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There is a strong interest in the development of microsomal prostaglandin E2 synthase‐1 (mPGES‐1) inhibitors of their potential to safely and effectively treat inflammation. Herein, 70 QSAR models were built on the dataset (735 mPGES‐1 inhibitors) characterized with RDKit descriptors by multiple linear regression (MLR), support vector machine (SVM), random forest (RF), deep neural networks (DNN), and eXtreme Gradient Boosting (XGBoost). The other three regression models on the dataset are represented by SMILES using self‐attention recurrent neural networks (RNN) and Graph Convolutional Networks (GCN). For the best model (Model C2), which was developed by SVM with RDKit descriptors, the coefficient of determination (R2) of 0.861 and root mean squared error (RMSE) of 0.235 were achieved for the test set. Additionally, R2 of 0.692 and RMSE of 0.383 were obtained on the external test set. We investigated the applicability domain (AD) of Model C2 with the rivality index (RI), the prediction of Model C2 on 78.92% of molecules in the test set, and 78.33% of molecules in the external test set were reliable. After dissecting the RDKit descriptors of Model C2, we found important physicochemical properties of highly active mPGES‐1 inhibitors. Besides, by analyzing the attention weight of each atom of each inhibitor from the attention layer, we found that the benzamide group and the trifluoromethyl cyclohexane group are favorable substructures for mPGES‐1 inhibitors. [ABSTRACT FROM AUTHOR]

Predictive QSAR models for the search of new adenosine A2A receptor antagonists were developed by using OCHEM platform. The predictive ability of the regression models has coefficient of determination q2 = 0.65−0.71 with cross‐validation and independent test set. The inhibition activities of novel fused 7‐deazaxanthine compounds were predicted by the developed QSAR models. A preparative method for the synthesis of pyrimido[5′,4′:4,5]pyrrolo[1,2‐a][1,4]diazepine derivatives was developed, and 11 new adenosine A2A receptor antagonists were obtained. Preliminary investigations into the toxicology of fused 7‐deazaxanthine compounds toward commonly used model organism to assess toxicity invertebrate cladoceran D. magna were also described. [ABSTRACT FROM AUTHOR]

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved. [ABSTRACT FROM AUTHOR]

Microbial resistance to conventional antibiotics has led to a surge in antimicrobial peptide (AMP) rational design initiatives that rely heavily on algorithms with good prediction accuracy and sensitivity. We present a quantitative structure‐activity relationship (QSAR) approach for predicting activity of cathelicidins, an AMP family with broad‐spectrum activity. The best multiple linear regression model built against Escherichia coli ATCC 25922 could accurately predict activity of three rationally designed peptides CP, DP, and Mapcon, having high sequence similarity. On further experimental validation of the rationally designed peptides, CP was found to exhibit high antimicrobial activity with negligible hemolysis. Here, we provide CP, an AMP with potential therapeutic applications and a family‐based QSAR model for AMP prediction. [ABSTRACT FROM AUTHOR]

Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-nearest neighbor procedures. The classification QSAR models were constructed using WEKA- RF (random forest) method. The predictive ability of the models was tested by fivefold cross-validation; giving q2 = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation. The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials. [ABSTRACT FROM AUTHOR]

A quantitative structure–activity relationship study has been carried out, in which the relationship between the peroxisome proliferator-activated receptor α and the peroxisome proliferator-activated receptor γ agonistic activities of thiazolidinedione and oxazolidinedione derivatives and quantitative descriptors, Vsite calculated in a receptor-dependent manner is modeled. These descriptors quantify the volume occupied by the optimized ligands in regions that are either common or specific to the superimposed binding sites of the targets under consideration. The quantitative structure–activity relationship models were built by forward stepwise linear regression modeling for a training set of 27 compounds and validated for a test set of seven compounds, resulting in a squared correlation coefficient value of 0.90 for peroxisome proliferator-activated receptor α and of 0.89 for peroxisome proliferator-activated receptor γ. The leave-one-out cross-validation and test set predictability squared correlation coefficient values for these models were 0.85 and 0.62 for peroxisome proliferator-activated receptor α and 0.89 and 0.50 for peroxisome proliferator-activated receptor γ respectively. A dual peroxisome proliferator-activated receptor model has also been developed, and it indicates the structural features required for the design of ligands with dual peroxisome proliferator-activated receptor activity. These quantitative structure–activity relationship models show the importance of the descriptors here introduced in the prediction and interpretation of the compounds affinity and selectivity. [ABSTRACT FROM AUTHOR]