SAR and QSAR models of cyclooxygenase-1 (COX-1) inhibitors

Identification and mechanistic study of piceatannol as a natural xanthine oxidase inhibitor

Natural Xanthine oxidase (XOD) inhibitors represent promising therapeutic agents for hyperuricemia (HUA) treatment due to their potent efficacy and favorable safety profiles. This study involved the construction of a comprehensive database of 315 XOD inhibitors and development of 28 machine learning-based QSAR models. The ChemoPy light gradient boosting machine model exhibited the best performance (AUC = 0.9371 and MCC = 0.7423). This model identified three potential XOD inhibitors from the FooDB database: daphnetin, 7-hydroxycoumarin, and piceatannol. Molecular docking and dynamics simulations revealed favorable interactions, with piceatannol showing a remarkable stability through hydrogen bonding and hydrophobic interactions. ADME predictions suggested that all three compounds possess desirable drug-like properties and safety characteristics. Subsequent in vitro enzyme inhibition assays validated computational predictions, with piceatannol exhibiting the strongest inhibitory activity (IC50 = 8.80 ± 0.05 μM). Multispectroscopic analyses revealed that piceatannol-XOD binding was predominantly mediated by hydrogen bonding and van der Waals forces, which induced conformational changes characterized by decreased α-helical content and increased proportions of β-sheets, β-turns, and random coils. This study presents an efficient strategy for the identification of natural XOD inhibitors, elucidates the molecular mechanism of piceatannol-mediated XOD inhibition, and establishes a foundation for its therapeutic application in HUA treatment.

Synthesis, molecular dynamics simulation, and evaluation of biological activity of novel flurbiprofen and ibuprofen-like compounds

The frequent use of anti-inflammatory drugs and the side effects of existing drugs keep the need for new compounds constant. For this purpose, flurbiprofen and ibuprofen-like compounds, which are frequently used anti-inflammatory compounds in this study, were synthesized and their structures were elucidated. Like ibuprofen and flurbiprofen, the compounds contain a residue of phenylacetic acid. On the other hand, it contains a secondary amine residue. Thus, it is planned to reduce the acidity, which is the biggest side effect of NSAI drugs, even a little bit. The estimated ADME parameters of the compounds were evaluated. Apart from internal use, local use of anti-inflammatory compounds is also very important. For this reason, the skin permeability values of the compounds were also calculated. And it has been found to be compatible with reference drugs. The COX enzyme inhibitory effects of the obtained compounds were tested by in vitro experiments. Compound 2a showed significant activity against COX-1 enzyme with an IC50 = 0.123 + 0.005 μM. The interaction of the compound with the enzyme active site was clarified by molecular dynamics studies.

Machine learning methods applied for the prediction of biological activities of triple reuptake inhibitors

Major depressive disorder (MDD) is characterized by a series of disabling symptoms like anhedonia, depressed mood, lack of motivation for daily tasks and self-extermination thoughts. The monoamine deficiency hypothesis states that depression is mainly caused by a deficiency of monoamine at the synaptic cleft. Thus, major efforts have been made to develop drugs that inhibit serotonin (SERT), norepinephrine (NET) and dopamine (DAT) transporters and increase the availability of these monoamines. Current gold standard treatment of MDD uses drugs that target one or more monoamine transporters. Triple reuptake inhibitors (TRIs) can target SERT, NET, and DAT simultaneously, and are believed to have the potential to be early onset antidepressants. Quantitative structure-activity relationship models were developed using machine learning algorithms in order to predict biological activities of a series of triple reuptake inhibitor compounds that showed in vitro inhibitory activity against multiple targets. The results, using mostly interpretable descriptors, showed that the internal and external predictive ability of the models are adequate, particularly of the DAT and NET by Random Forest and Support Vector Machine models. The current work shows that models developed from relatively simple, chemically interpretable descriptors can predict the activity of TRIs with similar structure in the applicability domain using ML methods.Communicated by Ramaswamy H. Sarma.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

SAR and QSAR research on tyrosinase inhibitors using machine learning methods

Tyrosinase is a key rate-limiting enzyme in the process of melanin synthesis, which is closely related to human pigmentation disorders. Tyrosinase inhibitors can down-regulate tyrosinase to effectively reduce melanin synthesis. In this work, we conducted structure-activity relationship (SAR) study on 1097 diverse mushroom tyrosinase inhibitors. We applied five kinds of machine learning methods to develop 15 classification models. Model 5B built by fully connected neural networks and ECFP4 fingerprints achieved the highest prediction accuracy of 91.36% and Matthews correlation coefficient (MCC) of 0.81 on the test set. The applicability domains (AD) of classification models were defined by d S T D - P R O method. Moreover, we clustered the 1097 inhibitors into eight subsets by K-Means to figure out inhibitors' structural features. In addition, 10 quantitative structure-activity relationship (QSAR) models were constructed by four machine learning methods based on 813 inhibitors. Model 6 J, the best QSAR model, was developed by fully connected neural networks with 50 RDKit descriptors. It resulted in a coefficient of determination (r ²) of 0.770 and a root mean squared error (RMSE) of 0.482 on the test set. The AD of Model 6 J was visualized by Williams plot. The models built in this study can be obtained from the authors.

Fingerprint-based computational models of 5-lipo-oxygenase activating protein inhibitors: Activity prediction and structure clustering

Inflammatory diseases can be treated by inhibiting 5-lipo-oxygenase activating protein (FLAP). In this study, a data set containing 2,112 FLAP inhibitors was collected. A total of 25 classification models were built by five machine learning algorithms with five different types of fingerprints. The best model, which was built by support vector machine algorithm with ECFP_4 fingerprint had an accuracy and a Matthews correlation coefficient of 0.862 and 0.722 on the test set, respectively. The predicted results were further evaluated by the application domain d_STD-PRO (a distance between one compound to models). Each compound had a d_STD-PRO value, which was calculated by the predicted probabilities obtained from all 25 models. The application domain results suggested that the reliability of predicted results depended mainly on the compounds themselves rather than algorithms or fingerprints. A group of customized 10-bit fingerprint was manually defined for clustering the molecular structures of 2,112 FLAP inhibitors into eight subsets by K-Means. According to the clustering results, most of inhibitors in two subsets (subsets 2 and 4) were highly active inhibitors. We found that aryl oxadiazole/oxazole alkanes, biaryl amino-heteroarenes, two aromatic rings (often N-containing) linked by a cyclobutene group, and 1,2,4-triazole group were typical fragments in highly active inhibitors.

Classification of Cyclooxygenase-2 Inhibitors Using Support Vector Machine and Random Forest Methods

This work reports the classification study conducted on the biggest COX-2 inhibitor data set so far. Using 2925 diverse COX-2 inhibitors collected from 168 pieces of literature, we applied machine learning methods, support vector machine (SVM) and random forest (RF), to develop 12 classification models. The best SVM and RF models resulted in MCC values of 0.73 and 0.72, respectively. The 2925 COX-2 inhibitors were reduced to a data set of 1630 molecules by removing intermediately active inhibitors, and 12 new classification models were constructed, yielding MCC values above 0.72. The best MCC value of the external test set was predicted to be 0.68 by the RF model using ECFP_4 fingerprints. Moreover, the 2925 COX-2 inhibitors were clustered into eight subsets, and the structural features of each subset were investigated. We identified substructures important for activity including halogen, carboxyl, sulfonamide, and methanesulfonyl groups, as well as the aromatic nitrogen atoms. The models developed in this study could serve as useful tools for compound screening prior to lab tests.