Scientists have developed several drugs to cure brain-related disorders like Alzheimer's and Parkinson's disease to help humankind. But as of now, the medical fraternity can only cure the symptoms of such diseases and not eradicate them altogether because these medicines cannot cross the blood-brain barrier (BBB) and reach their target (i.e., the brain). Therefore, if certain drug delivery vehicles, such as the blood-brain barrier penetrating peptides (B3P2s), are discovered to cross the BBB, it would greatly benefit society. This article proposes a novel population-based search technique called the hybridized gravitational search algorithm (HyGSA) for discovering and optimizing B3P2s. The HyGSA has two explainable artificial intelligence (AI) modules. The first module is an explainable machine learning model that determines the desirable characteristics or objectives to be optimized by HyGSA while discovering the B3P2s. The second module comprises an explainable deep learning tool tha...

Artificial intelligence (AI) has emerged as a powerful tool in computational biology, where it is being used to analyze large datasets to detect difficult biological patterns. This has enabled the design of new drug molecules. In this article, a novel method called hybridized gravitational search algorithm (HyGSA) has been proposed to design novel blood-brain barrier penetrating peptides (B3P2s) with desirable characteristics that enable them to cross the blood-brain barrier (BBB) and deliver neurological drugs directly to the brain. The HyGSA has two important modules in the form of an explainable machine learning classifier (with an accuracy, f1-score, and area under the ROC curve (AUROC) of 84%, 84%, and 91%, respectively) and an explainable deep learning-based B3P2 classifier (with an accuracy, f1-score, and AUROC of 89%, 91%, and 95%, respectively). The former was used to determine the crucial hand-engineered features, and the latter was designed to determine the critical amino ac...