A deep learning based multi-model approach for predicting drug-like chemical compound's toxicity.

Ensuring the safety and efficacy of chemical compounds is crucial in small-molecule drug development. In the later stages of drug development, toxic compounds pose a significant challenge, losing valuable resources and time. Early and accurate prediction of compound toxicity using deep learning models offers a promising solution to mitigate these risks during drug discovery. In this study, we present the development of several deep-learning models aimed at evaluating different types of compound toxicity, including acute toxicity, carcinogenicity, hERG_cardiotoxicity (the human ether-a-go-go related gene caused cardiotoxicity), hepatotoxicity, and mutagenicity. To address the inherent variations in data size, label type, and distribution across different types of toxicity, we employed diverse training strategies. Our first approach involved utilizing a graph convolutional network (GCN) regression model to predict acute toxicity, which achieved notable performance with Pearson R 0.76, 0.74, and 0.65 for intraperitoneal, intravenous, and oral administration routes, respectively. Furthermore, we trained multiple GCN binary classification models, each tailored to a specific type of toxicity. These models exhibited high area under the curve (AUC) scores, with an impressive AUC of 0.69, 0.77, 0.88, and 0.79 for predicting carcinogenicity, hERG_cardiotoxicity, mutagenicity, and hepatotoxicity, respectively. Additionally, we have used the approved drug dataset to determine the appropriate threshold value for the prediction score in model usage. We integrated these models into a virtual screening pipeline to assess their effectiveness in identifying potential low-toxicity drug candidates. Our findings indicate that this deep learning approach has the potential to significantly reduce the cost and risk associated with drug development by expediting the selection of compounds with low toxicity profiles. Therefore, the models developed in this study hold promise as critical tools for early drug candidate screening and selection.

Biophysical and structural characterization of tetramethrin serum protein complex and its toxicological implications.

Tetramethrin (TMT) is a commonly used insecticide and has a carcinogenic and neurodegenerative effect on humans. The binding mechanism and toxicological implications of TMT to human serum albumin (HSA) were examined in this study employing a combination of biophysical and computational methods indicating moderate binding affinity and potential hepato and renal toxicity. Fluorescence quenching experiments showed that TMT binds to HSA with a moderate affinity, and the binding process was spontaneous and predominantly enthalpy-driven. Circular dichroism spectroscopy revealed that TMT binding did not induce any significant conformational changes in HSA, resulting in no changes in its alpha-helix content. The binding site and modalities of TMT interactions with HSA as computed by molecular docking and molecular dynamics simulations revealed that it binds to Sudlow site II of HSA via hydrophobic interactions through its dimethylcyclopropane carboxylate methyl propanyl group. The structural dynamics of TMT induce proper fit into the binding site creating increased and stabilizing interactions. Additionally, molecular mechanics-Poisson Boltzmann surface area calculations also indicated that non-polar and van der Waals were found to be the major contributors to the high binding free energy of the complex. Quantum mechanics (QM) revealed the conformational energies of the binding confirmation and the degree of deviation from the global minimum energy conformation of TMT. The results of this study provide a comprehensive understanding of the binding mechanism of TMT with HSA, which is important for evaluating the toxicity of this insecticide in humans.

Synergistic integration of deep learning with protein docking in cardiovascular disease treatment strategies.

This research delves into the exploration of the potential of tocopherol-based nanoemulsion as a therapeutic agent for cardiovascular diseases (CVD) through an in-depth molecular docking analysis. The study focuses on elucidating the molecular interactions between tocopherol and seven key proteins (1O8a, 4YAY, 4DLI, 1HW9, 2YCW, 1BO9 and 1CX2) that play pivotal roles in CVD development. Through rigorous in silico docking investigations, assessment was conducted on the binding affinities, inhibitory potentials and interaction patterns of tocopherol with these target proteins. The findings revealed significant interactions, particularly with 4YAY, displaying a robust binding energy of -6.39 kcal/mol and a promising Ki value of 20.84 µM. Notable interactions were also observed with 1HW9, 4DLI, 2YCW and 1CX2, further indicating tocopherol's potential therapeutic relevance. In contrast, no interaction was observed with 1BO9. Furthermore, an examination of the common residues of 4YAY bound to tocopherol was carried out, highlighting key intermolecular hydrophobic bonds that contribute to the interaction's stability. Tocopherol complies with pharmacokinetics (Lipinski's and Veber's) rules for oral bioavailability and proves safety non-toxic and non-carcinogenic. Thus, deep learning-based protein language models ESM1-b and ProtT5 were leveraged for input encodings to predict interaction sites between the 4YAY protein and tocopherol. Hence, highly accurate predictions of these critical protein-ligand interactions were achieved. This study not only advances the understanding of these interactions but also highlights deep learning's immense potential in molecular biology and drug discovery. It underscores tocopherol's promise as a cardiovascular disease management candidate, shedding light on its molecular interactions and compatibility with biomolecule-like characteristics.

