Predicting the effect of chemicals on fruit using graph neural networks.

The neural network method is a type of machine learning that has made significant advances over the past few years in a variety of fields, particularly text, speech, images, videos, etc. In areas where data is unstructured, traditional machine learning has not been able to surpass the 'glass ceiling'; therefore, researchers have turned to neural networks as auxiliary tools to achieve significant breakthroughs or develop new research methods. An array of computational chemistry challenges can be addressed using neural networks, including virtual screening, quantitative structure-activity relationships, protein structure prediction, materials design, quantum chemistry, and property prediction, among others. This paper proposes a strategy for predicting the chemical properties of fruits by using graph neural networks, and it aims to provide some guidance to researchers and streamline the identification process.

Physicochemical properties as a function of lomefloxacin biological activity.

OBJECTIVE: Aim: The goal is to discover QSAR of Lomefloxacin as antibacterial activity. PATIENTS AND METHODS: Materials and Methods: A number of lomefloxacins analogs activities were studied by program Windows Chem SW. The analogues were obtained and energy minimization was carried out through Molecular Modeling Program, the calculations were performed using General Atomic and Molecular Electronic Structure System (GAMESS) software. RESULTS: Results: There were six descriptions (N-quinoline more (-) ev charge, Kinetic Energy, Potential Energy, Log p, Log S, F6 charge) results have highly compatible of physicochemical properties with lomefloxacin analogs activities. It can be used to estimate the activities depending on QSAR equation of lomefloxacin analogs. CONCLUSION: Conclusions: The parameters used for calculation were depending on the quantum chemical was employed in deriving from computational study of properties and can used to predict the activities of certain analogs of Lomefloxacins as antibacterial compounds.

Organophosphate toxicity patterns: A new approach for assessing organophosphate neurotoxicity.

Organophosphorus compounds or organophosphates (OPs) are widely used as flame retardants, plasticizers, lubricants and pesticides. This contributes to their ubiquitous presence in the environment and to the risk of human exposure. The persistence of OPs and their bioaccumulative characteristics raise serious concerns regarding environmental and human health impacts. To address the need for safer OPs, this study uses a New Approach Method (NAM) to analyze the neurotoxicity pattern of 42 OPs. The NAM consists of a 4-step process that combines computational modeling with in vitro and in vivo experimental studies. Using spherical harmonic-based cluster analysis, the OPs were grouped into four main clusters. Experimental data and quantitative structure-activity relationships (QSARs) analysis were used in conjunction to provide information on the neurotoxicity profile of each group. Results showed that one of the identified clusters had a favorable safety profile, which may help identify safer OPs for industrial applications. In addition, the 3D-computational analysis of each cluster was used to identify meta-molecules with specific 3D features. Toxicity was found to correspond to the level of phosphate surface accessibility. Substances with conformations that minimize phosphate surface accessibility caused less neurotoxic effect. This multi-assay NAM could be used as a guide for the classification of OP toxicity, helping to minimize the health and environmental impacts of OPs, and providing rapid support to the chemical regulators, whilst reducing reliance on animal testing.

The coefficient of conformism of a correlative prediction (CCCP): Building up reliable nano-QSPRs/QSARs for endpoints of nanoparticles in different experimental conditions encoded via quasi-SMILES.

Simulation of the physicochemical and biochemical behavior of nanomaterials has its own specifics. However, the main goal of modeling for both traditional substances and nanomaterials is the same. This is an ecologic risk assessment. The universal indicator of toxicity is the n-octanol/water partition coefficient. Mutagenicity indicates the possibility of future undesirable environmental effects, possibly greater than toxicity. Models have been proposed for the octanol/water distribution coefficient of gold nanoparticles and the mutagenicity of silver nanoparticles. Unlike the previous studies, here the models are built using an updated scheme, which includes two improvements. Firstly, the computing involves a new criterion for prediction potential, the so-called coefficient of conformism of a correlative prediction (CCCP); secondly, the Las Vegas algorithm is used to select the potentially most promising models from a group of models obtained by the Monte Carlo algorithm. Apparently, CCCP is a measure of the predictive potential (not only correlation). This can give an advantage in developing a model in comparison to using the classic determination coefficient. Likely, CCCP can be more informative than the classical determination coefficient. The Las Vegas algorithm is able to improve the model obtained by the Monte Carlo method.

