Types Of Descriptors Research Articles

VotePLMs-AFP: Identification of antifreeze proteins using transformer-embedding features and ensemble learning

Antifreeze proteins (AFPs) are a unique class of biomolecules capable of protecting other proteins, cell membranes, and cellular structures within organisms from damage caused by freezing conditions. Given the significance of AFPs in various domains such as biotechnology, agriculture, and medicine, several machine learning methods have been developed to identify AFPs. However, due to the complexity and diversity of AFPs, the predictive performance of existing methods is limited. Therefore, there is an urgent need to develop an efficient and rapid computational method for accurately predicting AFPs. In this study, we proposed a novel predictor based on transformer-embedding features and ensemble learning for the identification of AFPs, termed VotePLMs-AFP. Firstly, three types of feature descriptors were extracted from pre-trained protein language models (PLMs) during the feature extraction process. Subsequently, we analyzed six combinations generated by these three embeddings to explore the optimal feature set, which was input into the soft voting-based ensemble learning classifier for the identification of AFPs. Finally, we evaluated the model on the two benchmark datasets. The experimental results show that our model achieves high prediction accuracy in 10-fold cross-validation (CV) and independent set testing, outperforming existing state-of-the-art methods. Therefore, our model could serve as an effective tool for predicting AFPs.

Development of machine learning-based quantitative structure–activity relationship models for predicting plasma half-lives of drugs in six common food animal species

Abstract Plasma half-life is a crucial pharmacokinetic parameter for estimating extralabel withdrawal intervals of drugs to ensure the safety of food products derived from animals. This study focuses on developing a quantitative structure–activity relationship (QSAR) model incorporating multiple machine learning and artificial intelligence algorithms, and aims to predict the plasma half-lives of drugs in 6 food animals, including cattle, chickens, goats, sheep, swine, and turkeys. By integrating 4 machine learning algorithms with 5 molecular descriptor types, 20 QSAR models were developed using data from the Food Animal Residue Avoidance Databank (FARAD) Comparative Pharmacokinetic Database. The deep neural network (DNN) algorithm demonstrated the best prediction ability of plasma half-lives. The DNN model with all descriptors achieved superior performance with a high coefficient of determination (R2) of 0.82 ± 0.19 in 5-fold cross-validation on the training sets and an R2 of 0.67 on the independent test set, indicating accurate predictions and good generalizability. The final model was converted to a user-friendly web dashboard to facilitate its wide application by the scientific community. This machine learning-based QSAR model serves as a valuable tool for predicting drug plasma half-lives and extralabel withdrawal intervals in 6 common food animals based on physicochemical properties. It also provides a foundation to develop more advanced models to predict the tissue half-life of drugs in food animals.

Multimodal deep learning framework to predict strain localization of Mg/LPSO two-phase alloys

This study proposes a method for predicting three-dimensional (3D) local strain distribution under compressive deformation of as-cast Mg/LPSO two-phase alloys from 3D microstructure images. The 3D local strain distribution was obtained by applying the digital volume correlation method to X-ray CT images before and after compression tests. Three microstructure descriptors were extracted from the 3D microstructure images around each strain measurement point: volume fractions of the phases, persistent diagrams that can express the connectivity of the phases, and two-phase spatial correlation that can express the spatial distribution of the phases. A deep learning model was then constructed to predict local strain from the three microstructure descriptors. Since two types of descriptors were used in this study, numerical data and image data, multimodal deep learning was employed to make predictions. Thus, the use of multiple microstructure descriptors enabled predictions to be made with higher accuracy than when predictions were made from a single descriptor. Feature importance of the descriptors was assessed through correlation analysis and occlusion sensitivity analysis. The results revealed that high strain tended to occur in the region where the hard phase, LPSO phase, had a large elongated phase oriented at a 45° direction to the loading direction. This result is consistent with other previous studies and indicates that the proposed method is effective in elucidating the relationship between the microstructure and the deformation behavior of the material.

