With the increasing environmental presence of pharmaceuticals and industrial chemicals, rapid and reliable tools are urgently needed to assess potential risks to ecosystems and human health. However, experimental acute oral toxicity data for aniline compounds and their derivatives (AnCDs) remain scarce, limiting large-scale hazard evaluation. Quantitative structure-toxicity relationship (QSTR) modeling offers an efficient alternative to experimental testing and supports regulatory risk assessment. In this study, acute oral toxicity data (LD50) for 54 AnCDs in rat and 57 AnCDs in mouse were collected to develop predictive models in compliance with OECD guidelines. The resulting QSTR models exhibited satisfactory internal validation (R2 > 0.6 and QLOO2 > 0.6) and strong external predictive performance (Rtest2 > 0.7, QFn2 > 0.7, and CCCtest > 0.85). To further enhance predictive capability, a novel quantitative read-across structure-toxicity relationship (q-RASTR) approach integrating read-across information with 2D molecular descriptors was developed, yielding enhanced external predictivity. Mechanistic interpretation revealed that valence electron distribution, topological distances between atomic pairs, and key electronic and structural molecular features play critical roles in determining acute oral toxicity. This study represents a good application instance in which an integrated QSTR and q-RASTR new approach methodologies (NAMs) framework were applied to predict the acute oral toxicity of AnCDs in rodent models. Using atomic-centered fragments (ACFs), the validated models were applied to hundreds of untested external compounds, enabling toxicity prioritization and the identification of high-risk substances. This dual-model NAMs framework provides a valuable tool for chemical risk assessment, regulatory decision-making, and the rational design of safer chemicals and pharmaceuticals.

Pyrazole and pyrrolidine represent two classes of high-frequency nitrogen-containing “privileged scaffolds” in drug discovery and industrial chemicals; however, their potential acute toxicity poses significant challenges to clinical translation and industrial application. This study aims to establish systematic prediction models for the acute oral toxicity of the two scaffolds in rats and mice, strictly adhering to OECD guidelines. Based on experimental data for 552 compounds collected from PubChem, we calculated 2D molecular descriptors and systematically compared the performance of several modeling strategies, traditional 2D-QSTR, q-RASTR, Hybrid-ARKA, and ARKA-RASTR and six machine learning (ML) algorithms. The results demonstrated that the ARKA-RASTR framework yielded the most superior performance. It not only outperformed other methods in external validation but also effectively overcame the internal stability issues often associated with conventional q-RASTR approaches, with all validation metrics exceeding the most stringent international standards. In terms of mechanistic interpretation, this study innovatively employed the ARKA-RASTR model for intelligent physical mechanism analysis, dynamically linking variable importance to specific toxicity intensity ranges, thereby significantly enhancing model interpretability. Finally, the optimized models were applied to the virtual screening of 18,000 real world compounds lacking experimental values. Through applicability domain (AD) assessment, we provided prioritized lists of the top ten potential high- and low-toxicity candidates for each scaffold. We also developed an online web-based predictor: PPAOT (Pyrazole-Pyrrolidine-Acute Oral Toxicity), enabling one-stop toxicity prediction. By leveraging advanced data fusion strategies, this study offers robust tools and clear guidance for the early safety assessment and structural optimization of nitrogen-containing heterocyclic drugs and chemicals.