Suspect, Nontarget Screening, and Toxicity Prediction of Per- and Polyfluoroalkyl Substances in the Landfill Leachate.

Landfills are the final stage of urban wastes containing perfluoroalkyl and polyfluoroalkyl substances (PFASs). PFASs in the landfill leachate may contaminate the surrounding groundwater. As major environmental pollutants, emerging PFASs have raised global concern. Besides the widely reported legacy PFASs, the distribution and potential toxic effects of numerous emerging PFASs remain unclear, and unknown PFASs still need discovery and characterization. This study proposed a comprehensive method for PFAS screening in leachate samples using suspect and nontarget analysis. A total of 48 PFASs from 10 classes were identified; nine novel PFASs including eight chloroperfluoropolyether carboxylates (Cl-PFPECAs) and bistriflimide (HNTf2) were reported for the first time in the leachate, where Cl-PFPECA-3,1 and Cl-PFPECA-2,2 were first reported in environmental media. Optimized molecular docking models were established for prioritizing the PFASs with potential activity against peroxisome proliferator-activated receptor α and estrogen receptor α. Our results indicated that several emerging PFASs of N-methyl perfluoroalkyl sulfonamido acetic acids (N-MeFASAAs), n:3 fluorotelomer carboxylic acid (n:3 FTCA), and n:2 fluorotelomer sulfonate (n:2 FTSA) have potential health risks that cannot be ignored.

Screening Novel Per- and Polyfluoroalkyl Substances in Human Blood Based on Nontarget Analysis and Underestimated Potential Health Risks.

Nontarget analysis has gained prominence in screening novel perfluoroalkyl and polyfluoroalkyl substances (PFASs) in the environment, yet remaining limited in human biological matrices. In this study, 155 whole blood samples were collected from the general population in Shijiazhuang City, China. By nontarget analysis, 31 legacy and novel PFASs were assigned with the confidence level of 3 or above. For the first time, 11 PFASs were identified in human blood, including C1 and C3 perfluoroalkyl sulfonic acids (PFSAs), C4 ether PFSA, C8 ether perfluoroalkyl carboxylic acid (ether PFCA), C4-5 unsaturated perfluoroalkyl alcohols, C9-10 carboxylic acid-perfluoroalkyl sulfonamides (CA-PFSMs), and C1 perfluoroalkyl sulfonamide. It is surprising that the targeted PFASs were the highest in the suburban population which was impacted by industrial emission, while the novel PFASs identified by nontarget analysis, such as C1 PFSA and C9-11 CA-PFSMs, were the highest in the rural population who often drank contaminated groundwater. Combining the toxicity prediction results of the bioaccumulation potential, lethality to rats, and binding affinity to target proteins, C3 PFSA, C4 and C7 ether PFSAs, and C9-11 CA-PFSMs exhibit great health risks. These findings emphasize the necessity of broadening nontarget analysis in assessing the PFAS exposure risks, particularly in rural populations.

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of in vivo toxicity data. We therefore developed a two-stage machine learning (ML) framework to predict human-equivalent PODs for oral exposure to organic chemicals based on chemical structure. Utilizing ML-based predictions for structural/physical/chemical/toxicological properties from OPERA 2.9 as features (Stage 1), ML models using random forest regression were trained with human-equivalent PODs derived from in vivo data sets for general noncancer effects (n = 1,791) and reproductive/developmental effects (n = 2,228), with robust cross-validation for feature selection and estimating generalization errors (Stage 2). These two-stage models accurately predicted PODs for both effect categories with cross-validation-based root-mean-squared errors less than an order of magnitude. We then applied one or both models to 34,046 chemicals expected to be in the environment, revealing several thousand chemicals of moderate concern and several hundred chemicals of high concern for health effects at estimated median population exposure levels. Further application can expand by orders of magnitude the coverage of organic chemicals that can be evaluated for their human health risks and impacts.

