Authors:
Nasser Thallaj, Ela Vashishtha

Addresses:
1Department of Medicinal Chemistry and Quality Control, Faculty of Pharmacy, Al-Rachid Privet University, Damascus, Syria. 2Healthcare Planning and Strategy Leader, Texas Health Resources, Texas, United States of America. profthallaj@gmail.com1, elavashishtha@texashealth.org2

Abstract:

These spacers can create tailor-made recognition motifs that recognize specific molecules or molecular structures. The bis-porphyrin nucleoside spacers allow for greater flexibility and accuracy in the design of recognition motifs, leading to improved molecular recognition. This makes it possible to create custom recognition motifs for various applications, such as drug target identification and drug delivery. Bis-porphyrin nucleoside spacers can also build complicated recognition motifs with many recognition sites. This allows the creation of highly precise recognition motifs for many targets, improving medication delivery and efficacy. This can drastically cut drug dosage, lowering adverse effects and expense. High stability makes recognition motifs appropriate for long-term applications. Several disorders have been treated with encouraging outcomes using this technology. It could change medicine delivery and improve focused treatments. This technology will grow more significant as more applications are developed to serve more people. It could transform the healthcare business and improve disease treatment. It could save time and money by simplifying therapies and increasing patient outcomes. It can also lower treatment costs and make them more accessible. It can also help doctors make better judgments and deliver better care by providing more accurate and timely patient insights. More dependable and accurate than traditional treatments, AI-driven medical treatments reduce errors and patient harm. Doctors and healthcare providers are increasingly using AI-driven medicinal treatments. AI-driven medical treatments are cheaper than traditional ones, saving healthcare providers money. AI-driven treatments can also speed up diagnosis and treatment, saving time and money. 

Keywords: Light-Harvesting; Reaction Center; Antenna Effect; Electron Transfer; Dexter Type; Förster Type; Doctors and Healthcare; AI-Driven Medical Treatments.

Received on: 19/11/2022, Revised on: 17/01/2023, Accepted on: 11/03/2023, Published on: 05/04/2023

FMDB Transactions on Sustainable Health Science Letters, 2023 Vol. 1 No. 2, Pages: 54-69

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