Deep Learning and Site‐Specific Drug Delivery
Dhanalekshmi Unnikrishnan Meenakshi, Nandakumar Selvasudha, Arul Prakash Francis, Pushpa Sweety, Shivkanya Fuloria, Neeraj Kumar Fuloria, Vetriselvan Subramaniyan, Shah Alam Khan · 2022
Site-specific drug delivery [SSDD] is a smart localized and targeted delivery system that is used to improve drug efficiency, decrease drug-related toxicity, and prolong the duration of action by having protected interaction between a drug and the diseased tissue. SSDD system in association with the computational approaches is employed in discovery, design, and development of drugs to improve treatment outcomes. Artificial intelligence [AI] networks and tools are playing a prominent role in developing pharmaceutical products by employing fundamental paradigms. Among many computational techniques, deep learning [DL] technology utilizes artificial neural networks [ANN], belongs to machine learning [ML] approach that holds the key to measuring and forecasting a drug's affinity for specific targets. It can reduce both cost and time by speeding up the drug development process rationally with careful decisions. DL is considered as the primary strategy to predict bioactivity as it shows improved performance compared with other technologies in the field. DL can assist in evaluating the success of a target-based drug design and development before the actual laboratory synthesis or production of the drug molecule. This chapter highlights the potential applications of DL in assigning a specific drug target site by predicting the structure of the target protein and drug affinity for a successful treatment. It also spotlights the impactful applications of many types of DL in SSDD and its advantages over conventional SSDD systems. Furthermore, some formulations that are intended to lead to the target or site-specific delivery and DL role in docking and pharmacokinetics profiling are also addressed. Ongoing challenges, skepticism about the likelihood of success, and the paths to overcome by future technological advancements are also dealt with briefly. Due emphasis is given to the use of DL in reducing the economic burden of pharmaceutical industries to overcome costly failures and in developing target specific new drug candidate[s] for a successful therapeutic regimen beneficial to human life.