The integration of life cycle thinking into the earliest stages of drug discovery is imperative to mitigate the significant environmental footprint of pharmaceutical research and development. Conventional approaches, which rely heavily on resource-intensive synthesis and animal testing of numerous compounds, are inherently unsustainable. Herein, we demonstrate how the adoption of in silico strategies can serve as a powerful green chemistry lever to address this challenge. We present a high-throughput computational workflow employing interpretable quantitative structure-activity relationship (QSAR), quantitative read-across (q-RASAR), and machine learning (ML) models to accurately predict the acute oral toxicity of six types of drug scaffolds. The State-of-the-Art (SOTA) algorithms including several deep learning (DL) methods were also explored for global modeling of all scaffolds. The developed models achieved robust predictive performance, with external validation coefficients (R test 2) ranging from 0.7674 to 0.8980 across different scaffolds. This framework, rigorously developed and validated on a data set of 1150 compounds, was applied to virtually screen over 23,000 untested molecules. By enabling the prioritization of low-toxicity leads and the elimination of hazardous candidates prior to synthesis, our approach directly minimizes the demand for chemical reagents, solvents, and energy-intensive laboratory processes, thereby reducing waste generation and carbon emissions at the source. Concurrently, it substantially reduces the reliance on animal testing, aligning with the 3Rs principles. The analysis of structure-toxicity relationships further guides the de novo design of safer chemicals. Notably, a life cycle-oriented estimate indicates that this predictive strategy could potentially yield approximately $238 million in direct R&D cost avoidance and prevent 5000-10,000 t of CO2-equivalent emissions. This work establishes a paradigm shift toward a more sustainable drug discovery ecosystem, where computational prediction proactively circumvents the environmental and ethical costs embedded in the traditional research life cycle.

Engineered nanoparticles (ENPs), defined as nanoscale materials with at least one dimension between 1 and 100 nm, exhibit multifunctional and tunable physicochemical properties, that are at the center of several innovative fields. However, ENPs may induce a variety of biochemical reactions upon entry into organisms that could be a threat to human health. Therefore, a systematic evaluation of the toxicity of ENPs is essential. Quantitative structure–activity relationship (QSAR) is a practical in vitro modeling approach used to evaluate the toxicity of nanoparticles. In this study, we established the nanometric QSAR (Nano-QSAR) modelling based on cell membrane damage of ENPs to HepaRG cells. The toxicity data of ENPs and related 2D descriptor information were collected from the NanoCommons Knowledge Base. Periodic table descriptors of the elements were calculated using the Elemental Descriptor Calculator software. A multiple linear regression (MLR) model was constructed, and subsequently combined with read-across (RA) descriptors to establish the Nano-quantitative read-across structure–activity relationship (Nano-q-RASAR) model. Furthermore, machine learning (ML) algorithms were applied to optimize the predictive performance of the models. All models were validated according to the stringent OECD QSAR validation guidelines. Finally, a series of true external ENPs without experimental values were autonomously designed, and predicted using the best GB-Nano-QSAR model. Overall, this study can provide efficient and reliable predictions for the cell membrane damage of ENPs and a detailed theoretical explanation of their toxicity mechanism, which is of practical value for the toxicity assessment of ENPs.

In this study, density functional theory (DFT) calculations and molecular dynamics (MD) simulations were combined to investigate the interaction mechanism and drug-loading performance of chitosan (CS) as a carrier for carmustine (BCNU). DFT results indicate that adsorption of BCNU onto the CS surface is an exothermic process, with adsorption energies ranging from -8.69 to -26.41 kcal·mol-1. The complexes are primarily stabilized by hydrogen bonding and van der Waals (vdW) interactions. Among them, complex D exhibits the most negative adsorption energy and the greatest structural stability. FMO analyses show that the HOMO is mainly localized on CS, whereas the LUMO is localized on BCNU. The complexes exhibit a smaller Eg than isolated BCNU and show enhanced electrophilicity relative to isolated CS, which is beneficial for the chemical reactivity of the drug. QTAIM and NCI analyses confirm that medium strength hydrogen bonds and vdW forces dominate the non-covalent interactions between CS and BCNU. MD simulations reveal the assembly behavior and stability of the complexes at different drug loading ratios. Among the studied systems, the 8BCNU@8CS complex with a drug loading of 14.08 wt% exhibits the lowest mean square displacement and solvent accessible surface area, suggesting a more compact and stable encapsulation structure, which may be conducive to achieving sustained release based on simulation conditions. This work elucidates the atomic-scale interaction mechanisms between CS and BCNU and provides a theoretical basis for designing efficient, stable CS-based nanocarriers for BCNU delivery.