Bio-QSARs 2.0: Unlocking a new level of predictive power for machine learning-based ecotoxicity predictions by exploiting chemical and biological information.

Practical, legal, and ethical reasons necessitate the development of methods to replace animal experiments. Computational techniques to acquire information that traditionally relied on animal testing are considered a crucial pillar among these so-called new approach methodologies. In this light, we recently introduced the Bio-QSAR concept for multispecies aquatic toxicity regression tasks. These machine learning models, trained on both chemical and biological information, are capable of both cross-chemical and cross-species predictions. Here, we significantly extend these models' applicability. This was realized by increasing the quantity of training data by a factor of approximately 20, accomplished by considering both additional chemicals and aquatic organisms. Additionally, variable test durations and associated random effects were accommodated by employing a machine learning algorithm that combines tree-boosting with mixed-effects modeling (i.e., Gaussian Process Boosting). We also explored various biological descriptors including Dynamic Energy Budget model parameters, taxonomic distances, as well as genus-specific traits and investigated the inclusion of mode-of-action information. Through these efforts, we developed Bio-QSARs for fish and aquatic invertebrates with exceptional predictive power (R squared of up to 0.92 on independent test sets). Moreover, we made considerable strides to make models applicable for a range of use cases in environmental risk assessment as well as research and development of chemicals. Models were made fully explainable by implementing an algorithmic multicollinearity correction combined with SHapley Additive exPlanations. Furthermore, we devised novel approaches for applicability domain construction that take feature importance into account. We are hence confident these models, which are available via open access, will make a significant contribution towards the implementation of new approach methodologies and ultimately have the potential to support "Green Chemistry" and "Green Toxicology".

Targeting bacterial growth in biofilm conditions: rational design of novel inhibitors to mitigate clinical and food contamination using QSAR.

Antimicrobial resistance (AMR) is a pressing global issue exacerbated by the abuse of antibiotics and the formation of bacterial biofilms, which cause up to 80% of human bacterial infections. This study presents a computational strategy to address AMR by developing three novel quantitative structure-activity relationship (QSAR) models based on molecular topology to identify potential anti-biofilm and antibacterial agents. The models aim to determine the chemo-topological pattern of Gram (+) antibacterial, Gram (-) antibacterial, and biofilm formation inhibition activity. The models were applied to the virtual screening of a commercial chemical database, resulting in the selection of 58 compounds. Subsequent in vitro assays showed that three of these compounds exhibited the most promising antibacterial activity, with potential applications in enhancing food and medical device safety.

Predicting Elimination of Small-Molecule Drug Half-Life in Pharmacokinetics Using Ensemble and Consensus Machine Learning Methods.

Half-life is a significant pharmacokinetic parameter included in the excretion phase of absorption, distribution, metabolism, and excretion. It is one of the key factors for the successful marketing of drug candidates. Therefore, predicting half-life is of great significance in drug design. In this study, we employed eXtreme Gradient Boosting (XGboost), randomForest (RF), gradient boosting machine (GBM), and supporting vector machine (SVM) to build quantitative structure-activity relationship (QSAR) models on 3512 compounds and evaluated model performance by using root-mean-square error (RMSE), R2, and mean absolute error (MAE) metrics and interpreted features by SHapley Additive exPlanation (SHAP). Furthermore, we developed consensus models through integrating four individual models and validated their performance using a Y-randomization test and applicability domain analysis. Finally, matched molecular pair analysis was used to extract the transformation rules. Our results revealed that XGboost outperformed other individual models (RMSE = 0.176, R2 = 0.845, MAE = 0.141). The consensus model integrating all four models continued to enhance prediction performance (RMSE = 0.172, R2 = 0.856, MAE = 0.138). We evaluated the reliability, robustness, and generalization ability via Y-randomization test and applicability domain analysis. Meanwhile, we utilized SHAP to interpret features and employed matched molecular pair analysis to extract chemical transformation rules that provide suggestions for optimizing drug structure. In conclusion, we believe that the consensus model developed in this study serve as a reliable tool to evaluate half-life in drug discovery, and the chemical transformation rules concluded in this study could provide valuable suggestions in drug discovery.

Pharmacokinetics Profiler (PhaKinPro): Model Development, Validation, and Implementation as a Web Tool for Triaging Compounds with Undesired Pharmacokinetics Profiles.