Predicting antimicrobial properties of lignin derivatives through combined data driven and experimental approach

Meta-analysis, experimental and data-driven quantitative structure–activity relationship (QSAR) models were developed to predict the antimicrobial properties of lignin derivatives. Five machine learning algorithms were applied to develop QSAR models based on the ChEMBL, a public non-lignin specific database. QSAR models were refined using ordinary-least-square regressions with a meta-analysis dataset extracted from literature and an experimental dataset. The minimum inhibition concentration (MIC) values of compounds in the meta-analysis dataset correlate to classification-based descriptors and the number of aliphatic carboxylic acid groups (R2 = 0.759). Comparatively, QSARs derived from the experimental datasets suggest that the number of aromatic hydroxyl groups were better predictors of Bacterial Load Difference (BLD, R2 = 0.831) for Bacillus subtilis, while the number of alkyl aryl groups were the strongest correlation in predicting the BLD (R2 = 0.682) of Escherichia coli. This study provides insights into the type of descriptors that correlate to antimicrobial activity and guides the valorization of lignin into sustainable antimicrobials for potential applications in food preservation, fermentation, and other industrial sectors.

A content analysis of cannabis edibles package marketing in the United States

Background: With states legalizing cannabis at a rapid pace, and the increasing popularity of edibles, it is important to document marketing practices to better understand how they might be appealing and misleading to consumers to guide state policymakers. Methods: A descriptive content analysis of 1229 cannabis edible packages advertised on a publicly available website between June and November 2022 and available for sale in licensed dispensaries was performed. Results: Healthy ingredient descriptors were the most common type of descriptor with 31 % of packages including words like “vegan”, “gluten free” and “natural”. Quality descriptors like “handcrafted” were on 28 % of packages. Other descriptors were focused on the consumer experience including expected effects (e.g., “relax”) (27 %), taste or flavor (e.g., “sour”) (21 %) and pharmacokinetics (e.g., “fast-acting”) (19 %). Images of non-cannabis plants and outdoor nature settings were on half of packages. Images of the cannabis plant were on 33 % of packages. Flavor imagery including images of food were common (43 %). Other marketing appeals included images of people (15 %), animals (12 %) and space (10 %). Conclusions: Package marketing used by other commercial industries was common on cannabis edible packages. Edibles marketing is distinct from other cannabis products in its ability to focus on the food ingredients which could mislead consumers into thinking the cannabis, rather than the food, is healthy or less harmful. Research examining the impact of cannabis edibles marketing strategies on appeal and harm perceptions is critically needed to guide policymakers as they establish packaging regulations to optimize public health and safety.

Machine-Learning-Assisted Design of Deep Eutectic Solvents Based on Uncovered Hydrogen Bond Patterns

Non-ionic deep eutectic solvents (DESs) are non-ionic designer solvents with various applications in catalysis, extraction, carbon capture, and pharmaceuticals. However, discovering new DES candidates is challenging due to a lack of efficient tools that accurately predict DES formation. The search for DES relies heavily on intuition or trial-and-error processes, leading to low success rates or missed opportunities. Recognizing that hydrogen bonds (HBs) play a central role in DES formation, we aim to identify HB features that distinguish DES from non-DES systems and use them to develop machine learning (ML) models to discover new DES systems. We first analyze the HB properties of 38 known DES and 111 known non-DES systems using their molecular dynamics (MD) simulation trajectories. The analysis reveals that DES systems have two unique features compared to non-DES systems: The DESs have ① more imbalance between the numbers of the two intra-component HBs and ② more and stronger inter-component HBs. Based on these results, we develop 30 ML models using ten algorithms and three types of HB-based descriptors. The model performance is first benchmarked using the average and minimal receiver operating characteristic (ROC)-area under the curve (AUC) values. We also analyze the importance of individual features in the models, and the results are consistent with the simulation-based statistical analysis. Finally, we validate the models using the experimental data of 34 systems. The extra trees forest model outperforms the other models in the validation, with an ROC-AUC of 0.88. Our work illustrates the importance of HBs in DES formation and shows the potential of ML in discovering new DESs.

