The large collection of known and experimentally verified compounds from the ChEMBL database was used to build different classification models for predicting the antimalarial activity against Plasmodium falciparum. Four different machine learning methods, namely the support vector machine (SVM), random forest (RF), k-nearest neighbour (kNN) and XGBoost have been used for the development of models using the diverse antimalarial dataset from ChEMBL. A well-established feature selection framework was used to select the best subset from a larger pool of descriptors. Performance of the models was rigorously evaluated by evaluation of the applicability domain, Y-scrambling and AUC-ROC curve. Additionally, the predictive power of the models was also assessed using probability calibration and predictiveness curves. SVM and XGBoost showed the best performances, yielding an accuracy of ~85% on the independent test set. In term of probability prediction, SVM and XGBoost were well calibrated. Total gain (TG) from the predictiveness curve was more related to SVM (TG = 0.67) and XGBoost (TG = 0.75). These models also predict the high-affinity compounds from PubChem antimalarial bioassay (as external validation) with a high probability score. Our findings suggest that the selected models are robust and can be potentially useful for facilitating the discovery of antimalarial agents.

Head and neck squamous cell carcinoma (HNSCC) continues to be a global public health burden even after a tremendous development in its treatment. It is a heterogeneous cancer of upper aero-digestive tract. The contemporary strategy to treat cancer is the use of anticancer drugs against proteins possessing abnormal expression. Targeted chemotherapy was found successful in HNSCC, but, there is still a stagnant improvement in the survival rates and high recurrence rates due to undesirable chemotherapy reactions, non-specificity of drugs, resistance against drugs and drug toxicity on non-cancerous tissues and cells. Various extensive studies lead to the identification of drug targets capable to treat HNSCC effectively. The current review article gives an insight into these promising anticancer targets along with knowledge of drugs under various phases of development. In addition, new potential targets that are not yet explored against HNSCC are also described. We believe that exploring and developing drugs against these targets might prove beneficial in treating HNSCC.

Background/Objective: This study aimed to develop a predictive model to classify and rank highly active compounds that inhibit HIV-1 integrase (IN). Methods: A total of 2271 potential HIV-1 inhibitors were selected from the ChEMBL database. The most relevant molecular descriptors were identified using a hybrid GA-SVM-RFE approach. Predictive models were built using Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Multi-Layer Perceptron (MLP). The models underwent a comprehensive evaluation employing calibration, Y-randomization, and Net Gain methodologies. Results: The four models demonstrated intense calibration, achieving an accuracy greater than 0.88 and an area under the curve (AUC) exceeding 0.90. Net Gain at a high probability threshold indicates that the models are both effective and highly selective, ensuring more reliable predictions with greater confidence. Additionally, we combine the predictions of multiple individual models by using majority voting to determine the final prediction for each compound. The Rank Score (weighted sum) serves as a confidence indicator for the consensus prediction, with the majority of highly active compounds identified through high scores in both the 2D descriptors and ECFP4-based models, highlighting the models’ effectiveness in predicting potent inhibitors. Furthermore, cluster analysis identified significant classes associated with vigorous biological activity. Conclusions: Some clusters were found to be enriched in highly potent compounds while maintaining moderate scaffold diversity, making them promising candidates for exploring unique chemical spaces and identifying novel lead compounds. Overall, this study provides valuable insights into predicting integrase binders, thereby enhancing the accuracy of predictive models.

Among the major altered pathways in head and neck squamous cell carcinoma, AKT/mTORC1/S6K and NRF2/KEAP1 pathway are quite significant. The overexpression and overstimulation of proteins from both these pathways makes them the promising candidates in cancer therapeutics. Inhibiting mTOR has been in research from past several decades but the tumour heterogeneity, and upregulation of several compensatory feed-back mechanisms, encourages to explore other downstream targets for inhibiting the pathway. One such downstream effectors of mTOR is S6K2. It is reported to be overexpressed in cancers such as head and neck cancer, breast cancer and prostate cancer. In case of NRF2/KEAP1 pathway, nuclear factor erythroid 2-related factor 2 (NFE2L2 or NRF2) is overexpressed in ∼90% of head and neck squamous cell carcinoma (HNSCC) cases. It associates with poor survival rate and therapeutic resistance in HNSCC treatment. NRF2 pathway is the primary antioxidant pathway in the cell which also serves pro-tumorigenic functions, such as repression of apoptosis, cell proliferation support and chemoresistance. The aim of this work was to explore S6K2 and NRF2 and identify novel and potential inhibitors against them for treating head and neck squamous cell carcinoma. Since the crystal structure of S6K2 was not available at the time of this study, we modelled its structure using homology modelling and performed high throughput screening, molecular dynamics simulations, free energy calculations and protein-ligand interaction studies to identify the inhibitors. We identified natural compounds Crocin and Gypenoside XVII against S6K2 and Chebulinic acid and Sennoside A against NRF2. This study provides a significant in-depth understanding of the two studied pathways and therefore can be used in the development of potential therapeutics against HNSCC. Communicated by Ramaswamy H. Sarma