Central nervous system (CNS) therapeutics remain a defining challenge for nanomedicine because successful delivery requires nanocarriers to navigate a route-dependent cascade of biological barriers, including systemic clearance, peripheral organ sequestration, blood–brain barrier (BBB) interaction or bypass, CNS tissue penetration, target-cell internalization, and functional payload availability. This Perspective critically compares lipid nanoparticles (LNPs) and exosome-based systems as two leading non-viral nanocarrier platforms for CNS therapeutics. Rather than seeking a universal winner, we frame them as complementary delivery philosophies: engineering-led controllability for LNPs and biology-led functionality for exosome-based systems. We argue that CNS nanocarrier success must be defined by functional delivery rather than mere brain accumulation. LNPs offer scalable manufacturing, efficient nucleic acid encapsulation, tunable lipid chemistry, and clinical validation, but remain limited by peripheral clearance, restricted BBB penetration, and inefficient endosomal escape. Exosome-based systems offer endogenous communication pathways, membrane recognition, and biological interface functions, but their translation is hindered by source heterogeneity, difficult isolation and drug loading, batch inconsistency, endogenous cargo concerns, and regulatory ambiguity. Parkinson’s disease is used as a representative stress-test indication to illustrate how disease stage, target cell, therapeutic cargo, and route of administration should dictate platform choice. The field must move beyond universal platform ranking toward mechanism-matched selection and matched functional benchmarking. Future progress may depend on convergent designs that address defined delivery bottlenecks and combine LNP manufacturing rigor and payload control with exosome-inspired biological interface functions. Such precision hybrid nanocarriers should be tailored to specific CNS disease biology, delivery routes, and clinical realities.

RNA therapeutics, spanning messenger RNA vaccines, small interfering RNAs, antisense oligonucleotides, and aptamers, are advancing rapidly, yet the design space that governs whether an RNA drug succeeds remains difficult to navigate. Its principal determinants, namely sequence, chemical modification, secondary structure, delivery vehicle, and tissue tropism, are strongly coupled, and together they constitute a combinatorial space far too large for human heuristics or isolated experiments to explore effectively. Here, we envisage a platform that brings two complementary advances into concert. First, RNA and molecular foundation models can compress this design space into a learnable representation that generalizes across RNA molecules and the carriers that deliver them. Second, a lab-in-the-loop architecture narrows the gap between prediction and biological function by coupling model-generated proposals to automated wet-laboratory experiments, allowing the model to be continually recalibrated against domain-specific feedback. Calibrated uncertainty connects these components: it enables the loop to prioritize candidates whose measurement is expected to be most informative. A platform built on these principles could support joint optimization of sequence and modification, co-design of payload and delivery vehicle, off-target prediction and in silico candidate ranking, and could ultimately bring the rational design of personalized therapies within reach.