Combinations of Analytical and Machine Learning Methods in a Single Simulation Framework for Amphoteric Molecules Detection

The most recent advances in personalized medicine require highly accurate drug syntheses. A significant component of synthesis is verification. This step requires confirming the sequence of amino acids (AAs) in the target drug (protein). This letter presents a novel methodology for identifying amphoteric molecules, such as AAs, peptides, and many enantiomers, using the signal from the surface potential and capacitance obtained from field-effect transistor (FET) sensors. Our methodology combines the site-binding and Gouy–Chapman–Stern (GCS) models with a transformer mode. We have termed the resulting approach BioToken. It can find AA sequences up to a length of 5 with 95% accuracy. BioToken achieves this by using FET sensor data, which is cheaper and easier to obtain than the competing state-of-the-art mass spectroscopy technology. This letter presents the BioToken concept and shows our initial steps in its development.