Advancing AI-Driven Approaches for Enhanced Prediction and Analysis of Drug-Protein Interactions

A major contributor to the process of drug distribution in the body is the affinity of the compounds for plasma proteins and bodily tissues. Pharmacokinetics Systemic circulation is the absorption site of drugs in the body, mainly existing in the form of drugs-complex and free. There is a dynamic equilibrium between bound drugs and free drugs. Free or unbound drugs readily cross membrane barriers. The bound drugs are still used as depots and slowly released to free drug. One of the most important physicochemical properties that can influence pharmacokinetics and therapeutic efficacy is the extent to which drug bind to plasma proteins. So, the fraction bound to plasma protein is one of the main determinants of drug distribution. Human serum comprises approximately 60% of the total plasma proteins in the blood. Proteins such as Albume (HSA) can bind various distinct substrates and the latter potentially affecting the %PPB. Classic methods for determining PPB, such as equilibrium dialysis and ultra-filtration, typically operate on time scales of minutes to hours, ruling them out for determination of compounds that undergo significant chemical degradation over time. Moreover, such methods are tedious and cannot be automated. There have been several attempts to develop computational models for prediction of chemical binding to plasma proteins. The present work aimed at developing prediction models on available Artificial Intelligence (AI) based algorithms that can accurately predict the drug-plasma protein interaction. Here are a few aspects and trends that should come in the future as technology and research in this field advance: Deep learning neural network Graph neural network Multi-modal data integration Explainable (AI) Generative models Transfer learning Personalized Medicine Data augmentation and synthesis High-Performance computing Ethical and Regulatory considerations.