(=S)(OCC)O] influencing the chronic aquatic toxicity. These results showed that the compounds with low molecular volume, high polarity and molecular weight could contribute to adverse effects on rotifers, facilitating the deeper understanding of chronic toxicity mechanisms. In addition, forecast models had better performances than the common models embedded into ECOSAR software. This study provided insights into structural features responsible for the toxicity of different groups of chemicals and thereby allowed for the rational design of green and safer alternatives.

Critical target identification and human health risk ranking of metal ions based on mechanism-driven modeling

Huge amounts of metals have been released into environment due to various anthropogenic activities, such as smelting and processing of metals and subsequent application in construction, automobiles, batteries, optoelectronic devices, and so on, resulting in widespread detection in environmental media. However, some metal ions are considered as "Environmental health hazards", leading to serious human health concerns through affecting critical targets. Hence, it is necessary to quickly and effectively recognize the key target of metal ions in living organisms. Fortunately, the development of high-throughput analysis and in silico approaches offer a promising tool for target identification. In this study, the key oncogenic target (tumor suppressor protein, p53) was screened by network analysis based on the comparative toxicogenomics database (CTD). Some metal ions could bind to p53 core domain, impair its function and induce the development of cancer risk, but its mechanisms were still unclear. Therefore, a quantitative structure-activity relationship (QSAR) model was constructed to characterize the binding constants (K_a) between DNA binding domain of p53 (p53 DBD) and nine metal ions (Mg²⁺, Ca²⁺, Cu²⁺, Zn²⁺, Co²⁺, Ni²⁺, Mn²⁺, Fe³⁺ and Ba²⁺). It had good robustness and predictive ability, which could be used to predict the K_a values of other six metal ions (Li⁺, Ag⁺, Cs⁺, Cd²⁺, Hg²⁺ and Pb²⁺) within application domain. The results showed strong binding affinity between Cd²⁺/Hg²⁺/Pb²⁺ and p53 DBD. Subsequent mechanism analyses revealed that first hydrolysis constant (|logK_OH|) and polarization force (Z²/r) were key metal ion-characteristic parameters. The metal ions with weak hydrolysis constants and strong polarization forces could readily interact with N-containing histidine and S-containing cysteine of p53 DBD, which resulted in high K_a values. This study identified p53 as potential target for metal ions, revealed the key characteristics affecting the actions and provide a basic understanding of metal ions-p53 DBD interaction.

Artificial Intelligence-Based Toxicity Prediction of Environmental Chemicals: Future Directions for Chemical Management Applications

Recently, research on the development of artificial intelligence (AI)-based computational toxicology models that predict toxicity without the use of animal testing has emerged because of the rapid development of computer technology. Various computational toxicology techniques that predict toxicity based on the structure of chemical substances are gaining attention, including the quantitative structure-activity relationship. To understand the recent development of these models, we analyzed the databases, molecular descriptors, fingerprints, and algorithms considered in recent studies. Based on a selection of 96 papers published since 2014, we found that AI models have been developed to predict approximately 30 different toxicity end points using more than 20 toxicity databases. For model development, molecular access system and extended-connectivity fingerprints are the most commonly used molecular descriptors. The most used algorithm among the machine learning techniques is the random forest, while the most used algorithm among the deep learning techniques is a deep neural network. The use of AI technology in the development of toxicity prediction models is a new concept that will aid in achieving a scientific accord and meet regulatory applications. The comprehensive overview provided in this study will provide a useful guide for the further development and application of toxicity prediction models.

In Silico Prediction of Chemical Aquatic Toxicity by Multiple Machine Learning and Deep Learning Approaches

Fish is one of the model animals used to evaluate the adverse effects of a chemical exposed to the ecosystem. However, its low throughput and relevantly high expense make it impossible to test all new chemicals in manufacture. Hence using in silico models to prioritize compounds to be tested has been widely applied in environmental risk assessment and drug discovery. In this study, we constructed the local predictive models for four fish species, including bluegill sunfish, rainbow trout, fathead minnow, and sheepshead minnow, and the global models with all four fish data. A total of 1874 unique compounds with their labels, i.e. toxic (LC₅₀ < 10 ppm) or nontoxic were collected from ECOTOX and literature. Both conventional machine learning methods and the deep learning architecture, graph convolutional network (GCN), were used to build predictive models. The classification accuracy of the best local model for each fish species was higher than 0.83. For the global models, two strategies including consistency prediction and probability threshold were adopted to improve the predictive capability at the cost of limiting applicability domain. For 63% of compounds in domain, the accuracy was around 0.97. By comparison of the deep learning and machine learning methods, we found that the single-task GCN showed specific advantages in performance and multi-task GCN showed no advantages over the conventional machine learning methods. The data and models are available on GitHub (https://github.com/ChemPredict/ChemicalAquaticToxicity).

