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

Authors

Kunlu Lin, Ying Yang, Chuyao Zhou, Hanyue Mao, Zhiquan Zhang, Libangxi Liu, Xiaoying Yu, Haoming Liu, Long Liu, Xiaoyan Wang

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e77084. Aug 27, 2026. Epub Aug 27, 2026.

Abstract

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.

PMID:
42656053
Bibliographic data and abstract were imported from PubMed on 27 Aug 2026.

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