Fabrication of black phosphorus/CdS heterostructure with enhancement photocatalytic degradation activity for tetrabromobisphenol A and toxicity prediction of intermediates.

Black phosphorus nanosheets (BPNs)/CdS heterostructure was successfully synthesized via hydrothermal method. The experimental results indicated that BPNs modified the surface of CdS nanoparticles uniformly. Meanwhile, the BPNs/CdS heterostructure exhibited a distinguished high rate of photocatalytic activity for Tetrabromobisphenol A (TBBPA) degradation under visible light irradiation (λ > 420 nm), the kinetic constant of TBBPA degradation reached 0.0261 min-1 was approximately 5.68 and 9.67 times higher than that of CdS and P25, respectively. Moreover, superoxide radical (•O2-) is the main active component in the degradation process of TBBPA (the relative contribution is 91.57%). The photocatalytic mechanism and intermediates of the TBBPA was clarified, and a suitable model and pathway for the degradation of TBBPA were proposed. The results indicated that the toxicities of some intermediates were higher than the parent pollutant. This research provided an efficient approach by a novel photocatalyst for the removal of TBBPA from wastewater, and the appraisal methods for the latent risks from the intermediates were reported in this paper.

The rat acute oral toxicity of trifluoromethyl compounds (TFMs): a computational toxicology study combining the 2D-QSTR, read-across and consensus modeling methods.

All areas of the modern society are affected by fluorine chemistry. In particular, fluorine plays an important role in medical, pharmaceutical and agrochemical sciences. Amongst various fluoro-organic compounds, trifluoromethyl (CF3) group is valuable in applications such as pharmaceuticals, agrochemicals and industrial chemicals. In the present study, following the strict OECD modelling principles, a quantitative structure-toxicity relationship (QSTR) modelling for the rat acute oral toxicity of trifluoromethyl compounds (TFMs) was established by genetic algorithm-multiple linear regression (GA-MLR) approach. All developed models were evaluated by various state-of-the-art validation metrics and the OECD principles. The best QSTR model included nine easily interpretable 2D molecular descriptors with clear physical and chemical significance. The mechanistic interpretation showed that the atom-type electro-topological state indices, molecular connectivity, ionization potential, lipophilicity and some autocorrelation coefficients are the main factors contributing to the acute oral toxicity of TFMs against rats. To validate that the selected 2D descriptors can effectively characterize the toxicity, we performed the chemical read-across analysis. We also compared the best QSTR model with public OPERA tool to demonstrate the reliability of the predictions. To further improve the prediction range of the QSTR model, we performed the consensus modelling. Finally, the optimum QSTR model was utilized to predict a true external set containing many untested/unknown TFMs for the first time. Overall, the developed model contributes to a more comprehensive safety assessment approach for novel CF3-containing pharmaceuticals or chemicals, reducing unnecessary chemical synthesis whilst saving the development cost of new drugs.

Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives.

Toxicity prediction is a critical step in the drug discovery process that helps identify and prioritize compounds with the greatest potential for safe and effective use in humans, while also reducing the risk of costly late-stage failures. It is estimated that over 30% of drug candidates are discarded owing to toxicity. Recently, artificial intelligence (AI) has been used to improve drug toxicity prediction as it provides more accurate and efficient methods for identifying the potentially toxic effects of new compounds before they are tested in human clinical trials, thus saving time and money. In this review, we present an overview of recent advances in AI-based drug toxicity prediction, including the use of various machine learning algorithms and deep learning architectures, of six major toxicity properties and Tox21 assay end points. Additionally, we provide a list of public data sources and useful toxicity prediction tools for the research community and highlight the challenges that must be addressed to enhance model performance. Finally, we discuss future perspectives for AI-based drug toxicity prediction. This review can aid researchers in understanding toxicity prediction and pave the way for new methods of drug discovery.