Computational models that predict pharmacokinetic properties are critical to deprioritize drug candidates that emerge as hits in high-throughput screening campaigns. We collected, curated, and integrated a database of compounds tested in 12 major end points comprising over 10,000 unique molecules. We then employed these data to build and validate binary quantitative structure-activity relationship (QSAR) models. All trained models achieved a correct classification rate above 0.60 and a positive predictive value above 0.50. To illustrate their utility in drug discovery, we used these models to predict the pharmacokinetic properties for drugs in the NCATS Inxight Drugs database. In addition, we employed the developed models to predict the pharmacokinetic properties of all compounds in the DrugBank. All models described in this paper have been integrated and made publicly available via the PhaKinPro Web-portal that can be accessed at https://phakinpro.mml.unc.edu/.

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors.

Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

Novel 5-Sulfonyl-1,3,4-thiadiazole-Substituted Flavonoids as Potential Bactericides and Fungicides: Design, Synthesis, Three-Dimensional Quantitative Structure-Activity Relationship Studies.

Flavonoids, ubiquitous natural products, provide sources for drug discovery owing to their structural diversity, broad-spectrum pharmacological activity, and excellent environmental compatibility. To develop antibacterial and antifungal agents with novel mechanisms of action and innovative structures, a series of novel 5-sulfonyl-1,3,4-thiadiazole-substituted flavonoids were designed and synthesized, and their biological activities against seven agriculturally common phytopathogenic microorganisms were evaluated. The results of the antimicrobial bioassay showed that most of the target compounds displayed excellent inhibitory effects against Xanthomonas oryzae, Rhizoctonia solani, and Colletotrichum orbiculare. Compounds 1, 3, 7, 9, 13, and 14 exhibited remarkable antibacterial activity against X. oryzae pv. oryzae with EC50 values below 10 µg/mL, which were superior to bismerthiazol (70.89 µg/mL). Compound 2 (EC50 = 0.41 µg/mL) displayed the most effective inhibitory potency against R. solani in vivo, comparable protective effects with the positive control carbendizam. Preliminary mechanistic studies indicated that compound 2 induced disordered entanglement of hyphae, shrinkage of hyphal surfaces, extravasation of cellular contents, and vacuole swelling and rupture, which disrupted normal hyphal growth. Subsequently, compounds 35-53 with good antifungal activity were designed and synthesized based on reliable three-dimensional quantitative structure-activity relationship (3D-QSAR) models. Compound 49 showed high efficacy and superior antifungal activity against R. solani, with an EC50 value of 0.28 µg/mL and a half-maximal effective concentration of 0.46 µg/mL.

3D-QSAR and Molecular Dynamics Study of Isoxazole Derivatives to Identify the Structural Requirements for Farnesoid X Receptor (FXR) Agonists.

The farnesoid X receptor (FXR) has been recognized as a potential drug target for the treatment of non-alcoholic fatty liver disease (NAFLD). FXR agonists benefit NAFLD by modulating bile acid synthesis and transport, lipid metabolism, inflammation, and fibrosis pathways. However, there are still great challenges involved in developing safe and effective FXR agonists. To investigate the critical factors contributing to their activity on the FXR, 3D-QSAR molecular modeling was applied to a series of isoxazole derivatives, using comparative molecular field analysis (CoMFA (q2 = 0.664, r2 = 0.960, r2pred = 0.872)) and comparative molecular similarity indices analysis (CoMSIA (q2 = 0.706, r2 = 0.969, r2pred = 0.866)) models, which demonstrated strong predictive ability in our study. The contour maps generated from molecular modeling showed that the presence of hydrophobicity at the R2 group and electronegativity group at the R3 group in these compounds is crucial to their agonistic activity. A molecular dynamics (MD) simulation was carried out to further understand the binding modes and interactions between the FXR and its agonists in preclinical or clinical studies. The conformational motions of loops L: H1/H2 and L: H5/H6 in FXR-ligand binding domain (LBD) were crucial to the protein stability and agonistic activity of ligands. Hydrophobic interactions were formed between residues (such as LEU287, MET290, ALA291, HIS294, and VAL297) in helix H3 and ligands. In particular, our study found that residue ARG331 participated in salt bridges, and HIS447 participated in salt bridges and hydrogen bonds with ligands; these interactions were significant to protein-ligand binding. Eight new potent FXR agonists were designed according to our results, and their activities were predicted to be better than that of the first synthetic FXR agonist, GW4064.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence.

