Gene Expression Programming Algorithm Research Articles

Land subsidence prediction in coal mining using machine learning models and optimization techniques.

Land surface subsidence is an environmental hazard resulting from the extraction of underground resources. In underground mining, when mineral materials are extracted deep within the ground, the emptying or caving of the mined spaces leads to vertical displacement of the ground, known as subsidence. This subsidence can extend to the surface as trough subsidence, as the movement and deformation of the hanging-wall rocks of the mining stope propagate upwards. Accurately predicting subsidence is crucial for estimating damage and protecting surface buildings and structures in mining areas. Therefore, developing a model that considers all relevant parameters for subsidence estimation is essential. In this article, we discuss the prediction of land subsidence caused by the caving of a stop's roof, focusing on coal mining using the longwall method. The main aim of this research is to improve the accuracy of prediction models of land subsidence due to mining. For this purpose, we consider a total of 11 parameters related to coal mining, including mining thickness and depth (related to the deposit), as well as density, cohesion, internal friction angle, elasticity modulus, bulk modulus, shear modulus, Poisson's ratio, uniaxial compressive strength, and tensile strength (related to the overburden). We utilize information collected from 14 coal mines regarding mining and subsidence to achieve this. We then explore the prediction of subsidence caused by mining using the gene expression programming (GEP) algorithm, optimized through a combination of the artificial bee colony (ABC) and ant lion optimizer (ALO) algorithms. Modeling results demonstrate that combining the GEP algorithm with optimization based on the ABC algorithm yields the best subsidence prediction, achieving a correlation coefficient of 0.96. Furthermore, sensitivity analysis reveals that mining depth and density have the greatest and least effects, respectively, on land surface subsidence resulting from coal mining using the longwall method.

Optimization design of cement mixing columns supported height embankment using Plaxis remote scripting and Gene-expression programming technique

This paper is going to present the advanced feature in Plaxis using remote scripting with Python wrapper. Python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making.

Evaluating Future Building Energy Efficiency and Environmental Sustainability with PCM integration in Building Envelope

The energy demand in the building sector is anticipated to increase with climate change and the high energy consumption is responsible for releasing enormous amounts of CO2 into the environment, intensifying climate change. Phase change material (PCM) integration into the building envelope has a high potential to reduce the building's energy demand without compromising environmental sustainability. This research investigated the impact of PCMs (PCM 18–PCM 30) on building cooling, heating, and annual energy efficiency for future climate scenarios (2095) in 13 different climate zones worldwide. For this purpose, the Fanger comfort model was implemented and numerical simulations were performed using Energyplus. The performance of PCM was evaluated using novel Predicted Mean Vote fluctuation reductions (PMVFR), and the Total Predicted Mean Vote drop (TPMVD). Further, for the first time, a time series-forecasting model for the estimation of CO2 emissions originating from various energy production sources in the future was developed using gene-expression programming (GEP) algorithm. Finally, the two different maps showing the PCM efficiency for energy conservation and environmental sustainability at the global level are presented. The results evidence a maximum of 12.9% reduction in annual energy demand in the future with the integration of PCM. The GEP-based prediction model demonstrated good accuracy as determined by the statistical parameters and satisfied the external validation requirements. The environmental analysis exhibits that PCM integration can reduce carbon emissions up to 204 CO2-e Kg/year. Overall, the integration of PCM is regarded as a sustainable solution for building energy efficiency, considering climate change.