Targeting PirAvp and PirBvp Toxins of Vibrio parahaemolyticus with Oilseed Peptides: An In Silico Approach

Acute hepatopancreatic necrosis disease (AHPND), caused by PirAvp- and PirBvp-releasing Vibrio parahaemolyticus strains, has resulted in massive mortality in shrimp aquaculture. Excessive use of antibiotics for AHPND management has led to antibiotic resistance, highlighting the urgency to search for alternatives. Using an in silico approach, we aimed to discover PirAvp/PirBvp-binding peptides from oilseed meals as alternatives to antibiotics. To search for peptides that remain intact in the shrimp digestive tract, and therefore would be available for toxin binding, we focused on peptides released from tryptic hydrolysis of 37 major proteins from seeds of hemp, pumpkin, rape, sesame, and sunflower. This yielded 809 peptides. Further screening led to 24 peptides predicted as being non-toxic to shrimp, fish, and humans, with thermal stability and low water solubility. Molecular docking on the 24 peptides revealed six dual-target peptides capable of binding to key regions responsible for complex formation on both PirAvp and PirBvp. The peptides (ISYVVQGMGISGR, LTFVVHGHALMGK, QSLGVPPQLGNACNLDNLDVLQPTETIK, ISTINSQTLPILSQLR, PQFLVGASSILR, and VQVVNHMGQK) are 1139-2977 Da in mass and 10-28 residues in length. Such peptides are potential candidates for the future development of peptide-based anti-AHPND agents which potentially mitigate V. parahaemolyticus pathogenesis by intercepting PirAvp/PirBvp complex formation.

Generating accurate in silico predictions of acute aquatic toxicity for a range of organic chemicals: Towards similarity-based machine learning methods

There has been an increase in the use of non-animal approaches, such as in silico and/or in vitro methods, for assessing the risks of hazardous chemicals. A number of machine learning algorithms link molecular descriptors that interpret chemical structural properties with their biological activity. These computer-aided methods encounter several challenges, the most significant being the heterogeneity of datasets; more efficient and inclusive computational methods that are able to process large and heterogeneous chemical datasets are needed. In this context, this study verifies the utility of similarity-based machine learning methods in predicting the acute aquatic toxicity of diverse organic chemicals on Daphnia magna and Oryzias latipes. Two similarity-based methods were tested that employ a limited training dataset, most similar to a given fitting point, instead of using the entire dataset that encompasses a wide range of chemicals. The kernel-weighted local polynomial approach had a number of advantages over the distance-weighted k-nearest neighbor (k-NN) algorithm. The results highlight the importance of lipophilicity, electrophilic reactivity, molecular polarizability, and size in determining acute toxicity. The rigorous model validation ensures that this approach is an important tool for estimating toxicity in new or untested chemicals.

Aquatic toxicity (Pre)screening strategy for structurally diverse chemicals: global or local classification tree models?

With an ever-increasing number of synthetic chemicals being manufactured, it is unrealistic to expect that they will all be subjected to comprehensive and effective risk assessment. A shift from conventional animal testing to computer-aided methods is therefore an important step towards advancing the environmental risk assessments of chemicals. The aims of this study are two-fold: firstly, it examines the relationships between structural and physicochemical features of a diverse set of organic chemicals, and their acute aquatic toxicity towards Daphnia magna and Oryzias latipes using a classification tree approach. Secondly, it compares the efficiency and accuracy of the predictions of two modeling schemes: local models that are inherently restricted to a smaller subset of structurally-related substances, and a global model that covers a wider chemical space and a number of modes of toxic action. The classification tree-based models differentiate the organic chemicals into either 'highly toxic' or 'low to non-toxic' classes, based on internal and external validation criteria. These mechanistically-driven models, which demonstrate good performance, reveal that the key factors driving acute aquatic toxicity are lipophilicity, electrophilic reactivity, molecular polarizability and size. A comparative analysis of the performance of the two modeling schemes indicates that the local models, trained on homogeneous data sets, are less error prone, and therefore superior to the global model. Although the global models showed worse performance metrics compared to the local ones, their applicability domain is much wider, thereby significantly increasing their usefulness in practical applications for regulatory purposes. This demonstrates their advantage over local models and shows they are an invaluable tool for modeling heterogeneous chemical data sets.

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

Respiratory sensitization has been considered an important toxicological endpoint, because of the severe risk to human health. A great part of sensitization events were caused by low molecular weight (< 1000) respiratory sensitizers in the past decades. However, there is currently no widely accepted test method that can identify prospective low molecular weight respiratory sensitisers. Herein, we performed the study of modeling and insights into molecular basis of low molecular weight respiratory sensitizers with a high-quality data set containing 136 respiratory sensitizers and 518 nonsensitizers. We built a number of classification models by using OCHEM tools, and a consensus model was developed based on the ten best individual models. The consensus model showed good predictive ability with a balanced accuracy of 0.78 and 0.85 on fivefold cross-validation and external validation, respectively. The readers can predict the respiratory sensitization of organic compounds via https://ochem.eu/article/114857 . The effect of several molecular properties on respiratory sensitization was also evaluated. The results indicated that these properties differ significantly between respiratory sensitizers and nonsensitizers. Furthermore, 14 privileged substructures responsible for respiratory sensitization were identified. We hope the models and the findings could provide useful help for environmental risk assessment.