Machine Learning‐Driven Design of Multicomponent Bone Inorganic Matrix Mimicking Scaffolds for Osteogenesis Enhanced by Neurogenesis

Bone defects require materials with osteogenic, neurogenic, and angiogenic activity, yet designing such materials within high-dimensional compositional spaces remains challenging. Here, we report a machine learning (ML) driven strategy to accelerate the design of multicomponent mesoporous bioactive glasses (MBG). A dataset of 169 formulations was used to train a Support Vector Machine (SVM) model to classify osteogenic potential into two classes, high (relative ALP activity ≥0.83) and low (relative ALP activity <0.83). The model screened 10 000 candidates and identified 126 as high potential. These 126 candidates were then subjected to experimental mineralization assays using BMSCs, and selected four high-performance candidates (Candidates 2-5) and one lower-performance control (Candidate 1). The corresponding MBG cores (MBG1-MBG5) were incorporated into NGF-loaded silk fibroin shells to fabricate core-shell nanofibrous scaffolds via coaxial electrospinning. Among them, MBG5@SF/NGF exhibited the highest osteogenic activity, promoting BMSCs viability, differentiation, matrix mineralization, and Erk1/2 phosphorylation, along with upregulation of osteogenic and neuro-inductive markers. Transcriptomic sequencing confirmed activation of osteogenic and neurogenic pathways. In mice calvarial defect model, MBG5@SF/NGF reconstructed a neuro-vascular-bone regenerative microenvironment. We conclude that the ML accelerated screening cascade, combined with experimental validation, identified MBG5@SF/NGF as a promising scaffold for functional bone regeneration.