Predicting the precipitated calcium carbonate and unconfined compressive strength of bio-mediated sands through robust hybrid optimization algorithms

Biological soil stabilization through Microbial-Induced Calcite Precipitation (MICP) demonstrates the potential to be an eco-friendly and sustainable approach to enhance the strength properties of cohesionless granular soils. In this study, based on a comprehensive set of experimental data compiled from literature, the amounts of Precipitated Calcium Carbonate (PCC) and Unconfined Compressive Strength (UCS) of MICP-treated sands are estimated through hybridizing Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models with metaheuristic algorithms. During the data extraction procedure, a total of 70 and 115 datasets are used to predict the PCC and UCS, respectively. Accordingly, five key features of urea concentration (Mu), calcium chloride (Mcc), optical density of bacterial suspension (OD600), pH and total volume of injection per volume of sample (Vt), are first selected as the inputs of the hybrid models to estimate the amount of calcium carbonate precipitation within the porous structure of MICP-treated granular medium. The experimental data on the PCC along with the basic characteristics of the host sand including mean grain size (D50), uniformity coefficient (Cu) and void ratio (e), are then adopted as the inputs of the numerical models in the second phase whilst the UCS of bio-mediated sands is set as the output. The performance and accuracy of implemented models are rigorously assessed through analyzing various statistical indices and performance criteria. According to the numerical analysis, the ANN-PSO and ANFIS-PSO hybrid models show the best performance in predicting PCC and UCS of bio-mediated sands, respectively. Using the well-established Gene Expression Programming (GEP) algorithm, practical correlations are developed to predict the PCC and UCS values of MICP-mediated soils based on the characteristics of biological binding agents as well as the host material. The Machine Learning (ML)-based models presented in this study provide engineers with a fruitful framework for the rough and preliminary estimation of the amount of calcite precipitation as well as the UCS of bio-mediated sands in the field prior to stabilization and performing complementary tests.

Conducting 2D and 3D QSAR Analyses and Molecular Docking Studies of Analogues of 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl)ethan-1-ol with the Aim of Identifying Promising Drug Candidates for Targeting Glioblastoma

Background: 2-(1-(1,3,4-thiadiazol-2-yl)piperidin-4-yl) ethan-1-ol analogues represent novel glutaminase 1 inhibitors. Their exemplary antineoplastic efficacy underscores their prospective utility in glioblastoma chemotherapy. Objectives: This study aimed to elucidate 2D and 3D-QSAR models that authenticate the antineoplastic efficacy of ethan-1-ol analogues and delineate optimal structural configurations conducive to new pharmaceutical design. Methods: The Heuristic Method (HM) was employed for the development of a 2D-linear QSAR paradigm, whilst the Gene Expression Programming (GEP) algorithm was employed for a 2D-nonlinear QSAR paradigm. Concurrently, the CoMSIA methodology was deployed to scrutinize the nexus between pharmaceutical structure and potency. An ensemble of 200 nascent anti-glioma ethan-1-ol compounds was conceptualized, and their potency levels were prognosticated via chemical descriptors and molecular field delineations. Pharmaceuticals epitomizing peak potency were earmarked for molecular docking validation. Results: The empirical modeling exhibited pronounced superiority with the 3D paradigm, succeeded by the GEP nonlinear paradigm and culminated with the HM linear model. The 3D paradigm was characterized by a robust Q2 (0.533), R2 (0.921), and F-values (132.338) complemented by a minimal SEE (0.110). The molecular descriptor MNO coupled with the hydrogen bond donor field facilitated novel pharmaceutical conceptualizations, leading to the identification of the quintessential active molecule, 24J.138, lauded for its superlative antineoplastic attributes and docking proficiency. Conclusion: The orchestration of bidimensional and tridimensional paradigms, synergized by innovative amalgamation of contour maps and molecular descriptors, provides novel insights and methodologies for the synthesis of glioblastoma chemotherapeutic agents.

Online state of health estimation for lithium-ion batteries based on gene expression programming

Lithium-ion battery is a kind of energy storage devices with complex internal reaction and many factors affecting its performance. Accurate prediction of its SOH (State of Health) is of great significance to prolong its service life and improve safety performance. However, the current prediction for SOH has the difficulties of selecting health factors and using data-driven methods with opaque mechanisms. In this paper, a data-driven model will be established with the capacity change during aging of lithium-ion batteries as a health indicator to realize precise prediction of capacity degradation. Firstly, the feature parameters were extracted and analyzed from the battery cyclic aging test dataset. Two in-situ nondestructive characterization methods, incremental capacity analysis curve and differential thermal voltammetry curve, were utilized to resolve the evolution paths of the feature parameters. After that, a data-driven battery capacity degradation estimation is realized based on the GEP (Gene Expression Programming) algorithm, the performance is compared with the existing vehicle-end and cloud-end models, and a higher-accuracy SOH stacking model is developed with an increase of less than 1 ms in computational time. The results indicated that the GEP model proposed in this paper has obvious advantages in terms of physical explain-ability, computational efficiency and robustness.

