EDC-Predictor: A Novel Strategy for Prediction of Endocrine-Disrupting Chemicals by Integrating Pharmacological and Toxicological Profiles

Artificial Intelligence for the Discovery of Safe and Effective Flame Retardants

Organophosphorus flame retardants (OPFRs) are important chemical additives that are used in commercial products. However, owing to increasing health concerns, the discovery of new OPFRs has become imperative. Herein, we propose an explainable artificial intelligence-assisted product design (AIPD) methodological framework for screening novel, safe, and effective OPFRs. Using a deep neural network, we established a flame retardancy prediction model with an accuracy of 0.90. Employing the SHapley Additive exPlanations approach, we have identified the Morgan 507 (P═N connected to a benzene ring) and 114 (quaternary carbon) substructures as promoting units in flame retardancy. Subsequently, approximately 600 compounds were selected as OPFR candidates from the ZINC database. Further refinement was achieved through a comprehensive scoring system that incorporated absorption, toxicity, and persistence, thereby yielding six prospective candidates. We experimentally validated these candidates and identified compound Z2 as a promising candidate, which was not toxic to zebrafish embryos. Our methodological framework leverages AIPD to effectively guide the discovery of novel flame retardants, significantly reducing both developmental time and costs.

TCnet: A Novel Strategy to Predict Target Combination of Alzheimer's Disease via Network-Based Methods

Alzheimer's disease (AD) is a complex neurodegenerative disorder with an unclear pathogenesis; the traditional ″single gene-single target-single drug″ strategy is insufficient for effective treatment. This study explores a novel strategy for the multitarget therapy of AD by integrating multiomics data and employing network analysis. Different from conventional single-target methods, TCnet adopts a mechanism-driven strategy, utilizing multiomics data to decompose disease mechanisms, construct potential target combinations, and prioritize the optimal combinations using a scoring function. TCnet not only advances our understanding of disease mechanisms but also facilitates large-scale drug screening. This approach was further employed to screen active compounds from Huang-Lian-Jie-Du-Tang (HLJDT), identifying quercetin as a candidate targeting GSK3β and ADAM17. Subsequent in vitro experiments confirmed the neuroprotective and anti-inflammatory effects of quercetin. Overall, TCnet offers a promising approach for predicting target combinations and provides new insights and directions for drug discovery in AD.

Toxic Alerts of Endocrine Disruption Revealed by Explainable Artificial Intelligence

The local interpretable model-agnostic explanation method was used to unveil substructures (toxic alerts) that cause endocrine disruption in chemical compounds using machine learning models. The random forest classifier was applied to build explainable models with the TOX21 data sets after data curation. Using these models applied to the EDC and EDKB-FDA data sets, the substructures that cause endocrine disruption in chemical compounds were unveiled, providing stable, more specific, and consistent explanations, which are essential for trust and acceptance of the findings, mainly due to the difficulty of finding relevant experimental evidence for different receptors (androgen, estrogen, aryl hydrocarbon, aromatase, and peroxisome proliferator-activated receptors). This approach is significant because of its contribution to the interpretability of explainable machine learning algorithms, particularly in the context of unveiling substructures associated with endocrine disruption in five targets (androgen receptor, estrogen receptor, aryl hydrocarbon receptors, aromatase receptors, and peroxisome proliferator-activated receptors), thereby advancing the relevant field of environmental toxicology, where a careful evaluation of the potential risks of exposure to new compounds is needed. The specific substructures thiophosphate, sulfamate, anilide, carbamate, sulfamide, and thiocyanate are presented as toxic alerts that cause endocrine disruption to better understand their potential risks and adverse effects on human health and the environment.

Novel Approach to Screen Endocrine-Disrupting Chemicals via Endocrine-Enhanced Reduced Human Transcriptome

Endocrine-disrupting chemicals (EDCs) can interfere with multiple pathways and trigger different modes of action. Thus, the traditional EDC in vitro screening processes often require a battery of bioassays to cover multiple target pathways. Here we developed an endocrine-enhanced reduced human transcriptome (ERHT) focused on hormone receptor signaling induced by the EDCs regulating specific genes. ERHT was developed based on 1200 prioritized genes covering 110 endocrine-related biological pathways across eight potential adverse outcomes. The ability of this approach to identify EDCs was derived from machine learning of 1068 dose-dependent transcriptome profiles and enhanced by quantifying chemical-induced critical pathway responses, and thus, it demonstrated excellent classification performance (AUC = 0.84 ± 0.03) in internal cross-validation. We ultimately applied this approach to known EDCs and inactive substances to validate the reliability of this approach. Through external validation on 210 chemicals, the extrapolation accuracy exceeded 80%, demonstrating the outstanding practical performance of this approach. Meanwhile, the pathway responses induced by the same chemical were consistent with the experimental results reported by multiple sequencing platforms, highlighting the robustness of this approach. The above results demonstrate that this approach can provide novel insights for EDCs' high-throughput screening and comprehensive toxic mechanisms through biological pathways.