CPSign: conformal prediction for cheminformatics modeling

Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptors are also supported. The main modeling methodology is support vector machines (SVMs), but additional modeling methods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efficiency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approaches including random forest, and a directed message-passing neural network. The results show that CPSign produces robust predictive performance with comparative predictive efficiency, with superior runtime and lower hardware requirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input files, perform descriptor calculation and modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet flexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign.Scientific contribution CPSign provides a single software that allows users to perform data preprocessing, modeling and make predictions directly on chemical structures, using conformal and probabilistic prediction. Building and evaluating new models can be achieved at a high abstraction level, without sacrificing flexibility and predictive performance—showcased with a method evaluation against contemporary modeling approaches, where CPSign performs on par with a state-of-the-art deep learning based model.

Machine learning methods for unveiling the potential of antioxidant short peptides in goat milk-derived proteins during in vitro gastrointestinal digestion

Milk serves as an important dietary source of bioactive peptides, offering notable benefits to individuals. Among the antioxidant short peptides (di- and tripeptides) generated from gastrointestinal digestion are characterized by enhanced bioavailability and bioaccessibility, while assessing them individually presents a labor-intensive and expensive challenge. Based on 4 distinct types of AA descriptors (physicochemical, 3-dimensional structural, quantum, and topological attributes) and genetic algorithms for feature selection, 1 and 4 machine learning-predicted models separately for di- and tripeptides with 2,2-azinobis(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt radical scavenging capacity exhibited excellent fitting and prediction ability with random forest regression as machine learning algorithm. Intriguingly, the electronic properties of N-terminal AA were considered as only factor affecting the antioxidant capacity of dipeptides containing both tyrosine and tryptophan. Four peptides from the potential di- and tripeptides exhibited highly predicted values by the constructed predicted models. Subsequently, a total of 45 dipeptides and 52 tripeptides were screened by a customized workflow in goat milk during in vitro simulated digestion. In addition to 5 known antioxidant dipeptides, 9 peptides were quantified during digestion, exhibiting concentrations ranging from 0.04 to 1.78 mg L-1. Particularly noteworthy was the promising in vivo functionality of antioxidant dipeptides with N-terminal tyrosine, supported by in silico assays. Overall, this investigation explored crucial molecular properties influencing antioxidant short peptides and high-throughput screening potential peptides with antioxidant activity from goat milk aided by machine learning, thereby facilitating the discovery of novel functional peptides from milk-derived proteins and paving the way for understanding their metabolites during digestion.

Computing the Hosoya Index of Some Nanostar Dendrimers

Dendrimers are highly branched macromolecules built up from a monomer, with new branches added in steps until a tree structure is created. The various biological characteristics of dendrimers are a good choice in chemistry, biology, the medical field, and nano-science. A topological index is a type of molecular descriptor that is calculated based on the molecular graph of a chemical structure. The Hosoya index is a well-known topological index that is used to predict some of their physico-chemical properties from the structure of molecules. The Hosoya index of a graph is defined as the total number of its independent edge sets. In this paper, we study the Hosoya index of some families of nanostar dendrimers. We obtain the formulas of the Hosoya index for two infinite classes of dendrimers, namely, nanostar dendrimer and tetrathiafulvalene dendrimer. We use the Mathematica program to evaluate the results and accuracy of calculations. Our results can be used in analyzing the molecular topology of these families of nanostar dendrimers.

Insight to the prediction of CO2 solubility in ionic liquids based on the interpretable machine learning model

In this work, we investigated three different machine learning (ML)-based models, i.e., gaussian process regression (GPR), LightGBM, and CatBoost, for predicting the solubility of CO2 in various ionic liquids (ILs). Three molecular descriptors, i.e., group contribution (GC), molecular structure descriptors (MSD), and hybrid GC-MSD are used in our three models. The performance of our developed models were rigorously evaluated using mean absolute error (MAE), coefficient of determination (R2), and mean relative error (MRE) (i.e., relative deviation in percentage), with each model subjected to multiple tests employing different random state parameters. The dataset underwent partitioning into training and testing sets at an 80:20 ratio, with additional splits at various ratios to assess prediction performance sensitivity. Overall, all models exhibited proficient CO2 solubility prediction in ILs, with performance varying based on descriptor type. Notably, the hybrid GC-MSD consistently outperformed others, attributed to GC-MSD incorporates a broader array of molecular feature information. Particularly, the CatBoost-GC-MSD model excelled, achieving an impressive R2 of 0.9925, MAE of 0.0122, and MRE of 11.1550%. Comparing our models to previous studies revealed the superior performance of CatBoost-GC-MSD across all descriptor types. Furthermore, our model interpretation, employing shapley additive explanation (SHAP) analysis, identified pressure, temperature, Chi0, Kappa2, and EState_VSA10 as the top five influential input features. These findings provide valuable insights into the underlying molecular features affecting CO2 solubility in ILs and lay the foundation for future research in this field.

