Identification of kinase inhibitors that rule out the CYP27B1-mediated activation of vitamin D: an integrated machine learning and structure-based drug designing approach

MLASM: Machine learning based prediction of anticancer small molecules

Cancer, being the second leading cause of death globally. So, the development of effective anticancer treatments is crucial in the field of medicine. Anticancer peptides (ACPs) have shown promising therapeutic potential in cancer treatment compared to traditional methods. However, the process of identifying ACPs through experimental means is often time-intensive and expensive. To overcome this issue, we employed a machine learning-based approach for the first time to develop an anticancer model using small molecules. Anticancer small molecules (ACSMs) are compounds that have been developed to target and inhibit cancer cells. In this study, we used 10,000 compounds to develop the machine learning models using five algorithms such as, Random Forest (RF), Light gradient boosting machine (LightGBM), K-nearest neighbors (KNN), Decision tree (DT) and Extreme Gradient Boosting (XGB). The developed models were evaluated using the test set and top three models were identified (RF, LightGBM and XGB). Furthermore, to validate the predictive performance of our models, we have performed external validation using an FDA approved anticancer compounds/drugs. Following this analysis, we found that our LightGBM model correctly predicted 9 compounds as active. However, RF and XGB exhibited some limitations by predicting 8 and 7 compounds as active out of 10, respectively. These results demonstrate that, when compared to RF and XGB, the LightGBM model showcase robust prediction capabilities, achieving a superior accuracy of 79% with an AUC of 0.88. These findings provide promising insights into the potential of our approach for predicting anticancer small molecules, highlighting the role of machine learning in advancing cancer treatment research.

Artificial intelligence and machine learning in clinical pharmacological research

BACKGROUND

Clinical pharmacology research has always involved computational analysis. With the abundance of drug-related data available, the integration of artificial intelligence (AI) and machine learning (ML) methods has emerged as a promising way to enhance clinical pharmacology research.

METHODS

Based on an accepted definition of clinical pharmacology as a field of research dealing with all aspects of drug-human interactions, the analysis included publications from institutes specializing in clinical pharmacology. Research topics and the most used machine learning methods in clinical pharmacology were retrieved from the PubMed database and summarized.

RESULTS

ML was identified in 674 publications attributed to clinical pharmacology research, with a significant increase in publication activity over the last decade. Notable research topics addressed by ML/AI included Covid-19-related clinical pharmacology research, clinical neuropharmacology, drug safety and risk assessment, clinical pharmacology related to cancer research, and antimicrobial and antiviral research unrelated to Covid-19. In terms of ML methods, neural networks, random forests, and support vector machines were frequently mentioned in the abstracts of the retrieved papers.

CONCLUSIONS

ML, and AI in general, is increasingly being used in various research areas within clinical pharmacology. This report presents specific examples of applications and highlights the most used ML methods.