Machine Learning-Based Hazard-Driven Prioritization of Features in Nontarget Screening of Environmental High-Resolution Mass Spectrometry Data.

Nontarget high-resolution mass spectrometry screening (NTS HRMS/MS) can detect thousands of organic substances in environmental samples. However, new strategies are needed to focus time-intensive identification efforts on features with the highest potential to cause adverse effects instead of the most abundant ones. To address this challenge, we developed MLinvitroTox, a machine learning framework that uses molecular fingerprints derived from fragmentation spectra (MS2) for a rapid classification of thousands of unidentified HRMS/MS features as toxic/nontoxic based on nearly 400 target-specific and over 100 cytotoxic endpoints from ToxCast/Tox21. Model development results demonstrated that using customized molecular fingerprints and models, over a quarter of toxic endpoints and the majority of the associated mechanistic targets could be accurately predicted with sensitivities exceeding 0.95. Notably, SIRIUS molecular fingerprints and xboost (Extreme Gradient Boosting) models with SMOTE (Synthetic Minority Oversampling Technique) for handling data imbalance were a universally successful and robust modeling configuration. Validation of MLinvitroTox on MassBank spectra showed that toxicity could be predicted from molecular fingerprints derived from MS2 with an average balanced accuracy of 0.75. By applying MLinvitroTox to environmental HRMS/MS data, we confirmed the experimental results obtained with target analysis and narrowed the analytical focus from tens of thousands of detected signals to 783 features linked to potential toxicity, including 109 spectral matches and 30 compounds with confirmed toxic activity.

Risk assessment based on dose-responsive and time-responsive genes to build PLS-DA models for exogenously induced lung injury.

Xenobiotics can easily harm human lungs owing to the openness of the respiratory system. Identifying pulmonary toxicity remains challenging owing to several reasons: 1) no biomarkers for pulmonary toxicity are available that might help to detect lung injury; 2) traditional animal experiments are time-consuming; 3) traditional detection methods solely focus on poisoning accidents; 4) analytical chemistry methods hardly achieve universal detection. An in vitro testing system able to identify the pulmonary toxicity of contaminants from food, the environment, and drugs is urgently needed. Compounds are virtually infinite, whereas toxicological mechanisms are countable. Therefore, universal methods to identify and predict the risks of contaminants can be designed based on these well-known toxicity mechanisms. In this study, we established a dataset based on transcriptome sequencing of A549 cells upon treatment with different compounds. The representativeness of our dataset was analyzed using bioinformatics methods. Artificial intelligence methods, namely partial least squares discriminant analysis (PLS-DA) models, were employed for toxicity prediction and toxicant identification. The developed model predicted the pulmonary toxicity of compounds with a 92 % accuracy. These models were submitted to an external validation using highly heterogeneous compounds, which supported the accuracy and robustness of our developed methodology. This assay exhibits universal potential applications for water quality monitoring, crop pollution detection, food and drug safety evaluation, as well as chemical warfare agent detection.

5,6-Dihydrotetrazolo[1,5-c]quinazolines: Toxicity prediction, synthesis, antimicrobial activity, molecular docking, and perspectives.

Antimicrobial resistance is a never-ending challenge, which should be considered seriously, especially when using unprescribed "over-the-counter" drugs. The synthesis and investigation of novel biologically active substances is among the directions to overcome this problem. Hence, 18 novel 5,6-dihydrotetrazolo[1,5-c]quinazolines were synthesized, their identity, purity, and structure were elucidated by elemental analysis, IR, LC-MS, 1 Н, and 13 C NMR spectra. According to the computational estimation, 15 substances were found to be of toxicity Class V, two of Class IV, and only one of Class II. The in vitro serial dilution method of antimicrobial screening against Escherichia coli, Staphylococcus aureus, Klebsiella aerogenes, Pseudomonas aeruginosa, and Candida albicans determined b3, c1, c6, and c10 as the "lead-compounds" for further modifications to increase the level of activity. Substance b3 demonstrated antibacterial activity that can be related to the calculated high affinity toward all studied proteins: 50S ribosomal protein L19 (PDB ID: 6WQN), sterol 14-alpha demethylase (PDB ID: 5TZ1), and ras-related protein Rab-9A (PDB ID: 1WMS). The structure-activity and structure-target affinity relationships are discussed. The targets for further investigations and the anatomical therapeutic chemical codes of drug similarity are predicted.