Big Data Anomaly Prediction Algorithm of Smart City Power Internet of Things Based on Parallel Random Forest

Abstract Conventional power data prediction algorithms easily lead to the loss of key power data in a complex wireless network environment. Therefore, a power big data anomaly prediction algorithm based on parallel random forest is proposed. According to the power big data anomaly prediction algorithm based on parallel random forest, a network power big data anomaly prediction algorithm platform is established, and based on the platform, key data features such as user address and power transmission packet structure are extracted according to the category of power users. According to the relationship between power shunt function value and power data unit density, the parameter value of the system and finally the reasonable anomaly prediction of power big data in wireless network are determined. Finally, filter the classified data through attribute reduction and gene expression programming algorithm to obtain the data to be encrypted and complete the research on the anomaly prediction algorithm of power big data. Experimental results show that the proposed algorithm has better prediction performance and can ensure better data prediction effect.

On the Evaluation of Coal Strength Alteration Induced by CO2 Injection Using Advanced Black-Box and White-Box Machine Learning Algorithms

Summary The injection of carbon dioxide (CO2) into coal seams is a prominent technique that can provide carbon sequestration in addition to enhancing coalbed methane extraction. However, CO2 injection into the coal seams can alter the coal strength properties and their long-term integrity. In this work, the strength alteration of coals induced by CO2 exposure was modeled using 147 laboratory-measured unconfined compressive strength (UCS) data points and considering CO2 saturation pressure, CO2 interaction temperature, CO2 interaction time, and coal rank as input variables. Advanced white-box and black-box machine learning algorithms including Gaussian process regression (GPR) with rational quadratic kernel, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting decision tree (AdaBoost-DT), multivariate adaptive regression splines (MARS), K-nearest neighbor (KNN), gene expression programming (GEP), and group method of data handling (GMDH) were used in the modeling process. The results demonstrated that GPR-Rational Quadratic provided the most accurate estimates of UCS of coals having 3.53%, 3.62%, and 3.55% for the average absolute percent relative error (AAPRE) values of the train, test, and total data sets, respectively. Also, the overall determination coefficient (R2) value of 0.9979 was additional proof of the excellent accuracy of this model compared with other models. Moreover, the first mathematical correlations to estimate the change in coal strength induced by CO2 exposure were established in this work by the GMDH and GEP algorithms with acceptable accuracy. Sensitivity analysis revealed that the Spearman correlation coefficient shows the relative importance of the input parameters on the coal strength better than the Pearson correlation coefficient. Among the inputs, coal rank had the greatest influence on the coal strength (strong nonlinear relationship) based on the Spearman correlation coefficient. After that, CO2 interaction time and CO2 saturation pressure have shown relatively strong nonlinear relationships with model output, respectively. The CO2 interaction temperature had the smallest impact on coal strength alteration induced by CO2 exposure based on both Pearson and Spearman correlation coefficients. Finally, the leverage technique revealed that the laboratory database used for modeling CO2-induced strength alteration of coals was highly reliable, and the suggested GPR-Rational Quadratic model and GMDH correlation could be applied for predicting the UCS of coals exposed to CO2 with high statistical accuracy and reliability.

