Identification of amino acids with sensitive nanoporous MoS<sub>2</sub>: towards machine learning-based prediction
Identification of amino acids with sensitive nanoporous MoS2: towards machine learning-based predictionIdentification of amino acids with sensitive nanoporous MoS<sub>2</sub>: towards machine learning-based prediction, Published online: 24 May 2018; doi:10.1038/s41699-018-0060-8Molecular dynamics simulations combined with machine learning techniques enable the prediction of MoS2 nanopore sequencing capabilities. A team led by N. R. Aluru at the University of Illinois at Urbana-Champaign used logistic regression, nearest neighbor, and random forest classifiers to develop a machine learning-based platform capable of predicting the sensing capabilities of nanoporous, atomically thin MoS2. The material was shown to be able to identify individual amino acids in polypeptide chains with high accuracy and distinguishability. Twenty amino acids could be detected and categorized in different classes based on current-residence time training data, with an accuracy of up to 99.6%. These results show promise for the development of amino acid detection platforms with atomically thin materials assisted by machine learning.
Published in: "NPJ 2D Materials and Applications".