Genetic algorithm multiple linear regression and machine learning-driven QSTR modeling for the acute toxicity of sterol biosynthesis inhibitor fungicides

Sterol Biosynthesis Inhibitors (SBIs) are a major class of fungicides used globally. Their widespread application in agriculture raises concerns about potential harm and toxicity to non-target organisms, including humans. To address these concerns, a quantitative structure-toxicity relationship (QSTR) modeling approach has been developed to assess the acute toxicity of 45 different SBIs. The genetic algorithm (GA) was used to identify key molecular descriptors influencing toxicity. These descriptors were then used to build robust QSTR models using multiple linear regression (MLR), support vector regression (SVR), and artificial neural network (ANN) algorithms. The Cross-validation, Y-randomization test, applicability domain methods, and external validation were carried out to evaluate the accuracy and validity of the generated models. The MLR model exhibited satisfactory predictive performance, with an R2 of 0.72. The SVR and ANN models obtained R2 values of 0.7 and 0.8, respectively. ANN model demonstrated superior performance compared to other models, achieving R2 cv and R2 test values of 0.74 and 0.7, respectively. The models passed both internal and external validation, indicating their robustness. These models offer a valuable tool for risk assessment, enabling the evaluation of potential hazards associated with future applications of SBIs.

Study on the toxicity prediction model ofacetolactate synthase inhibitor herbicides based on human serum albumin and superoxide dismutase binding information

Toxicity significantly influences the successful development of drugs. Based on the toxicity prediction method (carrier protein binding information-toxicity relationship) previously established by the our group, this paper introduces information on the interaction between pesticides and environmental markers (SOD) into the model for the first time, so that the toxicity prediction model can not only predict the toxicity of pesticides to humans and animals, but also predict the toxicity of pesticides to the environment. Firstly, the interaction of acetolactate synthase inhibitor herbicides (ALS inhibitor herbicides) with human serum albumin (HSA) and superoxide dismutase (SOD) was investigated systematically from theory combined with experiments by spectroscopy methods and molecular docking, and important fluorescence parameters were obtained. Then, the fluorescence parameters, pesticides acute toxicity LD50 and structural splitting information were used to construct predictive modeling of ALS inhibitor herbicides based on the carrier protein binding information (R2 = 0.977) and the predictive modeling of drug acute toxicity based on carrier protein binding information and conformational relationship (R2 = 0.991), which had effectively predicted pesticides toxicity in humans and animals. To predict potential environmental toxicity, the predictive modeling of drug acute toxicity based on superoxide dismutase binding information was established (R2 = 0.883) by ALS inhibitor herbicides-SOD binding information, which has a good predictive ability in the potential toxicity of pesticides to the environment. This study lays the foundation for developing low toxicity pesticides.

Predictionof Environmental FateandToxicityofInsecticidesUsing Multi-Target QSAR Approach

Ecotoxicological risk assessments form the foundation of regulatory decisions for industrial chemicals used in various sectors. In this study, a multi-target-QSAR model established by a backpropagation neural network trained with the Levenberg-Marquardt (LM) algorithm was used to construct a statistically robust and easily interpretable Mt-QSAR model with high external predictability for the simultaneous prediction of the environmental fate in form of octanol-water partition coefficient (LogP), (BCF) and acute oral toxicity in mammals and birds (LD50rat ) and (LD50bird ) for a wide range of chemical structural classes of insecticides. Principal component analysis was performed on descriptors selected by the SW-MLR method, and the selected PCs were used for constructing the SW-MLR-PCA-ANN model. The developed well-trained model (RMSE=0.83, MPE=0.004, CCC=0.82, IIC=0.78, R2 =0.69) was statistically robust as indicated by the external validation parameters (RMSE=0.93, MPE=0.008, CCC=0.77, IIC=0.68, R2 =0.61). The AD of the developed Mt-QSAR model was also defined to identify the most reliable predictions. Finally, the missing values in the dataset for the aforementioned targets were predicted using the constructed Mt-QSAR model. The proposed approach can be used for simultaneous prediction of the environmental fate of new insecticides, especially ones that haven't been tested yet.

Study of modeling and optimization for predicting the acute toxicity of carbamate pesticides using the binding information with carrier protein

To predict drug acute toxicity using the binding information with human serum albumin, our research group established a new method (Carrier protein binding information-toxicity relationship, CPBITR). Unfortunately, the previous model had too few data sets which may affect the accuracy and credibility of the model. In this paper, therefore, we measured the binding modes of three carbamate pesticides, Bendiocarb, Butocarboxim and Dioxacarb with human serum albumin (HSA) to supplement the previously modeled training set. Multispectral methods and molecular docking were used to study their binding modes. We built and optimized the previous models with the combined information of three different toxicity pesticides and HSA in order to find better prediction method. The results showed that Back-propagation Artificial Neural Network model has the best fitting effect among these models. In conclusion, the proposed model effectively improves the accuracy and credibility of the existing model. It results in significant predict drug acute toxicity using the binding information with carrier protein and contribute to drug development and research.

A new method for predicting the acute toxicity of carbamate pesticides based on the perspective of binding information with carrier protein

Toxicity is one of the most important factors limiting the success of new drug development. In this paper, we built a fast and convenient new method (Carrier protein binding information-toxicity relationship, CPBITR) for predicting drug acute toxicity based on the perspective of binding information with carrier protein. First, we studied the binding information between carbamate pesticides and human serum albumin (HSA) through various spectroscopic methods and molecular docking. Then a total of 16 models were established to clarify the relationship between binding information with HSA and drug toxicity. The results showed that the binding information was related to toxicity. Finally we obtained the effective toxicity prediction model for carbamate pesticides. And the "Platform for Predicting Drug Toxicity Based on the Information of Binding with Carrier Protein" was established with the Back-propagation neural network model. We proposed and proved that it was feasible to predict drug toxicity from this new perspective: binding with carrier protein. According to this new perspective, toxicity prediction model of other drugs can also be established. This new method has the advantages of convenience and fast, and can be used to screen out low-toxic drugs quickly in the early stage. It is helpful for drug research and development.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer

OBJECTIVE

By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes.

METHODS

From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models.

RESULTS

The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%.

CONCLUSION

Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.