Advanced Situation with Recombinant Toxins: Diversity, Production and Application Purposes.

Today, the production and use of various samples of recombinant protein/polypeptide toxins is known and is actively developing. This review presents state-of-the-art in research and development of such toxins and their mechanisms of action and useful properties that have allowed them to be implemented into practice to treat various medical conditions (including oncology and chronic inflammation applications) and diseases, as well as to identify novel compounds and to detoxify them by diverse approaches (including enzyme antidotes). Special attention is given to the problems and possibilities of the toxicity control of the obtained recombinant proteins. The recombinant prions are discussed in the frame of their possible detoxification by enzymes. The review discusses the feasibility of obtaining recombinant variants of toxins in the form of protein molecules modified with fluorescent proteins, affine sequences and genetic mutations, allowing us to investigate the mechanisms of toxins' bindings to their natural receptors.

Molecular Toxicity Virtual Screening Applying a Quantized Computational SNN-Based Framework.

Spiking neural networks are biologically inspired machine learning algorithms attracting researchers' attention for their applicability to alternative energy-efficient hardware other than traditional computers. In the current work, spiking neural networks have been tested in a quantitative structure-activity analysis targeting the toxicity of molecules. Multiple public-domain databases of compounds have been evaluated with spiking neural networks, achieving accuracies compatible with high-quality frameworks presented in the previous literature. The numerical experiments also included an analysis of hyperparameters and tested the spiking neural networks on molecular fingerprints of different lengths. Proposing alternatives to traditional software and hardware for time- and resource-consuming tasks, such as those found in chemoinformatics, may open the door to new research and improvements in the field.

Can Pharmaceutical Excipients Threaten the Aquatic Environment? A Risk Assessment Based on the Microtox® Biotest.

The ecotoxicological impact of pharmaceuticals has received considerable attention, primarily focusing on active pharmaceutical ingredients (APIs) while largely neglecting the potential hazards posed by pharmaceutical excipients. Therefore, we analyzed the ecotoxicity of 16 commonly used pharmaceutical excipients, as well as 26 API-excipient and excipient-excipient mixtures utilizing the Microtox® test. In this way, we assessed the potential risks that pharmaceutical excipients, generally considered safe, might pose to the aquatic environment. We investigated both their individual ecotoxicity and their interactions with tablet ingredients using concentration addition (CA) and independent action (IA) models to shed light on the often-overlooked ecotoxicological consequences of these substances. The CA model gave a more accurate prediction of toxicity and should be recommended for modeling the toxicity of combinations of drugs with different effects. A challenge when studying the ecotoxicological impact of some pharmaceutical excipients is their poor water solubility, which hinders the use of standard aquatic ecotoxicity testing techniques. Therefore, we used a modification of the Microtox® Basic Solid Phase protocol developed for poorly soluble substances. The results obtained suggest the high toxicity of some excipients, i.e., SLS and meglumine, and confirm the occurrence of interactions between APIs and excipients. Through this research, we hope to foster a better understanding of the ecological impact of pharmaceutical excipients, prompting the development of risk assessment strategies within the pharmaceutical industry.

Phytochemical Analysis, In Vitro Biological Activities, and Computer-Aided Analysis of Potentilla nepalensis Hook Compounds as Potential Melanoma Inhibitors Based on Molecular Docking, MD Simulations, and ADMET.