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

Discovery of Trisubstituted N-Phenylpyrazole Containing Diamides with Improved Insecticidal Activity.

To increase the structural diversity of insecticides and meet the needs of effective integrated insect management, the structure of chlorantraniliprole was modified based on a previously established three-dimensional quantitative structure-activity relationship (3D-QSAR) model. The pyridinyl moiety in the structure of chlorantraniliprole was replaced with a 4-fluorophenyl group. Further modifications of this 4-fluorophenyl group by introducing a halogen atom at position 2 and an electron-withdrawing group (e.g., iodine, cyano, and trifluoromethyl) at position 5 led to 34 compounds with good insecticidal efficacy against Mythimna separata, Plutella xylostella, and Spodoptera frugiperda. Among them, compound IV f against M. separata showed potency comparable to that of chlorantraniliprole. IV p against P. xylostella displayed a 4.5 times higher potency than chlorantraniliprole. In addition, IV d and chlorantraniliprole exhibited comparable potencies against S. frugiperda. Transcriptome analysis showed that the molecular target of compound IV f is the ryanodine receptor. Molecular docking was further performed to verify the mode of action and insecticidal activity against resistant P. xylostella.

The novel angiotensin-I-converting enzyme inhibitory peptides from Scomber japonicus muscle protein hydrolysates: QSAR-based screening, molecular docking, kinetic and stability studies.

Food-derived angiotensin-converting enzyme-inhibitory (ACE-I) peptides have attracted extensive attention. Herein, the ACE-I peptides from Scomber japonicus muscle hydrolysates were screened, and their mechanisms of action and inhibition stability were explored. The quantitative structure-activity relationship (QSAR) model based on 5z-scale metrics was developed to rapidly screen for ACE-I peptides. Two novel potential ACE-I peptides (LTPFT, PLITT) were predicted through this model coupled with in silico screening, of which PLITT had the highest activity (IC50: 48.73 ± 7.59 µM). PLITT inhibited ACE activity with a mixture of non-competitive and competitive mechanisms, and this inhibition mainly contributed to the hydrogen bonding based on molecular docking study. PLITT is stable under high temperatures, pH, glucose, and NaCl. The zinc ions (Zn2+) and copper ions (Cu2+) enhanced ACE-I activity. The study suggests that the QSAR model is effective in rapidly screening for ACE-I inhibitors, and PLITT can be supplemented in foods to lower blood pressure.

QSPR for the prediction of critical micelle concentration of different classes of surfactants using machine learning algorithms.

The determination of the critical micelle concentration (CMC) is a crucial factor when evaluating surfactants, making it an essential tool in studying the properties of surfactants in various industrial fields. In this present research, we assembled a comprehensive set of 593 different classes of surfactants including, anionic, cationic, nonionic, zwitterionic, and Gemini surfactants to establish a link between their molecular structure and the negative logarithmic value of critical micelle concentration (pCMC) utilizing quantitative structure-property relationship (QSPR) methodologies. Statistical analysis revealed that a set of 14 significant Mordred descriptors (SlogP, GATS6d, nAcid, GATS8dv, GATS4dv, PEOE_VSA11, GATS8d, ATS0p, GATS1d, MATS5p, GATS3d, NdssC, GATS6dv and EState_VSA4), along with temperature, served as appropriate inputs. Different machine learning methods, such as multiple linear regression (MLR), random forest regression (RFR), artificial neural network (ANN), and support vector regression (SVM), were employed in this study to build QSPR models. According to the statistical coefficients of QSPR models, SVR with Dragonfly hyperparameter optimization (SVR-DA) was the most accurate in predicting pCMC values, achieving (R2 = 0.9740, Q2 = 0.9739, r‾m2 = 0.9627, and Δrm2 = 0.0244) for the entire dataset.

Environmental impact of PFAS: Filling data gaps using theoretical quantum chemistry and QSPR modeling.

