Linking electron ionization mass spectra of organic chemicals to toxicity endpoints through machine learning and experimentation

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.

BitterMasS: Predicting Bitterness from Mass Spectra

Bitter compounds are common in nature and among drugs. Previously, machine learning tools were developed to predict bitterness from the chemical structure. However, known structures are estimated to represent only 5-10% of the metabolome, and the rest remain unassigned or "dark". We present BitterMasS, a Random Forest classifier that was trained on 5414 experimental mass spectra of bitter and nonbitter compounds, achieving precision = 0.83 and recall = 0.90 for an internal test set. Next, the model was tested against spectra newly extracted from the literature 106 bitter and nonbitter compounds and for additional spectra measured for 26 compounds. For these external test cases, BitterMasS exhibited 67% precision and 93% recall for the first and 58% accuracy and 99% recall for the second. The spectrum-bitterness prediction strategy was more effective than the spectrum-structure-bitterness prediction strategy and covered more compounds. These encouraging results suggest that BitterMasS can be used to predict bitter compounds in the metabolome without the need for structural assignment of individual molecules. This may enable identification of bitter compounds from metabolomics analyses, for comparing potential bitterness levels obtained by different treatments of samples and for monitoring bitterness changes overtime.

Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils

Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH4) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristics of biochar and soil properties that influence biochar's performance. Here, we successfully developed an interpretable multitask deep learning (MTDL) model by employing a tensor tracking paradigm to facilitate parameter sharing between two separate data sets, enabling a synergy between Cd and CH4 mitigation with biochar amendments. The characteristics of biochar contribute similar weightings of 67.9% and 62.5% to Cd and CH4 mitigation, respectively, but their relative importance in determining biochar's performance varies significantly. Notably, this MTDL model excels in custom-tailoring biochar to synergistically mitigate Cd and CH4 in paddy soils across a wide geographic range, surpassing traditional machine learning models. Our findings deepen our understanding of the interactive effects of Cd and CH4 mitigation with biochar amendments in paddy soils, and they also potentially extend the application of artificial intelligence in sustainable environmental remediation, especially when dealing with multiple objectives.

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds

Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause biased regulation of chemicals. In the present study, we implemented comprehensive prediction models involving multiple advanced machine learning and end-to-end deep learning to assess the aquatic toxicity of chemicals. The generated optimal models accurately unravel the quantitative structure-toxicity relationships, with the correlation coefficients of all training sets from 0.59 to 0.81 and of the test sets from 0.56 to 0.83. For each chemical, its ecological risk was determined from the toxicity information towards multiple species. The results also revealed the toxicity mechanism of chemicals was species sensitivity, and the high-level organisms were faced with more serious side effects from hazardous substances. The proposed approach was finally applied to screen over 16,000 compounds and identify high-risk chemicals. We believe that the current approach can provide a useful tool for predicting the toxicity of diverse organic chemicals and help regulatory authorities make more reasonable decisions.

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)-toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

Fire retardant performance, toxicity and combustion characteristics, and numerical evaluation of core materials for sandwich panels

According to fire accident statistics, fires in buildings are increasing. The flame-retardant performance of insulation materials is considered an important factor for preventing the spread of fire and ensuring evacuation. This study evaluated the flame-retardant performance and combustion characteristics of four types of organic thermal insulation used as core materials in sandwich panels. The flame-retardant performance evaluation based on total heat release and heat release rate revealed that phenolic foam (PF) satisfied the criteria for non-combustible grade insulation. An analysis of the hazardous gases released while combustion of the four insulation materials indicated that a significant amount of CO was released-an average of 19,000 ppm or higher-in the rigid urethan foam (PIR) and spray-type polyurethane foam (SPU). The fractional effective dose (FED) value was derived from the gas analysis results according to ISO 13344. PIR and SPU had an average FED value of 2.0 or higher and were identified as very dangerous in the case of fire accidents. Moreover, the evacuation time in the case of a fire in a warehouse-type building was comprehensively analyzed considering the material, size, and height for the four types of insulation. PIR was the most vulnerable to fire, and for PF, the danger limit was not reached until the end of the simulation.

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

Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in the early stages, with knowledge gaps, technical bottlenecks in data quality, high-dimensional/heterogeneous/small-sample data analysis and model interpretability, and a lack of an in-depth understanding of environmental toxicology. Given the above problems, we review the recent progress in the literature and highlight state-of-the-art toxicological studies using ML (such as learning and predicting toxicity in complicated biosystems and multiple-factor environmental scenarios of long-term and large-scale pollution). Beyond predicting simple biological endpoints by integrating untargeted omics and adverse outcome pathways, ML development should focus on revealing toxicological mechanisms. The integration of data-driven ML with other methods (e.g., omics analysis and adverse outcome pathway frameworks) endows ML with widely promising application in revealing toxicological mechanisms. High-quality databases and interpretable algorithms are urgently needed for toxicology and environmental science. Addressing the core issues and future challenges for ML in this review may narrow the knowledge gap between environmental toxicity and computational science and facilitate the control of environmental risk in the future.