What types of performance level labels and descriptors are better suited for equity measurement tools? Contrasting recommendations

ABSTRACT The Equity Audit was designed to measure diversity, equity, and inclusion (DEI) within workplaces. The tool has three versions and is currently undergoing the standard setting process to gather validity evidence for its use within schools and organizations. This study reports on the recommendations from two panels of experts (one for the school version, the other for the Nonprofits/For-profit versions) regarding language use for performance level descriptors and labels. Our findings show that the panels were similar in their recommendations for performance level labels but differed in recommendations for the language of descriptors given workspace type. We discuss these findings in relation to the ethical and critical lens evaluators may adopt in considering the development of tools for DEI work.

Catalytic Activity of 2-Imino-1,10-phenthrolyl Fe/Co Complexes via Linear Machine Learning.

In anticipation of the correlations between catalyst structures and their properties, the catalytic activities of 2-imino-1,10-phenanthrolyl iron and cobalt metal complexes are quantitatively investigated via linear machine learning (ML) algorithms. Comparatively, the Ridge Regression (RR) model has captured more robust predictive performance compared with other linear algorithms, with a correlation coefficient value of R2= 0.952 and a cross-validation value of Q2= 0.871. It shows that different algorithms select distinct types of descriptors, depending on the importance of descriptors. Through the interpretation of the RR model, the catalytic activity is potentially related to the steric effect of substituents and negative charged groups. This study refines descriptor selection for accurate modeling, providing insights into the variation principle of catalytic activity.

An integrated approach to predict activators of NRF2 - the transcription factor for oxidative stress response

A variety of environmental and physiological conditions can cause oxidative stress that damage cellular components such as DNA, proteins and lipids. Oxidative stress is implicated in many human diseases including cancer, cardiovascular diseases, neurological diseases, inflammatory diseases, and aging. The nuclear factor erythroid 2–related factor 2 (NRF2) is a transcriptional factor that plays a key role in the cellular antioxidant defense system as it regulates transcription of antioxidant proteins and detoxifying enzymes. There is an urgent need to identify novel compounds that activate NRF2 and enhance antioxidant defense. We collected data from the high-throughput screening of NRF2 activators and identified molecular fragments (structural alerts) associated with the activation of NRF2. We also developed ten classification models using different types of molecular descriptors and machine learning techniques. Two approaches were used to establish the applicability domain of developed models: the structure-based approach and the distance to model approach. The best performing model that used message passing neural network (MPNN) technique showed accuracy of 87 % for the test set of chemicals within the distance to model of 0.3. The integrative approach using a combination of generated structural alerts and MPNN model was used to screen approved drugs collected in the DrugBank to identify potential NRF2 activators. Out of 2393 screened chemicals 138 compounds were predicted as NRF2 activators by both approaches. Analysis of these compounds showed that some drugs were already known activators of NRF2 while others are potentially novel activators.

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.

QSAR Analysis and Molecular Docking Studies of Aryl Sulfonamide Derivatives as Mcl-1 Inhibitors and the Influence of Structure and Chirality on the Inhibitory Activity