Predicting 28-Day Compressive Strength of Self-Compacting Concrete (SCC) Using Gene Expression Programming (GEP)

SCC is an innovative building material which can flow and compact itself without the use of external vibrations. It is an effective material to enhance the use of industrial waste products such fly ash and silica fume etc in concrete to reduce the carbon emissions from construction industry. Despite the many advantages of SCC over conventional concrete, there are very few methods which can effectively forecast compressive strength of SCC. It is due to the non-linear behaviour of SCC in relation to its mixture components. Thus, an innovative Machine Learning technique called Gene Expression Programming (GEP) is employed to estimate the strength of SCC. For this purpose, a database consisting of 231 datapoints is constructed using extensive literature search. The algorithm resulted in an empirical equation that relates compressive strength with seven most influential parameters: cement, fly ash, silica fume, coarse and fine aggregate, water, and superplasticizer. The dataset is split into two sets called the training and validation datasets having 70% and 30% of the data respectively. The training and validation data will be used to train and validate the algorithm respectively. The algorithm’s accuracy is checked by calculating the four commonly used error metrices: mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (R) and performance index (ƿ) for both datasets. The statistical evaluation revealed that the errors are within the ranges specified in the literature. The accuracy of the algorithm is also verified by plotting scatter and series plots of training and validation datasets. Thus, the developed equation by GEP algorithm can be effectively used to forecast the 28-day compressive strength of SCC having fly ash and silica fume as mineral admixtures.

An improved gene expression programming algorithm for function mining of map-reduce job execution in catenary monitoring systems.

Gene expression programming (GEP) is one of the most prominent algorithms in function mining. In order to obtain a more accurate function model in configuration parameters-execution efficiency (CP-EE) of map-reduce job in the high-speed railway catenary monitoring system, this paper proposes a novel algorithm, called GEP based on multi-strategy (MS-GEP). Compared to traditional GEP, the proposed algorithm can escape premature convergence and jump out of local optimum. First, an adaptive mutation rate is designed according to the evolutionary generations, population diversity, and individual fitness values. A manual intervention strategy is then proposed to determine whether the algorithm enters the dilemma of local optimum based on the generations of population evolutionary stagnation. Finally, the average quality of the population is changed by randomly replacing individuals, and the ancestral population is traced to change the evolutionary direction. The experimental results on the benchmarks of function mining show that the proposed MS-GEP has better solution quality and higher population diversity than other GEP algorithms. Furthermore, the proposed MS-GEP has higher accuracy on the function model of CP-EE of high-speed railway catenary monitoring system than other commonly used algorithms in the field of function mining.

Explicit pipe friction factor equations: evaluation, classification, and proposal

The Colebrook equation has been used to estimate the friction factor (f) in turbulent fluids. In this regard, many equations have been proposed to eliminate the iterative process of the Colebrook equation. The goal of this article was to perform an evaluation, classification, and proposal of the friction factor for better development of hydraulic projects. In this study, Gene Expression Programming (GEP), Newton-Raphson, and Python algorithms were applied. The accuracy and model selection were performed with the Maximum Relative Error (∆f/f), Percentage Standard Deviation (PSD), Model Selection Criterion (MSC), and Akaike Information Criterion (AIC). Of the 30 equations evaluated, the Vatankhah equation was the most accurate and simplest to obtain the friction factor with a classification of very high, reaching a value of ∆f/f<0.5% and 1.5<PSD<1.6. A new equation was formulated to obtain the explicit f with fast convergence and accuracy. It was concluded that the combination of GEP, error theory, and selection criteria provides a more reliable and strengthened model.

Research on Production Scheduling Optimization of Flexible Job Shop Production with Buffer Capacity Limitation Based on the Improved Gene Expression Programming Algorithm

Research on Production Scheduling Optimization of Flexible Job Shop Production with Buffer Capacity Limitation Based on the Improved Gene Expression Programming Algorithm

Mathematical model and augmented simulated annealing algorithm for mixed-model assembly job shop scheduling problem with batch transfer