Flavonoids, a ubiquitous class of plant polyphenolic compounds, are known for their wide spectrum of biological functions, exhibiting diverse physiological functions and possessing significant application value in pharmaceuticals, foods, and nutraceuticals. Thus, it is of great significance to conduct the toxicity assessment. However, it is impossible to perform the experimental testing for a vast number of flavonoid chemcials. In this case, in silico methods are promising to address this problem. In strict accordance with OECD principles, this study established quantitative structure–toxicity relationship (QSTR) models for predicting flavonoid acute intraperitoneal toxicity in mice by employing GA-MLR methodology. Read-Across (RA) methodology was employed to estimate the toxicity based on structural similarity. RASTR descriptors were then calculated and pooled together with QSTR descriptors to establish a q-RASTR model. Importantly, intelligent consensus modelling was implemented as another method to enhance model's stability and predictive performance. Finally, the optimal QSTR model satisfied rigorous internal and external validation benchmarks, with R2 = 0.7887, Q_LOO^2 = 0.7327, Q_Fn^2 = 0.8521–0.8772, CCC_test = 0.9299. Based on three computational toxicology methods (QSTR, RA, and consensus modeling), the optimal model was consensus model 0 (average predictions). This model was then applied to predict the toxicity of a real external dataset lacking toxicity values. A comparative analysis with the predictions from an open-source VEGA tool was conducted to verify the applicability and predictive reliability of our model. This work offers mechanistic insights into the toxicological behavior of flavonoids and provides a rapid toxicity prediction tool for evaluating the safety of flavonoid-based chemicals.

Quinoline is a common pharmaceutical scaffold molecule known for its wide range of biological and pharmacological activities, including antimalarial, antitumor, and antibacterial effects. With the continuous discovery of new bioactivities, there is a growing demand for the design and development of novel quinoline-based drugs. However, drug development is time-consuming and costly, and traditional toxicity testing methods such as animal experiments are resource-intensive. In the context of the 3Rs (Replacement, Reduction, Refinement) principle in animal research, quantitative structure-activity/toxicity relationship (QSAR/QSTR) modeling has become one of the most widely used methods for drug design and validation. This study collected acute oral toxicity data in rat for 33 quinoline derivatives and established a transferable, reproducible and interpretable QSTR model based on 2D molecular descriptors, following the OECD principles for model validation. Both internal and external validations were performed. The results demonstrated that the model possesses high goodness-of-fit, strong robustness, and excellent predictive power. Applicability domain (AD) analysis showed that the model has a broad range of applicability. Furthermore, the model's predictive performance was verified and enhanced using quantitative read-across structure-toxicity relationship (q-RASTR) and machine learning (ML) methods. Mechanistic interpretation provided detailed insights into the relationships between molecular descriptors and toxicity. Notably, for the first time, the model was applied for a true external dataset consisting of 1995 molecules lacking experimental values, thereby validating its extrapolation ability. Overall, the developed QSTR model exhibits good stability and predictive performance, offering theoretical support for the risk assessment and rational design of quinoline-based compounds.

Previous studies have demonstrated that peri-conceptional inositol supplementation could effectively ameliorate the recurrence of neural tube defects (NTDs); though, the mechanism remains unclear. In the current investigation, we detected the myo-inositol (MI) levels in maternal plasma and embryonic tissues in a Chinese population with high prevalence of NTDs and found maternal MI deficiency increased NTD susceptibility in this area. Pregnant mice were randomly divided into 2 groups. The control group was treated with 0.9