Per- and polyfluorinated alkyl substances (PFAS), known for their widespread environmental presence and slow degradation, pose significant concerns. Of the approximately 10,000 known PFAS, only a few have undergone comprehensive testing, resulting in limited experimental data. In this study, we employed a combination of physics-based methods and data-driven models to address gaps in PFAS bioaccumulation potential. Using the COnductor-like Screening MOdel for Realistic Solvents (COSMO-RS) method, we predicted n-octanol/water partition coefficients (logKOW), crucial for PFAS bioaccumulation. Our developed Quantitative Structure-Property Relationship (QSPR) model exhibited high accuracy (R2 = 0.95, RMSEC = 0.75) and strong predictive ability (Q2LOO = 0.93, RMSECV = 0.83). Leveraging the extensive NORMAN, we predicted logKOW for over 4,000 compounds, identifying 244 outliers out of 4519. Further categorizing the database into eight Organisation for Economic Co-operation and Development (OECD) categories, we confirmed fluorine atoms role in enhanced bioaccumulation. Utilizing predicted logKOW, water solubility logSW, and vapor pressure logVP values, we calculated additional physicochemical properties that are responsible for the transport and dispersion of PFAS in the environment. Parameters such as Henry's Law (kH), air-water partition coefficient (KAW), octanol-air coefficient (KOA), and soil adsorption coefficient (KOC) exhibited favorable correlations with literature data (R2 > 0.66). Our study successfully filled data gaps, contributing to the understanding of ubiquitous PFAS in the environment and estimating missing physicochemical data for these compounds.

Identification of the single and combined acute toxicity of Cr and Ni with Heterocypris sp. and the quantitative structure-activity relationship (QSAR) model.

Mining wastewater with heavy metals poses a serious threat to the ecological environment. However, the acute single and combined ecological effects of heavy metals, such as chromium (Cr) and nickel (Ni), on freshwater ostracods, and the development of relevant prediction models, remain poorly understood. In this study, Heterocypris sp. was chosen to investigate the single and combined acute toxicity of Cr and Ni. Then, the quantitative structure-activity relationship (QSAR) model was used to predict the combined toxicity of Cr and Ni. The single acute toxicity experiments revealed high toxicity for both Cr and Ni. In addition, Cr exhibited greater toxicity compared to Ni, as evidenced by its lower 96-hour half-lethal concentration (LC50) of 1.07 mg/L compared to 4.7 mg/L for Ni. Furthermore, the combined acute toxicity experiments showed that the toxicity of Cr-Ni was higher than Ni but lower than Cr. Compared with the concentration addition (CA) and independent action (IA) models, the predicted results of the QSAR model were more consistent with the experimental results for the Cr-Ni combined acute toxicity. So, the high accuracy of QSAR model identified its feasibility to predict the toxicity of heavy metal pollutants in mining wastewater.

Per- and polyfluoroalkyl substances inhibit human and rat 17ß-hydroxysteroid dehydrogenase 1: Quantitative structure-activity relationship and molecular docking analysis.

Per- and polyfluoroalkyl (PFAS) substances are enduring industrial materials. 17ß-Hydroxysteroid dehydrogenase isoform 1 (17ß-HSD1) is an estrogen metabolizing enzyme, which transforms estrone into estradiol in human placenta and rat ovary. Whether PFAS inhibit 17ß-HSD1 and what the structure-activity relationship (SAR) remains unexplored. We screened 18 PFAS for inhibiting human and rat 17ß-HSD1 in microsomes and studied their SAR and mode of action(MOA). Of the 11 perfluorocarboxylic acids (PFCAs), C8-C14 PFCAs at a concentration of 100 µM substantially inhibited human 17ß-HSD1, with order of C11 (half-maximal inhibition concentration, IC50, 8.94 µM) > C10 (10.52 µM) > C12 (14.90 µM) > C13 (30.97 µM) > C9 (43.20 µM) > C14 (44.83 µM) > C8 (73.38 µM) > others. Of the 7 per- and poly-fluorosulfonic acids (PFSAs), the potency was C8S (IC50, 14.93 µM) > C7S (80.70 µM) > C6S (177.80 µM) > others. Of the PFCAs, C8-C14 PFCAs at 100 µM markedly reduced rat 17ß-HSD1 activity, with order of C11 (IC50, 9.11 µM) > C12 (14.30 µM) > C10 (18.24 µM) > C13 (25.61 µM) > C9 (67.96 µM) > C8 (204.39 µM) > others. Of the PFSAs, the potency was C8S (IC50, 37.19 µM) > C7S (49.38 µM) > others. In contrast to PFOS (C6S), the partially fluorinated compound 6:2 FTS with an equivalent number of carbon atoms demonstrated no inhibition of human and rat 17ß-HSD1 activity at a concentration of 100 µM. The inhibition of human and rat enzymes by PFAS followed a V-shaped trend from C4 to C14, with a nadir at C11. Moreover, human 17ß-HSD1 was more sensitive than rat enzyme. PFAS inhibited human and rat 17ß-HSD1 in a mixed mode. Docking analysis revealed that they bind to the NADPH and steroid binding site of both 17ß-HSD1 enzymes. The 3D quantitative SAR (3D-QSAR) showed that hydrophobic region, hydrogen bond acceptor and donor are key factors in binding to 17ß-HSD1 active sites. In conclusion, PFAS exhibit inhibitory effects on human and rat 17ß-HSD1 depending on factors such as carbon chain length, degree of fluorination, and the presence of carboxylic acid or sulfonic acid groups, with a notable V-shaped shift observed at C11.

