Diabetes mellitus (DM) manifests as a complex and chronic metabolic disorder, posing a significant threat to global public health and contributing substantially to mortality rates. Characterized by elevated blood glucose levels or hyperglycemia and requires effective preventive and therapeutic strategies. One promising approach involves targeting the inhibition of α-glucosidase and α-amylase, key enzymes responsible for carbohydrate hydrolysis. Inhibiting these enzymes proves beneficial in reducing postprandial glucose levels and mitigating postprandial hyperglycemia. However, existing antidiabetic medications are associated with undesirable side effects, highlighting the need to develop new molecules with increased efficacy and reduced side effects. Traditional methods for designing such molecules are often lengthy and costly. To address this, computer-based molecular modeling tools offer a promising approach to evaluate the anti-diabetic activities of chemical compounds. This review aims to compile information on chemical compounds assessed for their anti-diabetic activities through molecular modeling, with a particular focus on the period from 2020 to 2023.

Growth factors and their receptor tyrosine kinases play a central role in regulating vital cellular processes such as proliferation, differentiation, division, and cell survival, and they are closely associated with the development of various types of cancer, particularly in the context of angiogenesis. Although several small chemical compounds targeting tyrosine kinase receptors have been approved by the FDA for cancer treatment by inhibiting angiogenesis, there is still a need for more effective medications. In silico studies are now crucial tools for the design of new drugs, offering considerable advantages such as cost and time reduction. In this review, we examined recent in silico research carried out between 2022 and 2024, focusing on new drug candidates synthesized to fight cancer, in particular by targeting tyrosine kinase receptors involved in the process of angiogenesis.

This review explores the transformative impact of AI on drug development and delivery in pharmaceutical sciences, spanning formulation design, real-time monitoring, targeted delivery, and future prospects. The rational design of smart drug carriers, informed by machine learning, optimizes formulations for individual patient needs. AI-driven sensors enable adaptive drug administration, enhancing precision. Despite promises, challenges like biocompatibility, regulations, and ethics persist. Interdisciplinary collaboration and transparent communication are crucial for responsible AI adoption. Anticipated trends include personalized dosage optimization and intelligent nanocarriers. The review underscores AI's potential in reshaping pharmaceuticals for patient-centric care while addressing challenges for widespread adoption.