Despite the success of screening methods, the synthesis of novel nanosensors presents a major bottleneck for the scalability of high throughput approaches. To reduce the time needed for the exploration of chemical space, different groups developed in-silico approaches for the prediction of suitable nanosensor modifications (Figure ). Apart from prediction, computational methods can furthermore be used for classification and clustering of sensor data. Prediction of optimal nanosensor functionalization. Due to the structure-function relationship of molecular recognition, the shape of a corona phase is strongly correlated with the conformation of a polymer on the nanomaterial. For SWCNTs, it is known that certain sequences display high affinities to certain chiralities and can be used to separate them. Finding such selective sequences is similar to the generation of a biosensor and early studies designed to systematically sample a DNA library for chirality dependent recognition via sequence pattern achieved a success rate between 7% and approx. 10%. To predict suitable surface modifications for biosensors, Yang et al. reported the first machine learning approach to molecular recognition of DNA-SWCNTs. Using this model, the frequency of finding correct recognition sequences for the recognition of SWCNT chiralities increased above 50%. More recently, Lee et al. presented how machine learning could distinguish high affinity ssDNA sequences and used molecular dynamics simulations to identify patterns of intramolecular hydrogen bonding in these sequences.