Machine Learning-Driven Methods for Nanobody Affinity Prediction

Because of their high affinity, specificity, and environmental stability, nanobodies (Nbs) have continuously received attention from the field of biological research. However, it is tough work to obtain high-affinity Nbs using experimental methods. In the current study, 12 machine learning algorithm...

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Bibliographic Details
Main Author:	Hua Feng (234718) (author)
Other Authors:	Xuefeng Sun (408801) (author), Ning Li (45258) (author), Qian Xu (119888) (author), Qin Li (36669) (author), Shenli Zhang (3236712) (author), Guangxu Xing (336317) (author), Gaiping Zhang (106747) (author), Fangyu Wang (4279168) (author)
Published:	2024
Subjects:	Biophysics Biochemistry Molecular Biology Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified continuously received attention eight noncovalent interactions true affinitive nbs showed higher sensitivity predicting nonaffinitive nbs mcc values ). good model robustness future nonaffinitive nbs two stacked models key noncovalent interactions nanobody affinity prediction effectively predict whether current study provides four optimized models effectively predict current study optimized models models showed associated interactions model comparison key factors 5 ). tough work potential patterns obtain high nine uncorrelated nb drugs machine learning intended ligands hydrogen bonding high affinity first time f1 score environmental stability driven methods design process could improve correlation coefficient biological research
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Machine Learning-Driven Methods for Nanobody Affinity Prediction

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