Detrimental impacts and QSAR baseline toxicity assessment of Japanese medaka embryos exposed to methylparaben and its halogenated byproducts.

Despite the theoretical risk of forming halogenated methylparabens (halo-MePs) during water chlorination in the absence or presence of bromide ions, there remains a lack of in vivo toxicological assessments on vertebrate organisms for halo-MePs. This research addresses these gaps by investigating the lethal (assessed by embryo coagulation) or sub-lethal (assessed by hatching success/heartbeat rate) toxicity and teratogenicity (assessed by deformity rate) of MeP and its mono- and di-halogen derivatives (Cl- or Br-) using Japanese medaka embryos. In assessing selected apical endpoints to discern patterns in physiological or biochemical alterations, heightened toxic impacts were observed for halo-MePs compared to MeP. These include a higher incidence of embryo coagulation (4-36 fold), heartbeat rate decrement (11-36 fold), deformity rate increment (32-223 fold), hatching success decrement (11-59 fold), and an increase in Reactive Oxygen Species (ROS) level (1.2-7.4 fold)/Catalase (CAT) activity (1.7-2.8 fold). Experimentally determined LC50 values are correlated and predicted using a Quantitative Structure Activity Relationship (QSAR) based on the speciation-corrected liposome-water distribution ratio (Dlipw, pH 7.5). The QSAR baseline toxicity aligns well with (sub)lethal toxicity and teratogenicity, as evidenced by toxic ratio (TR) analysis showing TR < 10 for MeP exposure in all cases, while significant specific or reactive toxicity was found for halo-MeP exposure, with TR > 10 observed (excepting three values). Our extensive findings contribute novel insights into the intricate interplay of embryonic toxicity during the early-life-stage of Japanese medaka, with a specific focus on highlighting the potential hazards associated with halo-MePs compared to the parent compound MeP.

Risk mitigation strategy and mechanism analysis of neonicotinoid pesticides on earthworms.

Neonicotinoid insecticides (NNIs) are a new class of widely used insecticides with certain risks to non-target organisms, like earthworms. The gray correlation method was used to calculate the comprehensive risk effect value of acute toxicity (LC50) and bioaccumulation (logKow) of NNIs on earthworms. A comprehensive effects three-dimensional quantitative structure-activity relationship (3D-QSAR) model was constructed, using NNIs molecular structures and the comprehensive effect value as the independent and dependent variables, respectively. One of the representatives guadipyr (GUA) was selected as the template molecule for the molecular design and modification. A total of 63 NNIs alternatives were designed with a reduced comprehensive value higher than 10%, and as high as 42%. After screening, 15 NNIs alternatives were screened with decreased acute toxicity to earthworms, bioaccumulation effects and improved functional property. The calculated primary acute risk quotient of earthworms shows that the designed NNIs alternatives have lower earthworm risks (reduction of 70.48-99.99%). Results also found that the electronic, geometric and topological parameters of NNIs are the key descriptors that affect NNIs alternatives' toxicity. The number of hydrophobic interaction amino acid residues in NNIs molecules also contributes to the acute toxicity and the bioaccumulation of NNIs alternatives on earthworms. This study aims to design and screen functionally improved and environmentally friendly NNIs alternatives that have low risk to earthworms and provide theoretical methods and new ideas for the risk control and development of pesticides represented by NNIs.