Hypoxia-activated nitroaromatic compounds represent a promising strategy for developing tumor-targeted O6-alkylguanine-DNA alkyltransferase (AGT) inhibitors, crucial for enhancing alkylating agent efficacy in cancer therapy. In this study, we investigated the reduction mechanisms of two hypoxia-activated AGT inhibitors, 2-nitro-6-benzyloxypurine (2-NBP) and O6-(3-nitro)benzylguanine (3-NBG), mediated by xanthine oxidoreductase (XOR) using molecular docking, molecular dynamics (MD) simulations, and quantum mechanics/molecular mechanics (QM/MM) calculations. Docking revealed that both inhibitors bind XOR's active site, with their nitroaromatic rings stacking over the flavin's isoalloxazine ring, mainly through hydrophobic interactions. MD simulations revealed 2-NBP bound XOR more favorably than 3-NBG. QM/MM calculations elucidated the individual reduction mechanisms of both inhibitors, involving six 1e-/1H+ transfers. The key differences lay in the lower energy barriers for 2-NBP in the first, third, and sixth steps compared to 3-NBG. Combined with MD simulation results, the QM/MM computations demonstrated that 2-NBP was more readily reduced by XOR than 3-NBG, despite the rate-limiting step (the fifth 1e-/1H+ transfer) of 3-NBG reduction exhibiting slightly more favorable in kinetics and thermodynamics than 2-NBP. Additionally, a water molecule in the active site was found to facilitate the second 1e-/1H+ transfer, reducing the energy barrier significantly. This work provided a theoretical basis for designing tumor-targeted AGT inhibitors.

Bisphenol A (BPA) and di(2-ethylhexyl) phthalate (DEHP) are widely recognized environmental neurotoxicants implicated in Alzheimer's disease (AD). However, the shared and divergent mechanisms by which BPA and DEHP induce neurotoxicity in AD remain largely unexplored. In this study, we conducted the first systematic comparative analysis of overlapping and distinct neurotoxic pathways of BPA and DEHP by integrating network toxicology and in vitro experimental validation. Five shared core targets (MMP9, PPARG, MAPK14, BCL2, and BCL2L1) were identified from multiple databases. Experimental validation confirmed that BPA and DEHP significantly upregulated MMP9, PPARG, and phosphorylated MAPK14, while downregulating BCL2 and BCL2L1 at both transcriptional and protein levels. KEGG pathway enrichment revealed both shared and divergent pathways, with the lipid and atherosclerosis pathway emerging as a common AD-relevant pathway, whereas BPA and DEHP showed compound-specific involvement in PI3K-Akt, MAPK, and other signaling cascades. Molecular docking analysis further demonstrated favorable binding of both BPA and DEHP to the shared core targets. Collectively, this study provides novel mechanistic insights into both overlapping and distinct neurotoxic effects of BPA and DEHP in AD, offering a theoretical basis for future mechanistic research and environmental risk assessment.

Vinylcyclohexene (VCH) derivatives such as β-ionone and limonene as natural and industrial compounds are possibly present in aquatic environments, and their addition reaction with the disinfectant hypochlorous acid (HOCl) has raised concern due to the potential cytotoxicity of their products. However, little is known about the mechanism of the addition reaction of VCH in chlorination. In this study, reaction mechanisms, reactive sites, and substitution effects of VCH with HOCl were investigated using the quantum chemical computation method. The results indicate that the Cl+ of HOCl preferentially attacks the endocyclic double C1=C2 bond to generate the carbocation and chloronium ion intermediates for 1-VCH and 3/4-VCH, respectively, and then hydroxylation occurs to yield chlorohydrins. Regarding the substituent effect, it was found that in most case, the double bond closer to/farther away from the site substituted by the –CH3/−CHO group becomes preferable to the other double bond, mainly due to the electron-donating/withdrawing property of the –CH3/–CHO group. Based on the above results, the reactive sites of some VCH derivatives were proposed. The findings of this work are helpful for a better understanding of the reaction mechanisms of complex olefins with HOCl and predicting their reactive sites.

