Bioinformatics- and AI-Guided Design of Advanced Drug Delivery Platforms: Predictive Modeling for Rational Construction

The design of drug delivery systems is shifting from an empirical, trial-and-error discipline toward a predictive, rational paradigm driven by bioinformatics, artificial intelligence (AI), and machine learning. By combining multi-omics analysis, molecular simulation, and computational modeling with materials design and synthesis, researchers can now anticipate nano–bio interactions and physicochemical behaviour before a carrier is made. This computation-led approach supports the discovery and optimization of diverse delivery systems, e.g. nanoparticles, lipid-based systems, polymeric carriers, targeted platforms, and stimuli-responsive systems while reducing reliance on repetitive laboratory iteration and accelerating the route to precise, personalized therapy. This Research Topic focuses on emerging interdisciplinary work in which bioinformatics and computational methods guide the design, construction, optimization, and biological evaluation of advanced drug delivery platforms.This Research Topic aims to bring together researchers from bioinformatics, computational science, materials science, pharmaceutics, nanomedicine, and biomedical engineering to show how computational methods can transform the design and construction of drug delivery platforms. Recent advances in these methods have enabled more predictable nanoparticle properties, controllable biodistribution, improved therapeutic outcomes, and reduced toxicity, opening the way to more precise and personalized diagnosis and therapy. We particularly welcome studies in which bioinformatics, AI, machine learning, or computational modeling play a central, methodological role rather than a supporting one in developing innovative delivery systems. By foregrounding the computational contribution, the collection seeks to promote interdisciplinary collaboration and accelerate the development of intelligent, clinically translatable drug delivery platforms.This Research Topic focuses on studies in which bioinformatics, AI, machine learning, and computational modeling play a central role in the design and construction of advanced drug delivery platforms. To keep the collection distinct and rigorous, we especially encourage work where the computational contribution is a core component rather than an add-on. Contributions are welcome across the following areas, but are not limited to them:1) Bioinformatics- and AI-guided design of drug delivery systems (including but not limited to polymeric nanoparticles, lipid nanoparticles, liposomes, micelles, nanogels, hydrogels, and hybrid nanomaterials)2) Predictive modeling of physicochemical properties and biological behaviours of delivery platforms (e.g. particle size, morphology, surface properties, drug loading, encapsulation efficiency, and stability)3) AI-assisted prediction and investigation of the nano–bio interface and biological interactions4) Computational strategies for targeted and stimuli-responsive delivery, including precision and personalized approaches5) AI-based prediction and optimization of formulation composition, biodistribution, and therapeutic outcome6) Predictive toxicology, safety assessment, and computational approaches supporting translation from bench to clinic Topic editors: Kuikun Yang, Chao Feng, Beibei Xie Source: https://www.frontiersin.org/research-topics/85481/bioinformatics--and-ai-guided-design-of-advanced-drug-delivery-platforms-predictive-modeling-for-rational-construction