Exploring Anti-osteoporosis Medicinal Herbs using Cheminformatics and Deep Learning Approaches.

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Abstract

Osteoporosis is a prevalent disease for the aged population. Chinese herb-derived natural compounds have anti-osteoporosis effects. Due to the complexity of chemical ingredients and natural products, it is necessary to develop a high-throughput approach with the integration of cheminformatics and deep-learning methods to explore their mechanistic action, especially herb/drug-gene interaction networks.Ten medicinal herbs for clinical osteoporosis treatment were selected. Chemical ingredients of top 10 herbs were retrieved from TCMIO database, and their predicted targets were obtained from SEA server. Anti-osteoporosis clinical drugs and targets were collected from multi-databases. Chemical space, fingerprint similarity, and scaffold comparison of the compounds between herbs and clinical drugs were analyzed by RDKit and SKlearn. A network of herb-ingredient-target were constructed via Gephi, and GO and KEGG enrichment analysis were performed using clusterProfiler. Additionally, the bioactivity of compounds and targets were predicted by DeepScreening. Molecular docking of YYH flavonoids to HSD17B2 was accomplished by AutoDockTools.Cheminformatics result depicts a pharmacological network consisting of 89 active components and 30 potential genes. The chemical structures of plant steroids, flavonoids, and alkaloids are key components for anti-osteoporosis effects. Moreover, bioinformatics result demonstrates that the active components of herbs mainly participate in steroid hormone biosynthesis and the TNF signaling pathway. Finally, deep-learning-based regression models were constructed to evaluate 22 anti-osteoporosis-related protein targets and predict the activity of 1350 chemical ingredients of the 10 herbs.The combination of cheminformatics and deep-learning approaches sheds light on the exploration of medicinal herbs mechanisms, and the identification of novel and active compounds from medical herbs in complex molecular systems.Copyright© Bentham Science Publishers; For any queries, please email at [email protected].