Screening of estrogen receptor activity of per- and polyfluoroalkyl substances based on deep learning and in vivo assessment

Over the past decades, exposure to per- and polyfluoroalkyl substances (PFAS), a group of synthetic chemicals notorious for their environmental persistence, has been shown to pose increased health risks. Despite that some PFAS were reported to have endocrine-disrupting toxicity in previous studies, accurate prediction models based on deep learning and the underlying structural characteristics related to the effect of molecular fluorination remain limited. To address these issues, we proposed a stacking deep learning architecture, GXDNet, that integrates molecular descriptors and molecular graphs to predict the estrogen receptor α (ERα) activities of compounds, enhancing the generalization ability compared to previous models. Subsequently, we screened the ERα activity of 10,067 PFAS molecules using the GXDNet model and identified potential ERα binders. The representative PFAS molecules with the top docking scores showed that the introduction of fluorinated alkane chains significantly increased the binding affinities of parent molecules with ERα, suggesting that the combination of phenol structural fragments and fluorinated alkane chains has a synergistic effect in improving the binding capacity of the ligands to ERα. The binding modes, SHapley Additive Explanations analysis, and attention map emphasized the importance of π-π stacking and hydrogen bonding interactions with the phenol group, while the fluorinated alkane chain enhanced the interaction with the hydrophobic amino acids of the active pocket. Experimental validation using zebrafish models further confirmed the ERα activity of the representative PFAS molecules. Overall, the current computational workflow is beneficial for the toxicological screening of emerging PFAS and accelerating the development of eco-friendly PFAS molecules, thereby mitigating the environmental and health risks associated with PFAS exposure.

Exposure to oxygenated polycyclic aromatic hydrocarbons and endocrine dysfunction: Multi-level study based on hormone receptor responses

Oxygenated polycyclic aromatic hydrocarbons (OPAHs) are a class of emerging environmental contaminants that exhibit high toxicity compared to parent PAHs. In addition to carcinogenic, teratogenic and mutagenic effects, recent studies show their potential to cause endocrine disruption, but the reports are controversial. In this study, we employed hormone receptors (ERα/AR/GRα/TRβ)-mediated dual luciferase reporter gene assay and molecular docking, and found that five typical OPAHs exhibited agonistic activity towards hormone receptors, and hydrogen bonding and hydrophobic interactions were the primary binding forces involved in OPAHs-receptor interactions. Then, we developed a weighted scoring system coupled with computerized screening and clarified that 1,2-benzanthraquinone (BAQ) had the strongest hormonal effects, while anthraquinone (AQ) exhibited the weakest effects. Using the in vivo exposure model, we clarified that BAQ induced hormone receptor-coupled developmental toxicity in zebrafish larvae, evidenced by increased expression of androgen receptors and key genes involved in hormone synthesis, pericardial edema and reduced body length. Importantly, we successfully constructed androgen response element-enhanced green fluorescent protein (ARE-EGFP) transient transfection zebrafish embryos, and confirmed the androgenic potency of BAQ, but not AQ. These findings highlight the endocrine-disrupting effects in the risk management of OPAHs.

Molecular designing of potential environmentally friendly PFAS based on deep learning and generative models

Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are widely used across a spectrum of industrial and consumer goods. Nonetheless, their persistent nature and tendency to accumulate in biological systems pose substantial environmental and health threats. Consequently, striking a balance between maximizing product efficiency and minimizing environmental and health risks by tailoring the molecular structure of PFAS has become a pivotal challenge in the fields of environmental chemistry and sustainable development. To address this issue, a computational workflow was proposed for designing an environmentally friendly PFAS by incorporating deep learning (DL) and molecular generative models. The hybrid DL architecture MolHGT+ based on heterogeneous graph neural network with transformer-like attention was applied to predict the surface tension, bioaccumulation, and hepatotoxicity of the molecules. Through virtual screening of the PFAS master database using MolHGT+, the findings indicate that incorporating the siloxane group and betaine fragment can effectively decrease both the bioaccumulation and hepatotoxicity of PFAS while preserving low surface tension. In addition, molecular generative models were employed to create a structurally diverse pool of novel PFASs with the aforementioned hit molecules serving as the initial template structures. Overall, our study presents a promising AI-driven method for advancing the development of environmentally friendly PFAS.