Background:: Mcl-1 is a kind of antiapoptotic protein and its overexpression is closely related to the occurrence of cancer. Aryl sulfonamide derivatives are expected to become new anticancer agents due to their high inhibitory activity on the Mcl-1 protein. Objective:: The study aimed to establish the QSAR model with good prediction ability and elaborate the influence of structure and chirality on the inhibitory activity. method: Multiple QSAR models were built with different types of descriptors and modeling methods. The molecular docking was performed on compound 45, 25, 26, 24R and 24S. Results:: The comprehensive models including 2D and 3D descriptors demonstrated that nonlinear LSSVM and GP methods gave better results (R2>0.94, RCV2>0.86). The training set had a good predictive power on the test set. The predictive performances of MCCV tests are basically coincident with the results of the single test set. The results of molecular docking showed that the hydrogen bond acceptor at the appropriate position of the substituent on the chiral center can form the hydrogen bond interaction with residue ASN260, which results in the stronger interaction between ligand and protein and higher inhibitory activity. The interaction differences between R and S configuration with Mcl-1 protein are mainly attributed to two residues, HIS224 and ASN260. Two opposite effects lead to the activity of R enantiomer slightly higher than that of S one. The results on chiral compound 24 with ambiguous absolute configuration demonstrated that the steric effect of the substituents on chiral carbon atom is crucial. When there are two substituents with big volume at the same time, high steric effect will prevent the binding of the substituent and the protein, which results in the low inhibitory activity. Conclusion:: The study may provide theoretical guidance on the design and synthesis of novel aryl sulfonamide derivatives with high inhibitory activity

Clustering of atoms relative to vector space in the Z-matrix coordinate system and 'graphical fingerprint' analysis of 3D pharmacophore structure.

The behavior of a molecule within its environment is governed by chemical fields present in 3D space. However, beyond local descriptors in 3D, the conformations a molecule assumes, and the resulting clusters also play a role in influencing structure-activity models. This study focuses on the clustering of atoms according to the vector space of four atoms aligned in the Z-Matrix Reference system for molecular similarity. Using 3D-QSAR analysis, it was aimed to determine the pharmacophore groups as interaction points in the binding region of the β2-adrenoceptor target of fenoterol stereoisomers. Different types of local reactive descriptors of ligands have been used to elucidate points of interaction with the target. Activity values for ligand-receptor interaction energy were determined using the Levenberg-Marquardt algorithm. Using the Molecular Comparative Electron Topology method, the 3D pharmacophore model (3D-PhaM) was obtained after aligning and superimposing the molecules and was further validated by the molecular docking method. Best guesses were calculated with a non-output validation (LOO-CV) method. Finally, the data were calculated using the 'graphic fingerprint' technique. Based on the eLKlopman (Electrostatic LUMO Klopman) descriptor, the Q2 value of this derivative set was calculated as 0.981 and the R2ext value is calculated as 0.998.

Quantitative Predictive Studies of Multiple Biological Activities of TRPV1 Modulators.

TRPV1 channel agonists and antagonists, which have powerful analgesic effects without the addictive qualities associated with traditional analgesics, have become a focus area for the development of novel analgesics. In this study, quantitative structure-activity relationship (QSAR) models for three bioactive endpoints (Ki, IC50, and EC50) were successfully constructed using four machine learning algorithms: SVM, Bagging, GBDT, and XGBoost. These models were based on 2922 TRPV1 modulators and incorporated four types of molecular descriptors: Daylight, E-state, ECFP4, and MACCS. After the rigorous five-fold cross-validation and external test set validation, the optimal models for the three endpoints were obtained. For the Ki endpoint, the Bagging-ECFP4 model had a Q2 value of 0.778 and an R2 value of 0.780. For the IC50 endpoint, the XGBoost-ECFP4 model had a Q2 value of 0.806 and an R2 value of 0.784. For the EC50 endpoint, the SVM-Daylight model had a Q2 value of 0.784 and an R2 value of 0.809. These results demonstrate that the constructed models exhibit good predictive performance. In addition, based on the model feature importance analysis, the influence between substructure and biological activity was also explored, which can provide important theoretical guidance for the efficient virtual screening and structural optimization of novel TRPV1 analgesics. And subsequent studies on novel TRPV1 modulators will be based on the feature substructures of the three endpoints.

Advancing electrocatalytic reactions through mapping key intermediates to active sites via descriptors.