In the mixed-model assembly job-shops, all the components are transferred in batches from the processing stage to the assembly stage to reduce transportation costs and timely satisfy the assembly material requirements. However, batch transfer in such a workshop has not received due attention in the current literature. Thus, this work addresses the mixed-model assembly job-shop scheduling problem with batch transfer via a mathematical model and an augmented simulated annealing algorithm to minimize manufacturing and transportation costs. The mathematical model takes batch transfer constraints into account and constructs the temporal and spatial links among the processing, transfer, and assembly stages. Production sequencing knowledge and batch transfer knowledge are first extracted via gene expression programming algorithm and clustering method and then implanted into the algorithmic operators in the form of rules and strategies to directly identify the promising solution space. Besides, eleven neighborhood structures including the critical component-based and batch-based ones, are designed to enhance the exploitation ability. A restart mechanism considering crossover and tempering helps to get out of local optima. Experimental results indicate that by adopting the extracted knowledge, problem-specific neighborhood structures, and restart mechanism, the proposed algorithm significantly outperforms other comparison algorithms in fixing the studied problem.

Exploration of 2D and 3D-QSAR analysis and docking studies for novel dihydropteridone derivatives as promising therapeutic agents targeting glioblastoma.

Background: Dihydropteridone derivatives represent a novel class of PLK1 inhibitors, exhibiting promising anticancer activity and potential as chemotherapeutic drugs for glioblastoma. Objective: The aim of this study is to develop 2D and 3D-QSAR models to validate the anticancer activity of dihydropteridone derivatives and identify optimal structural characteristics for the design of new therapeutic agents. Methods: The Heuristic method (HM) was employed to construct a 2D-linear QSAR model, while the gene expression programming (GEP) algorithm was utilized to develop a 2D-nonlinear QSAR model. Additionally, the CoMSIA approach was introduced to investigate the impact of drug structure on activity. A total of 200 novel anti-glioma dihydropteridone compounds were designed, and their activity levels were predicted using chemical descriptors and molecular field maps. The compounds with the highest activity were subjected to molecular docking to confirm their binding affinity. Results: Within the analytical purview, the coefficient of determination (R2) for the HM linear model is elucidated at 0.6682, accompanied by an R2 cv of 0.5669 and a residual sum of squares (S2) of 0.0199. The GEP nonlinear model delineates coefficients of determination for the training and validation sets at 0.79 and 0.76, respectively. Empirical modeling outcomes underscore the preeminence of the 3D-QSAR model, succeeded by the GEP nonlinear model, whilst the HM linear model manifested suboptimal efficacy. The 3D paradigm evinced an exemplary fit, characterized by formidable Q2 (0.628) and R2 (0.928) values, complemented by an impressive F-value (12.194) and a minimized standard error of estimate (SEE) at 0.160. The most significant molecular descriptor in the 2D model, which included six descriptors, was identified as "Min exchange energy for a C-N bond" (MECN). By combining the MECN descriptor with the hydrophobic field, suggestions for the creation of novel medications were generated. This led to the identification of compound 21E.153, a novel dihydropteridone derivative, which exhibited outstanding antitumor properties and docking capabilities. Conclusion: The development of 2D and 3D-QSAR models, along with the innovative integration of contour maps and molecular descriptors, offer novel concepts and techniques for the design of glioblastoma chemotherapeutic agents.

Modelling tunnel squeezing using gene expression programming: a case study

Squeezing is a well-known geotechnical phenomenon in the excavation of underground spaces, especially tunnels. Squeezing occurs under different geotechnical conditions in weak rock masses. A study was carried out to develop a mathematical and practical model to predict squeezing so that risk mitigation measures can be taken if necessary. A mathematical model based on a gene expression programming algorithm was developed using a database of 115 squeezing case histories. Four model performance evaluation criteria – specificity, sensitivity, accuracy and Matthew's correlation coefficient – and the receiver operating characteristic curve were then assessed based on a testing dataset. The accuracy of the model was also compared with that of previously developed models. Found to be able to determine squeezing with acceptable accuracy, the model was then used to investigate squeezing-prone areas in the Beheshtabad water-conveyance tunnel in Iran.

