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

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.

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.

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.

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.