The increasing global incidence of cancer emphasizes the vital role of machine learning algorithms and artificial intelligence (AI) in identifying novel anticancer targets and developing new drugs. Computational approaches can significantly quicken research on complex disorders, enabling the discovery of effective treatments. This study explores anticancer targets by assessing the potential of naturally occurring compounds derived from various plants to cure colorectal cancer. Twenty compounds were sourced from PubChem, and the RPS20 protein structure was obtained from AlphaFold, and mutation “V50S” was added. Validation of mutated RPS20 protein was performed using the Ramachandran plot and ERRAT. Binding sites on the mutated RPS20 protein were identified with DeepSite, followed by virtual screening to pinpoint the most promising natural lead drug candidate. Indirubin emerged as the lead drug candidate, fulfilling all ADMET criteria and exhibiting a good binding affinity. Further development included designing an AI-based drug using the WADDAICA server, which was validated through molecular docking, molecular dynamics (MD) simulation, and MMGBSA. The electronic properties of indirubin were studied using DFT calculations. The results show a moderate HOMO-LUMO gap, indicating its potential reactivity and the possible capability for biological target interactions. These findings indicate that indirubin could serve as a potent and effective cancer inhibitor, offering high efficacy with minimal side effects.

This review will examine how artificial intelligence, profound learning technologies, has affected drug discovery. Deep learning technology (DLT), a sub-field of AI that uses intricate algorithms and enormous datasets, is transforming every point along the road to drug development. Integrating clinical trial data, target identification or lead optimization, and personalized medicine have all become possible thanks to DLT. Given the explosion in IUPAC-compliant compounds registered with PubChem or derived from existing ones, DLT has given the pharmaceutical industry a massive booster shot. We will explore the key role generative models play in creating new drug compounds and why interdisciplinary collaboration is essential to entirely using AI's potential for drug discovery. In addition, the purpose of this article is to consider further perspectives concerning what problems exist at present in deep learning and AI-driven drug discovery. We focus on its potential as an accelerated, more effectiveeven tailored healthcare technology. As AI technology advances, a new field emerges in drug development, tipping the global balance between 'well' and 'ill.'

Dengue fever, which is caused by the dengue virus and primarily disseminated by Aedes mosquitoes, constitutes a significant global health issue, indicating 400 million infections and 22,000 fatalities each year. The clinical presentation of the disease varies widely, encompassing both asymptomatic manifestations and severe forms such as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS), particularly during secondary infections attributable to antibody-dependent enhancement (ADE). The increasing incidence is influenced by several factors, including climate change, globalization, and urbanization, resulting in recurrent epidemics, particularly in Southeast Asia and the Indian subcontinent. The current diagnostic methodologies encounter difficulties, often intersecting with other medical conditions, thereby necessitating the implementation of advanced techniques for precise identification. Management predominantly entails supportive care and traditional interventions, while substantial deficiencies persist in the realm of effective therapeutic alternatives and vaccine innovation. Notwithstanding advancements with live attenuated vaccines, a universally effective vaccine has yet to be achieved. Ongoing research is imperative to confront these challenges and establish effective preventive measures against dengue fever.