Aromatic polyamide thin film composite membranes have been extensively utilized in water treatment and desalination. However, unintended degradation of these membranes occurs when free chlorine is added to control biofouling. In this study, halogenation and degradation mechanisms of the polyamide monomer model compound, benzanilide (BA), during chlorination in the presence of halides Cl-, Br-, and I- were systematically investigated by a quantum chemical computational method. The results indicate that the reactive amide N and ortho/para-C in the anilide ring of BA undergo the respective concerted and classic SEAr mechanisms, and their reactivity depends on not only the chlorinating agents but also the speciation and tautomer of substrate BA, which could explain the experimental results observed at different pH. Comparing halogenation of BA by HOX (X = Cl/Br/I) at pH 7.0, notably, the kinetic reactivity order follows Br > I > Cl and I > Br > Cl for the amide N and C sites in the anilide ring, respectively, which can be explained by the "like-attracts-like" principle in the hard-soft-acid-base theory. The degradation of the membrane occurs in the hydrolysis of the chlorinated-BA with the C-N bond cleavage, in which N-halogenated products exhibit remarkably higher reactivity than BA and C-halogenated ones, and hydrolysis catalyzed by acid/base is definitely accelerated. Additionally, some modification strategies to improve the chlorine resistance of the polyamide membrane were proposed. The findings of this work are helpful in further understanding the degradation mechanisms and designing chlorine resistance of polyamide membrane during chlorination.

More and more halogenating agents have been identified in disinfection systems, however, it is full of challenge to quantify all their reactivity. In this study, 18 electrophiles including 11 halogenating...

Temozolomide (TMZ) is the only one oral first-line chemotherapeutic drug for glioblastoma treatment. However, O6-methylguanine-DNA methyltransferase (MGMT) can repair the lethal O6-methylguaine (O6-MeG) lesion produced by TMZ, thus imparting resistance to TMZ. Currently, the clinical utility of small molecule covalent MGMT inhibitors is limited by the occurrence of severe hematological toxicity. Therefore, developing new strategies for overcoming MGMT-mediated resistance is highly urgent. Here, we explored the feasibility that modulating Wnt/β-catenin signaling pathway in glioblastoma to inhibit MGMT expression to overcome TMZ resistance. From eight natural products or approved drugs with inhibitory effects on Wnt/β-catenin pathway, we found thymoquinone (TQ) completely suppressed MGMT expression in glioblastoma SF763 and SF767 cell lines within 24 h. As expected, TQ exhibited synergistic killing effects with TMZ in SF763 and SF767 cells, while in MGMT-negative SF126 cells only additive effect observed. Moreover, TQ remarkably enhanced the inhibition of TMZ on cell proliferation, clone formation, invasion and migration, and promoted cell apoptosis. In resistant SF763 mice tumor xenograft model, TQ significantly increased the suppression of TMZ on tumor growth, meanwhile maintaining good biosafety. Western blotting analysis indicated that TQ significantly inhibited the nuclear translocation of β-catenin and the expression of downstream proteins Cyclin D1 and MGMT. The addition of Wnt activator LiCl reversed the nuclear translocation of β-catenin and the expression of Cyclin D1 and MGMT induced by TQ. For the first time, our findings indicate that TQ can considerably increase the sensitivity of glioblastoma to TMZ by interfering Wnt/β-catenin pathway to downregulate MGMT expression.

Respiratory syncytial virus (RSV) is a leading cause of severe lower respiratory tract infections in infants, the elderly, and immunocompromised individuals worldwide. The pathogenic mechanism of RSV is closely linked to the membrane fusion process mediated by its fusion glycoprotein (F protein), which has consequently emerged as a critical target for developing anti-RSV therapeutics. At present, there is a lack of specific clinical treatments for RSV, and traditional drug discovery approaches are often time-consuming and expensive. In this context, quantitative structure–activity relationship (QSAR)–assisted drug design offers notable advantages. In this study, we collected a dataset consisting of 156 benzimidazole derivatives against F protein from publicly available sources. Transferable, reproducible, and interpretable 2D-QSAR inhibitory activity and cytotoxicity prediction models were constructed using Genetic Algorithm (GA) and Multiple Linear Regression (MLR). Following rigorous statistical validation, the best inhibitory activity model achieved R2 = 0.8740, Q_Loo^2 = 0.8272, R_test^2 = 0.8273, Q_Fn^2 = 0.8033–0.8492, CCCtest = 0.8782, MAEtest = 0.3014; the best cytotoxicity model was of R2 = 0.7573, Q_Loo^2 = 0.6926, R_test^2 = 0.7707, Q_Fn^2 = 0.7298–0.8656, CCCtest = 0.8639, MAEtest = 0.1342. The optimal inhibitory activity model was used to perform virtual screening on 912 benzimidazole derivatives retrieved from the PubChem, and identified 234 derivatives with better inhibitory activity than the reference JNJ-53718678. Among these, 152 derivatives were found to possess better docking binding energies than JNJ-53718678. Furthermore, we used the optimal toxicity model to assess their cytotoxicity, and identified 23 derivatives with predicted cytotoxicity lower than that of JNJ-53718678. Finally, through drug-likeness evaluation, ADMET analysis and molecular dynamics simulation, we obtained eight potential RSV inhibitors with higher inhibitory activity, lower cytotoxicity, and better pharmacokinetic properties compared to JNJ-53718678.