Descriptors play a crucial role in electrocatalysis as they can provide valuable insights into the electrochemical performance of energy conversion and storage processes. They allow for the understanding of different catalytic activities and enable the prediction of better catalysts without relying on the time-consuming trial-and-error approaches. Hence, this comprehensive review focuses on highlighting the significant advancements in commonly used descriptors for critical electrocatalytic reactions. First, the fundamental reaction processes and key intermediates involved in several electrocatalytic reactions are summarized. Subsequently, three types of descriptors are classified and introduced based on different reactions and catalysts. These include d-band center descriptors, readily accessible intrinsic property descriptors, and spin-related descriptors, all of which contribute to a profound understanding of catalytic behavior. Furthermore, multi-type descriptors that collectively determine the catalytic performance are also summarized. Finally, we discuss the future of descriptors, envisioning their potential to integrate multiple factors, broaden application scopes, and synergize with artificial intelligence for more efficient catalyst design and discovery.

Novel machine learning approach toward classification model of HIV-1 integrase inhibitors.

HIV-1 (human immunodeficiency virus-1) has been causing severe pandemics by attacking the immune system of its host. Left untreated, it can lead to AIDS (acquired immunodeficiency syndrome), where death is inevitable due to opportunistic diseases. Therefore, discovering new antiviral drugs against HIV-1 is crucial. This study aimed to explore a novel machine learning approach to classify compounds that inhibit HIV-1 integrase and screen the dataset of repurposing compounds. The present study had two main stages: selecting the best type of fingerprint or molecular descriptor using the Wilcoxon signed-rank test and building a computational model based on machine learning. In the first stage, we calculated 16 different types of fingerprint or molecular descriptors from the dataset and used each of them as input features for 10 machine-learning models, which were evaluated through cross-validation. Then, a meta-analysis was performed with the Wilcoxon signed-rank test to select the optimal fingerprint or molecular descriptor types. In the second stage, we constructed a model based on the optimal fingerprint or molecular descriptor type. This data followed the machine learning procedure, including data preprocessing, outlier handling, normalization, feature selection, model selection, external validation, and model optimization. In the end, an XGBoost model and RDK7 fingerprint were identified as the most suitable. The model achieved promising results, with an average precision of 0.928 ± 0.027 and an F1-score of 0.848 ± 0.041 in cross-validation. The model achieved an average precision of 0.921 and an F1-score of 0.889 in external validation. Molecular docking was performed and validated by redocking for docking power and retrospective control for screening power, with the AUC metrics being 0.876 and the threshold being identified at -9.71 kcal mol-1. Finally, 44 compounds from DrugBank repurposing data were selected from the QSAR model, then three candidates were identified as potential compounds from molecular docking, and PSI-697 was detected as the most promising molecule, with in vitro experiment being not performed (docking score: -17.14 kcal mol-1, HIV integrase inhibitory probability: 69.81%).

Data Augmentation for Genetic Programming-Driven Late Merging of HOG and Uniform LBP Features for Texture Classification

The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiveness depending on the specific application. However, it is generally accepted that they complement each other by compensating for their strengths and weaknesses. Therefore, many approaches focused on combining different types of image descriptors generating robust features that are invariant to image transformations. These solutions mostly focused on one way to deal with information and faced more problems as they need human intervention and a large set of data. The acknowledged benefits of combining multiple types of descriptors are accompanied by challenges specially. These challenges arise due to differences in properties, such as locality and sparsity, as well as the heterogeneity exhibited by generated features. To address this issue comprehensively, we present an approach that benefits from genetic programming techniques to generate and combine two distinct texture classifiers. It incorporates histograms of oriented gradients and local binary patterns descriptors, which capture different textures. To effectively fuse the results of both classifiers, the proposed approach employs a late fusion process along with data augmentation methods while using a limited amount of training data. To evaluate the performance of the proposed approach in texture image classification tasks, we have conducted extensive experiments on six challenging datasets encompassing various variations. We have also investigated its performance in a cross dataset problem where the model has been trained on instances of a dataset before being tested on samples of another dataset. The obtained results clearly demonstrate that the proposed approach surpasses other relevant low-level approaches, as well as existing GP-based and CNN methods specifically designed for describing and classifying textures. Thanks to its ability to simultaneously leverage multiple descriptors, the suggested solution shows a high potential for real-world applications, particularly in handling various image changes with robustness.