Based on T.E.S.T toxicity prediction and machine learning to forecast toxicity dynamics in the photocatalytic degradation of tetracycline

The integration of photocatalysis and biological treatment provides an effective strategy for controlling antibiotic contamination, which requires precise monitoring of toxicity changes during the photocatalytic process. In this study, nanoscale TiO2 (P25) was employed to degrade tetracycline (TC) under full-spectrum irradiation, with O2 identified as a crucial reactant for the generation reactive oxygen species (ROS). The toxicity simulation results of the degradation intermediates were closely correlated with the predictions of T.E.S.T software. By analyzing the content of intermediates under different experimental conditions, we developed a machine learning model utilizing the random forest algorithm with a correlation coefficient of R2 = 0.878 and a mean absolute error of MAE = 0.02. The model can track the changes of photocatalytic intermediates, in combination with toxicity simulation, which facilitates the prediction of toxicity at different degradation stages, thus allowing selection of the optimal timing of biological treatment interventions.

Mapping global distributions of clay-size minerals via soil properties and machine learning techniques

Clay-size mineral is a vital ingredient of soil that influences various environment behaviors. It is crucial to establish a global distribution map of clay-size minerals to improve the recognition of environment variations. However, there is a huge gap of lacking some mineral contents in poorly accessible remote areas. In this work, machine learning (ML) approaches were conducted to predict the mineral contents and analyze their global abundance changes through the relationship between soil properties and mineral distributions. The average content of kaolinite, illite, smectite, vermiculite, chlorite, and feldspar were predicated to be 28.69 %, 22.30 %, 12.42 %, 5.43 %, 5.03 %, and 1.44 % respectively. Model interpretation showed that topsoil bulk density and drainage class were the most significant factors for predicting all six minerals. It could be seen from the feature importance analysis that bulk density notably reflected the distribution of 2:1 layered minerals more than that of 1:1 mineral. High drainage favored secondary minerals development, while low drainage was more benefited for primary minerals. Moreover, the content variation of different minerals aligned with the distribution of corresponding soil properties, which affirmed the accuracy of established models. This study proposed a new approach to predict mineral contents through soil properties, which filled a necessary step of understanding the geochemical cycles of soil-related processes.

NADPHnet: a novel strategy to predict compounds for regulation of NADPH metabolism via network-based methods

Identification of compounds to modulate NADPH metabolism is crucial for understanding complex diseases and developing effective therapies. However, the complex nature of NADPH metabolism poses challenges in achieving this goal. In this study, we proposed a novel strategy named NADPHnet to predict key proteins and drug-target interactions related to NADPH metabolism via network-based methods. Different from traditional approaches only focusing on one single protein, NADPHnet could screen compounds to modulate NADPH metabolism from a comprehensive view. Specifically, NADPHnet identified key proteins involved in regulation of NADPH metabolism using network-based methods, and characterized the impact of natural products on NADPH metabolism using a combined score, NADPH-Score. NADPHnet demonstrated a broader applicability domain and improved accuracy in the external validation set. This approach was further employed along with molecular docking to identify 27 compounds from a natural product library, 6 of which exhibited concentration-dependent changes of cellular NADPH level within 100 μM, with Oxyberberine showing promising effects even at 10 μM. Mechanistic and pathological analyses of Oxyberberine suggest potential novel mechanisms to affect diabetes and cancer. Overall, NADPHnet offers a promising method for prediction of NADPH metabolism modulation and advances drug discovery for complex diseases.

Quinolone Antibiotics Inhibit the Rice Photosynthesis by Targeting Photosystem II Center Protein: Generational Differences and Mechanistic Insights

Soil antibiotic pollution profoundly influences plant growth and photosynthetic performance, yet the main disturbed processes and the underlying mechanisms remain elusive. This study explored the photosynthetic toxicity of quinolone antibiotics across three generations on rice plants and clarified the mechanisms through experimental and computational studies. Marked variations across antibiotic generations were noted in their impact on rice photosynthesis with the level of inhibition intensifying from the second to the fourth generation. Omics analyses consistently targeted the light reaction phase of photosynthesis as the primary process impacted, emphasizing the particular vulnerability of photosystem II (PS II) to the antibiotic stress, as manifested by significant interruptions in the photon-mediated electron transport and O2 production. PS II center D2 protein (psbD) was identified as the primary target of the tested antibiotics, with the fourth-generation quinolones displaying the highest binding affinity to psbD. A predictive machine learning method was constructed to pinpoint antibiotic substructures that conferred enhanced affinity. As antibiotic generations evolve, the positive contribution of the carbonyl and carboxyl groups on the 4-quinolone core ring in the affinity interaction gradually intensified. This research illuminates the photosynthetic toxicities of antibiotics across generations, offering insights for the risk assessment of antibiotics and highlighting their potential threats to carbon fixation of agroecosystems.

CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals

Endocrine-disrupting chemicals (EDCs) pose significant environmental and health risks due to their potential to interfere with nuclear receptors (NRs), key regulators of physiological processes. Despite the evident risks, the majority of existing research narrows its focus on the interaction between compounds and the individual NR target, neglecting a comprehensive assessment across the entire NR family. In response, this study assembled a comprehensive human NR dataset, capturing 49,244 interactions between 35,467 unique compounds and 42 NRs. We introduced a cross-attention network framework, "CatNet", innovatively integrating compound and protein representations through cross-attention mechanisms. The results showed that CatNet model achieved excellent performance with an area under the receiver operating characteristic curve (AUCROC) = 0.916 on the test set, and exhibited reliable generalization on unseen compound-NR pairs. A distinguishing feature of our research is its capacity to expand to novel targets. Beyond its predictive accuracy, CatNet offers a valuable mechanistic perspective on compound-NR interactions through feature visualization. Augmenting the utility of our research, we have also developed a graphical user interface, empowering researchers to predict chemical binding to diverse NRs. Our model enables the prediction of human NR-related EDCs and shows the potential to identify EDCs related to other targets.

Screening of the Antagonistic Activity of Potential Bisphenol A Alternatives toward the Androgen Receptor Using Machine Learning and Molecular Dynamics Simulation

Over the past few decades, extensive research has indicated that exposure to bisphenol A (BPA) increases the health risks in humans. Toxicological studies have demonstrated that BPA can bind to the androgen receptor (AR), resulting in endocrine-disrupting effects. In recent investigations, many alternatives to BPA have been detected in various environmental media as major pollutants. However, related experimental evaluations of BPA alternatives have not been systematically implemented for the assessment of chemical safety and the effects of structural characteristics on the antagonistic activity of the AR. To promote the green development of BPA alternatives, high-throughput toxicological screening is fundamental for prioritizing chemical tests. Therefore, we proposed a hybrid deep learning architecture that combines molecular descriptors and molecular graphs to predict AR antagonistic activity. Compared to previous models, this hybrid architecture can extract substantial chemical information from various molecular representations to improve the model's generalization ability for BPA alternatives. Our predictions suggest that lignin-derivable bisguaiacols, as alternatives to BPA, are likely to be nonantagonist for AR compared to bisphenol analogues. Additionally, molecular dynamics (MD) simulations identified the dihydrotestosterone-bound pocket, rather than the surface, as the major binding site of bisphenol analogues. The conformational changes of key helix H12 from an agonistic to an antagonistic conformation can be evaluated qualitatively by accelerated MD simulations to explain the underlying mechanism. Overall, our computational study is helpful for toxicological screening of BPA alternatives and the design of environmentally friendly BPA alternatives.

Network-Based Methods and Their Applications in Drug Discovery

Drug discovery is time-consuming, expensive, and predominantly follows the "one drug → one target → one disease" paradigm. With the rapid development of systems biology and network pharmacology, a novel drug discovery paradigm, "multidrug → multitarget → multidisease", has emerged. This new holistic paradigm of drug discovery aligns well with the essence of networks, leading to the emergence of network-based methods in the field of drug discovery. In this Perspective, we initially introduce the concept and data sources of networks and highlight classical methodologies employed in network-based methods. Subsequently, we focus on the practical applications of network-based methods across various areas of drug discovery, such as target prediction, virtual screening, prediction of drug therapeutic effects or adverse drug events, and elucidation of molecular mechanisms. In addition, we provide representative web servers for researchers to use network-based methods in specific applications. Finally, we discuss several challenges of network-based methods and the directions for future development. In a word, network-based methods could serve as powerful tools to accelerate drug discovery.

mtADENet: A novel interpretable method integrating multiple types of network-based inference approaches for prediction of adverse drug events