Temozolomide (TMZ) remains the primary oral chemotherapeutic agent for glioblastoma, but its efficacy is hampered by resistance mechanisms involving O6-methylguanine-DNA methyltransferase (MGMT). MGMT repairs the TMZ-induced lethal O6-methylguanine (O6-MeG) lesions, leading to treatment resistance. Current small molecule covalent MGMT inhibitors have limited clinical application due to severe hematological toxicity when used with TMZ. Therefore, alternative strategies to overcome MGMT-mediated resistance are critically needed. Targeting the Wnt/(3-catenin signaling pathway to suppress MGMT expression presents a promising approach. We synthesized and discovered that a novel Wnt inhibitor, DK419 (6-chloro-2-(trifluoromethyl)-N-(4-(trifluoromethyl)phenyl)-1H -benzimidazole-4-carboxamide), effectively suppressed MGMT expression within 12 h in TMZ-resistant SF763 and SF767 cell lines. DK419 demonstrated synergistic cytotoxic effects with TMZ in these cell lines, while only an additive effect was observed in MGMT-negative SF126 cells. Furthermore, DK419 significantly enhanced TMZ's inhibitory effects on cell proliferation, colony formation, invasion, and migration, while also promoting apoptosis. In a resistant mouse tumor xenograft model, DK419 significantly boosted TMZ's tumor growth suppression, maintaining good biosafety. Western blot analysis revealed that DK419 markedly inhibited the nuclear translocation of (3-catenin and decreased the expression of its downstream targets, Cyclin D1 and MGMT. The addition of the Wnt activator LiCl reversed DK419-induced effects on (3-catenin nuclear translocation and Cyclin D1 and MGMT expression. For the first time, our findings demonstrate that DK419 can significantly enhance glioblastoma sensitivity to TMZ by modulating the Wnt/(3-catenin pathway to downregulate MGMT expression.

Chloroethylnitrosoureas (CENUs) are important chemotherapies applied in the treatment of cancer. They exert anticancer activity by inducing DNA interstrand cross-links (ICLs) via the formation of two O-6-alkylguanine intermediates, O-6-chloroethylguanine (O-6-ClEtG) and N1,O-6-ethanoguanine (N1,O-6-EtG). However, O-6-alkylguanine-DNA alkyltransferase (AGT), a DNA-repair enzyme, can restore the O-6-alkylguanine damages and thereby obstruct the formation of ICLs (dG-dC cross-link). In this study, the inhibitory mechanism of ICL formation was investigated to elucidate the drug resistance of CENUs mediated by AGT in detail. Based on the structures of the substrate-enzyme complexes obtained from docking and MD simulations, two ONIOM (QM/MM) models with different sizes of the QM region were constructed. The model with a larger QM region, which included the substrate (O-6-ClEtG or N1,O-6-EtG), a water molecule, and five residues (Tyr114, Cys145, His146, Lys165, and Glu172) in the active pocket of AGT, accurately described the repairing reaction and generated the results coinciding with the experimental outcomes. The repair process consists of two sequential steps: hydrogen transfer to form a thiolate anion on Cys145 and alkyl transfer from the O-6 site of guanine (the rate-limiting step). The repair of N1,O-6-EtG was more favorable than that of O-6-ClEtG from both kinetics and thermodynamics aspects. Moreover, the comparison of the repairing process with the formation of dG-dC cross-link and the inhibition of AGT by O-6-benzylguanine (O-6-BG) showed that the presence of AGT could effectively interrupt the formation of ICLs leading to drug resistance, and the inhibition of AGT by O-6-BG that was energetically more favorable than the repair of O-6-ClEtG could not prevent the repair of N1,O-6-EtG. Therefore, it is necessary to completely eliminate AGT activity before CENUs medication to enhance the chemotherapeutic effectiveness. This work provides reasonable explanations for the supposed mechanism of AGT-mediated drug resistance of CENUs and will assist in the development of novel CENU chemotherapies and their medication strategies.