Plugging Characteristics and Evaluation Predicting Models by Controllable Self-Aggregation Nanoparticles in Pore Throat Microcapillaries.

Injecting nanoparticle profile agents into low-permeability heterogeneous reservoirs to plugging water breakthrough channels is a widely used technical method to enhance oil recovery. However, insufficient research on the plugging characteristics and prediction models of nanoparticle profile agents in the pore throat has led to a poor profile control effect, short profile control action time, and poor injection performance in the actual reservoir. This study uses controllable self-aggregation nanoparticles with a diameter of 500 nm and different concentrations as profile control agents. Microcapillaries of different diameter sizes were used to simulate the pore throat structure and flow space of oil reservoirs. Based on a large number of cross-physical simulation experimental data, the plugging characteristics of controllable self-aggregation nanoparticles in the pore throat were analyzed. Gray correlation analysis (GRA) and gene expression programming algorithm (GEP) analysis were used to determine the key factors affecting the resistance coefficient and plugging rate of profile control agents. With the help of GeneXproTools, the evolutionary algebra 3000 was selected to obtain the calculation formula and prediction model of the resistance coefficient and plugging rate of the injected nanoparticles in the pore throat. The experimental results show that the controllable self-aggregation nanoparticles will achieve effective plugging when the pressure gradient is greater than 100 MPa/m in the pore throat, and when the injection pressure gradient is 20-100 MPa/m, the nanoparticle solution will be in the aggregation to breakthrough state in the pore throat. The main factors affecting the injectability of nanoparticles, from strong to weak, are as follows: injection speed > pore length > concentration > pore diameter. The main factors affecting the plugging rate of nanoparticles, from strong to weak, are as follows: pore length > injection speed > concentration > pore diameter. The prediction model can effectively predict the injection performance and plugging performance of controllable self-aggregating nanoparticles in the pore throat. The prediction accuracy of the injection resistance coefficient is 0.91, and the accuracy of the plugging rate is 0.93 in the prediction model.

Effective and Interpretable Rule Mining for Dynamic Job-Shop Scheduling via Improved Gene Expression Programming with Feature Selection

Gene expression programming (GEP) is frequently used to create intelligent dispatching rules for job-shop scheduling. The proper selection of the terminal set is a critical factor for the success of GEP. However, there are various job features and machine features that can be included in the terminal sets to capture the different characteristics of the job-shop state. Moreover, the importance of features in the terminal set varies greatly between scenarios. The irrelevant and redundant features may lead to high computational requirements and increased difficulty in interpreting generated rules. Consequently, a feature selection approach for evolving dispatching rules with improved GEP has been proposed, so as to select the proper terminal set for different dynamic job-shop scenarios. First, the adaptive variable neighborhood search algorithm was embedded into the GEP to obtain a diverse set of good rules for job-shop scenarios. Secondly, based on the fitness of the good rules and the contribution of features to the rules, a weighted voting ranking method was used to select features from the terminal set. The proposed approach was then compared with GEP-based algorithms and benchmark rules in the different job-shop conditions and scheduling objectives. The experimentally obtained results illustrated that the performance of the dispatching rules generated using the improved GEP algorithm after the feature selection process was better than that of both the baseline dispatching rules and the baseline GEP algorithm.

Computation of energy across the type-C piano key weir using gene expression programming and extreme gradient boosting (XGBoost) algorithm

An accurate assessment of energy loss across dams and weir systems is a critical technical and monetary remedy for understanding the hydraulic system’s downstream morphology during the flood. Using the standard empirical formulas, accurately estimating the energy loss across the different hydraulic devices is a tedious and challenging procedure. Consequently, new and concise techniques remain highly sought after. This paper presents two empirical models based on the Gene Expression Programming (GEP) and Extreme Gradient Boosting (XGBoost) techniques to examine energy losses throughout the type-C PKW. The empirical models have been developed to consider five non-dimensional parameters, viz L/W, Ht/P, Wi/Wo, N, and Si/So, that influence the energy over the weir significantly. The models were created using experimental data from a wide range of residual energy losses and release capacities. Additionally, the adequacy of the constructed GEP and XGBoost models was assessed using the RMSE (root mean square error) and statistical variables coefficient (R2). As per the outcomes, the XGBoost model beats the GEP with the determination coefficient (R2) = 0.999, MAE = 0.0062, MAPE = 1.4% and RMSE = 0.0012 in the training stage and R2=0.998, MAE = 0.001, MAPE = 2.1, and RMSE = 0.001 in the testing data. These findings show that the XGBoost algorithm is more accurate than the two algorithms in this study for downstream energy prediction of type-C PKW.

