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BACKGROUND The traditional screening method for umami peptide, extracted from porcine bone, was labor-intensive and time-consuming. In this study, the rapid screening method and molecular mechanism of umami peptide was investigated. RESULTS This paper showed that a more precisely rapid screening method with composite machine learning and molecular docking was used to screen the potential umami peptide from porcine bone. As reference, 24 reported umami peptides were predicated by composite machine learning, with the accuracy of 86.7%. In this study, potential umami peptide sequences from porcine bone were screened by UMPred-FRL, Umami-MRNN Demo, and molecular docking was used to go for further screening. Finally, 9 peptides were screened and verified as umami peptides by this method: LREY、HEAL、LAKVH、FQKVVA、HVKELE、AEVKKAP、EAVEKPQS、KALSEEL and KKMFETES. The hydrogen bonding was deemed to main interaction force with receptor T1R3, and domain binding sites were Ser146, His121 and Glu277. The result demonstrated the feasibility of machine learning assisted T1R1/T1R3 receptor for rapid screening umami peptides. The screening method would not only adapt to screen umami peptides from porcine bone but possibly applied for other sources. It was also provided a reference for rapid screening of umami peptides. CONCLUSION The manuscript lays a rapid screening method in screening umami peptide, and 9 umami peptides from porcine bone were screened and identified. This article is protected by copyright. All rights reserved.