Drug discovery is a process of recognizing the chemical moieties having the potential to serve as drugs. It involves the higher cost, low efficacy, and increased timelines for discovering a drug which have made it a complex process. Hence, there is an urge need of advancement in drug discovery process which can provide the revolutionary changes. In recent years, deep learning bears promise in the process of drug discovery. Deep learning plays a crucial role in various drug discovery processes namely drug monitoring, peptide synthesis, legend-based virtual screening, toxicity prediction, pharmacophore modeling, quantitative structural–activity relationship (QSAR), poly-pharmacology, drug repositioning, and physiochemical activities. This chapter presents an outline of these expanding topics related to drug discovery, the key concepts of prevalent deep learning algorithms, and motivation to investigate these techniques for their potential applications in computer-assisted drug discovery and design.

Mitochondrial dysfunction is involved in Alzheimer’s disease (AD) pathogenesis. Mitochondria have their own genetic material; however, most of their proteins (∼99%) are synthesized as precursors on cytosolic ribosomes, and then imported into the mitochondria. Therefore, exploring proteome changes in these organelles can yield valuable information and shed light on the molecular mechanisms underlying mitochondrial dysfunction in AD. Here, we review AD-associated mitochondrial changes including the effects of amyloid beta and tau protein accumulation on the mitochondrial proteome. We also discuss the relationship of ApoE genetic polymorphism with mitochondrial changes, and present a meta-analysis of various differentially expressed proteins in the mitochondria in AD.

Area covered: Proteomics studies and their contribution to our understanding of mitochondrial dysfunction in AD pathogenesis.

Expert opinion: Proteomics has proven to be an efficient tool to uncover various aspects of this complex organelle, which will broaden our understanding of mitochondrial dysfunction in AD. Evidently, mitochondrial dysfunction is an early biochemical event that might play a central role in driving AD pathogenesis.

One of the most important aspects of human life is health. Every civilization is paying more attention to the early detection of a disease with its prevention along with implementing technology in the area of healthcare. The use of technology helps in identifying the most effective treatments for a variety of chronic illnesses. Deep learning (DL) and the Internet of Things (IoT) are rapidly expanding and playing an extremely important role in a wide range of applications such as healthcare and medical systems. Getting knowledge and useful insights from sophisticated, high-dimensional, and diverse biological data remain a major problem in healthcare transformation. The various kinds of data, such as electronic health records (EHRs) or electric medical records (EMRs), sensor data, text, and imaging, are all examples of variable, poorly documented, complex, and unstructured data that have emerged in modern biomedical research. As of now, COVID-19 has been wreaking havoc on the world since December 2019. Fortunately, the IoT is among the most significant paradigms in which artificial intelligence (AI) technology, such as big data analysis and cloud computing are playing a critical role in order to stop the spread of the COVID-19 virus. For instance, in the case of diagnosis and remote screening of COVID-19 patients, AI technology based on machine learning (ML) and DL has redefined the flow of work by having minimum contact with patients, moreover, allowing healthcare professionals to make clinical decisions in a more efficient manner, thus, helping in protecting not only patients but also healthcare professionals. Thus, in healthcare, the IoT plays a critical role in providing patients with better medical facilities as well as supporting hospitals and healthcare professionals. Because of IoT-enabled portable medical equipment, smart healthcare surveillance systems are on the rise. This chapter mainly focuses on the various approaches enabled by AI and DL and their significant applications in the field of medical and healthcare.

Diabetic retinopathy (DR) is one of the chronic complications of diabetes. It includes retinal blood vessels' damage. If untreated, it leads to loss of vision. The existing treatment strategies for DR are expensive, invasive, and need expertise during administration. Hence, there is a need to develop a non-invasive topical formulation that can penetrate deep to the posterior segment of retina and treat the damaged retinal vessels. In addition, it should also provide sustained release. In recent years, novel drug delivery systems (NDDS) have been explored for treating DR and found successful. In this study, chitosan (CS) modified 5-Fluorouracil Nanostructured Lipid Carriers (CS-5-FU-NLCs) were prepared by modified melt emulsification-ultrasonication method and optimized by Box-Behnken Design. The size, polydispersity index, zeta potential and entrapment efficiency of CS-5-FU-NLCs were 163.2 ± 2.3 nm, 0.28 ± 1.52, 21.4 ± 0.5 mV and 85.0 ± 0.2 %, respectively. The in vitro drug release and ex vivo permeation study confirmed higher and sustained drug release in CS-5-FU-NLCs as compared to 5-FU solution. HET-CAM Model ensured the non-irritant nature of CS-5-FU-NLCs. In vivo ocular studies of CS-5-FU-NLCs confirmed antiangiogenic effect of 5-FU by CAM model and diabetic retinopathy induced rat model, indicating successful delivery of 5-FU to the retina.