3D- and 2D-QSAR models' study and molecular docking of novel nitrogen-mustard compounds for osteosarcoma.

Background: The dipeptide-alkylated nitrogen-mustard compound is a new kind of nitrogen-mustard derivative with a strong anti-tumor activity, which can be used as a potential anti-osteosarcoma chemotherapy drug. Objective: 2D- and 3D-QSAR (structure-activity relationship quantification) models were established to predict the anti-tumor activity of dipeptide-alkylated nitrogen-mustard compounds. Method: In this study, a linear model was established using a heuristic method (HM) and a non-linear model was established using the gene expression programming (GEP) algorithm, but there were more limitations in the 2D model, so a 3D-QSAR model was introduced and established through the CoMSIA method. Finally, a series of new dipeptide-alkylated nitrogen-mustard compounds were redesigned using the 3D-QSAR model; docking experiments were carried out on several compounds with the highest activity against tumors. Result: The 2D- and 3D-QSAR models obtained in this experiment were satisfactory. A linear model with six descriptors was obtained in this experiment using the HM through CODESSA software, where the descriptor "Min electroph react index for a C atom" has the greatest effect on the compound activity; a reliable non-linear model was obtained using the GEP algorithm model (the best model was generated in the 89th generation cycle, with a correlation coefficient of 0.95 and 0.87 for the training and test set, respectively, and a mean error of 0.02 and 0.06, respectively). Finally, 200 new compounds were designed by combining the contour plots of the CoMSIA model with each other, together with the descriptors in the 2D-QSAR, among which compound I1.10 had a high anti-tumor and docking ability. Conclusion: Through the model established in this study, the factors influencing the anti-tumor activity of dipeptide-alkylated nitrogen-thaliana compounds were revealed, providing direction and guidance for the further design of efficient chemotherapy drugs against osteosarcoma.

3D,2D-QSAR study and docking of novel quinazolines as potential target drugs for osteosarcoma.

Background: Quinazolines are an important class of benzopyrimidine heterocyclic compounds with a promising antitumor activity that can be used for the design and development of osteosarcoma target compounds. Objective: To predict the compound activity of quinazoline compounds by constructing 2D- and 3D-QSAR models, and to design new compounds according to the main influencing factors of compound activity in the two models. Methods: First, heuristic method and GEP (gene expression programming) algorithm were used to construct linear and non-linear 2D-QSAR models. Then a 3D-QSAR model was constructed using CoMSIA method in SYBYL software package. Finally, new compounds were designed according to molecular descriptors of 2D-QSAR model and contour maps of 3D-QSAR model. Several compounds with optimal activity were used for docking experiments with osteosarcoma related targets (FGFR4). Results: The non-linear model constructed by GEP algorithm was more stable and predictive than the linear model constructed by heuristic method. A 3D-QSAR model with high Q2 (0.63) and R 2 (0.987) values and low error values (0.05) was obtained in this study. The success of the model fully passed the external validation formula, proving that the model is very stable and has strong predictive power. 200 quinazoline derivatives were designed according to molecular descriptors and contour maps, and docking experiments were carried out for the most active compounds. Compound 19g.10 has the best compound activity with good target binding capability. Conclusion: To sum up, the two novel QSAR models constructed were very reliable. The combination of descriptors in 2D-QSAR with COMSIA contour maps provides new design ideas for future compound design in osteosarcoma.