To identify toxicity drivers within poorly characterized high-molar-weight disinfection by-products (DBPs), relatively stable high-yield initial transformation products generated from aromatic amino acids and peptides and humic substances have drawn much attention. In this study, initial transformation products in chlorination of the indole moiety in tryptophan (Trp) are proposed and their formation mechanisms were investigated using a quantum chemical computational method. The results indicate that 3-Cl-Trp+ is initially formed after the Cl+ of HOCl attacks the indole moiety, and nucleophilic addition with nucleophilic agents (H2O and OCl-) is thermodynamically preferred over deprotonation to generate 2-X-3-Cl-indoline moiety (X = OH and OCl), which is in contrast to indole. Over 25 types of initial transformation products are proposed from the 2-X-3-Cl-indoline moiety and two ring opening pathways were found at N1-C2 and C2-C3 bonds. Significantly, most structures of initial transformation products proposed based on experimental detection m/z values were confirmed using quantum chemical calculations and some new products are proposed in this work. The results are helpful to expand our understanding of the intrinsic reactivity of aromatic ring towards chlorination by hypochlorous acid. Initial transformation products and their formation mechanisms in the chlorination of the indole moiety in tryptophan (Trp) are investigated using a quantum chemical computational method.

Protein kinase CK2 is a validated target for cancer therapy. Many natural products have shown inhibitory activity against CK2 as potential anti-cancer drug candidates. A compatible quantitative structure-activity relationship (QSAR) model of natural products is necessary to identify the structural determinants related to their biological activities and provides valuable clues for the discovery of natural leads as anticancer drugs. In this study, genetic algorithm (GA) and multiple linear regression (MLR) methods, combined with preferred molecular descriptors, were employed to build QSAR models of CK2 natural product inhibitors. The best model, composed of eight molecular descriptors, yielded Q2Loo = 0.7914 and R2 = 0.8220 for the training set and Q2ext = 0.7921 and R2ext = 0.7998 for the test set, indicating the model’s robust reliability and high predictability. As a proof of concept, a true external test set, distinct from the training and test sets, was synthesized and tested in vitro to verify the predictive ability of this model. The predicted pIC50 values of 13 compounds showed less than 30

Polycyclic aromatic hydrocarbons (PAHs) represent a common group of environmental pollutants that endanger various aquatic organisms via various pathways. To better prioritize the ecotoxicological hazard of PAHs to aquatic environment, we used 2D descriptors-based quantitative structure-toxicity relationship (QSTR) to assess the toxicity of PAHs toward six aquatic model organisms spanning three trophic levels. According to strict OECD guideline, six easily interpretable, transferable and reproducible 2D-QSTR models were constructed with high robustness and reliability. A mechanistic interpretation unveiled the key structural factors primarily responsible for controlling the aquatic ecotoxicity of PAHs. Furthermore, quantitative read-across and different machine learning approaches were employed to validate and optimize the modelling approach. Importantly, the optimum QSTR models were further applied for predicting the ecotoxicity of hundreds of untested/unknown PAHs gathered from Pesticide Properties Database (PPDB). Especially, we provided a priority list in terms of the toxicity of unknown PAHs to six aquatic species, along with the corresponding mechanistic interpretation. In summary, the models can serve as valuable tools for aquatic risk assessment and prioritization of untested or completely new PAHs chemicals, providing essential guidance for formulating regulatory policies.