Identification of adverse drug events (ADEs) is crucial to reduce human health risks and accelerate drug safety assessment. ADEs are mainly caused by unintended interactions with primary or additional targets (off-targets). In this study, we proposed a novel interpretable method named mtADENet, which integrates multiple types of network-based inference approaches for ADE prediction. Different from phenotype-based methods, mtADENet introduced computational target profiles predicted by network-based methods to bridge the gap between chemical structures and ADEs, and hence can not only predict ADEs for drugs and novel compounds within or outside the drug-ADE association network, but also provide insights for the elucidation of molecular mechanisms of the ADEs caused by drugs. We constructed a series of network-based prediction models for 23 ADE categories. These models achieved high AUC values ranging from 0.865 to 0.942 in 10-fold cross validation. The best model further showed high performance on four external validation sets, which outperformed two previous network-based methods. To show the practical value of mtADENet, we performed case studies on developmental neurotoxicity and cardio-oncology, and over 50 % of predicted ADEs and targets for drugs and novel compounds were validated by literature. Moreover, mtADENet is freely available at our web server named NetInfer (http://lmmd.ecust.edu.cn/netinfer/). In summary, mtADENet would be a powerful tool for ADE prediction and drug safety assessment in drug discovery and development.

Degradation of methotrexate by unactivated and solar-activated peroxymonosulfate in water: Moiety-specific reaction kinetics and transformation product-associated risks

Anticancer drugs have raised worldwide concern owing to their ubiquitous occurrence and ecological risks, necessitating the development of efficient removal strategies in water and wastewater treatment. Although peroxymonosulfate (PMS) is known to be a promising chemical in water decontamination, limited information is available regarding the removal efficiency of anticancer drugs by PMS and solar/PMS systems. This study first reports the moiety-specific reaction kinetics and mechanisms of methotrexate (MTX), an anticancer drug with widespread attention, by PMS (unactivated) and solar-activated PMS in water. It was found that MTX abatement by the direct PMS oxidation followed second-order kinetics, and the pH-dependent rate constants increased from 0.4 M-1 s-1 (pH 5.0) to 1.3 M-1 s-1 (pH 8.0), with a slight decrease to 1.1 M-1 s-1 at pH 9.0. The presence of chloride and bromide exerted no obvious influence on the removal of MTX by PMS. Furthermore, the chemical reactivity of MTX and its seven substructures with different reactive species was evaluated, and the degradation contributions of the reactive species involved were quantitatively analyzed in the solar/PMS system. The product analysis suggested similar reaction pathways of MTX by PMS and solar/PMS systems. The persistence, bioaccumulation, and toxicity of the transformation products were investigated, indicating treatment-driven risks. Notably, MTX can be removed efficiently from both municipal and hospital wastewater effluents by the solar/PMS system, suggesting its great potential in wastewater treatment applications. Overall, this study systematically evaluated the elimination of MTX by the unactivated PMS and solar/PMS treatment processes in water. The obtained findings may have implications for the mechanistic understanding and development of PMS-based processes for the degradation of such micropollutants in wastewater.

Elucidating sulfate radical-induced oxidizing mechanisms of solid-phase pharmaceuticals: Comparison with liquid-phase reactions

As a class of organic micropollutants of global concern, pharmaceuticals have prevalent distributions in the aqueous environment (e.g., groundwater and surface water) and solid matrices (e.g., soil, sediments, and dried sludge). Their contamination levels have been further aggravated by the annually increased production of expired drugs as emerging harmful wastes worldwide. Sulfate radicals (SO4•-)-based oxidation has attracted increasing attention for abating pharmaceuticals in the environment, whereas the transformation mechanisms of solid-phase pharmaceuticals remain unknown thus far. This investigation presented for the first time that SO4•-, individually produced by mechanical force-activated and heat-activated persulfate treatments, could effectively oxidize three model pharmaceuticals (i.e., methotrexate, sitagliptin, and salbutamol) in both solid and liquid phases. The high-resolution mass spectrometric analysis suggested their distinct transformation products formed by different phases of SO4•- oxidation. Accordingly, the SO4•--mediated mechanistic differences between the solid-phase and liquid-phase pharmaceuticals were proposed. It is noteworthy that the products from both systems were predicted with the remaining persistence, bioaccumulation, and multi-endpoint toxicity. Therefore, some post-treatment strategies need to be considered during practical applications of SO4•--based technologies in remediating different phases of micropollutants. This work has environmental implications for understanding the comparative transformation mechanisms of pharmaceuticals by SO4•- oxidation in remediating the contaminated solid and aqueous matrices.