Potentilla nepalensis Hook is a perennial Himalayan medicinal herb of the Rosaceae family. The present study aimed to evaluate biological activities such as the antioxidant, antibacterial, and anticancer activities of roots and shoots of P. nepalensis and its synergistic antibacterial activity with antibacterial drugs. Folin-Ciocalteau and aluminium chloride methods were used for the calculation of total phenolic (TPC) and flavonoid content (TFC). A DPPH radical scavenging assay and broth dilution method were used for the determination of the antioxidant and antibacterial activity of the root and shoot extracts of P. nepalensis. Cytotoxic activity was determined using a colorimetric MTT assay. Further, phytochemical characterization of the root and shoot extracts was performed using the Gas chromatography-mass spectrophotometry (GC-MS) method. The TPC and TFC were found to be higher in the methanolic root extract of P. nepalensis. The methanolic shoot extract of P. nepalensis showed good antioxidant activity, while then-hexane root extract of P. nepalensis showed strong cytotoxic activity against tested SK-MEL-28 cells. Subsequently, in silico molecular docking studies of the identified bioactive compounds predicted potential anticancer properties. This study can lead to the production of new herbal medicines for various diseases employing P. nepalensis, leading to the creation of new medications.

In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation.

Drug-induced liver injury (DILI) is one of the major causes of post-approval withdrawal of therapeutics. As a result, there is an increasing need for accurate predictive in vitro assays that reliably detect hepatotoxic drug candidates while reducing drug discovery time, costs, and the number of animal experiments. In vitro hepatocyte-based research has led to an improved comprehension of the underlying mechanisms of chemical toxicity and can assist the prioritization of therapeutic choices with low hepatotoxicity risk. Therefore, several in vitro systems have been generated over the last few decades. This review aims to comprehensively present the development and validation of two-dimensional (2D) and three-dimensional (3D) culture approaches on hepatotoxicity screening of compounds and highlight the main factors affecting predictive power of experiments. To this end, we first summarize some of the recognized hepatotoxicity mechanisms and related assays used to appraise DILI mechanisms and then discuss the challenges and limitations of in vitro models.

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.

Comprehensive Exposure Studies of Per- and Polyfluoroalkyl Substances in the General Population: Target, Nontarget Screening, and Toxicity Prediction.

Novel per- and polyfluoroalkyl substances (PFASs) in the environment and populations have received extensive attention; however, their distribution and potential toxic effects in the general population remain unclear. Here, a comprehensive study on PFAS screening was carried out in serum samples of 202 individuals from the general population in four cities in China. A total of 165 suspected PFASs were identified using target and nontarget analysis, including seven identified PFAS homolog series, of which 16 PFASs were validated against standards, and seven PFASs [4:2 chlorinated polyfluorinated ether sulfonate (4:2 Cl-PFESA), 7:2 chlorinated polyfluorinated ether sulfonate (7:2 Cl-PFESA), hydrosubstituted perfluoroheptanoate (H-PFHpA), chlorine-substituted perfluorooctanoate (Cl-PFOA), chlorine-substituted perfluorononanate (Cl-PFNA), chlorine-substituted perfluorodecanoate (Cl-PFDA), and perfluorodecanedioic acid (PFLDCA n = 8)] were reported for the first time in human serum. The Tox21-GCN model (a graph convolutional neural network model based on the Tox21 database) was established to predict the toxicity of the discovered PFASs, revealing that PFASs containing sulfonic acid groups exhibited multiple potential toxic effects, such as estrogenic effects and stress responses. Our study indicated that the general population was exposed to various PFASs, and the toxicity prediction results of individual PFASs suggested potential health risks that could not be ignored.

Construction of an In Silico Structural Profiling Tool Facilitating Mechanistically Grounded Classification of Aquatic Toxicants.

The performance of chemical safety assessment within the domain of environmental toxicology is often impeded by a shortfall of appropriate experimental data describing potential hazards across the many compounds in regular industrial use. In silico schemes for assigning aquatic-relevant modes or mechanisms of toxic action to substances, based solely on consideration of chemical structure, have seen widespread employment─including those of Verhaar, Russom, and later Bauer (MechoA). Recently, development of a further system was reported by Sapounidou, which, in common with MechoA, seeks to ground its classifications in understanding and appreciation of molecular initiating events. Until now, this Sapounidou scheme has not seen implementation as a tool for practical screening use. Accordingly, the primary purpose of this study was to create such a resource─in the form of a computational workflow. This exercise was facilitated through the formulation of 183 structural alerts/rules describing molecular features associated with narcosis, chemical reactivity, and specific mechanisms of action. Output was subsequently compared relative to that of the three aforementioned alternative systems to identify strengths and shortcomings as regards coverage of chemical space.