Machine learning for the discovery of molecular recognition based on single-walled carbon nanotube corona-phases

Xun Gong, Nicholas Renegar, Retsef Levi, Michael S. Strano

npj Computational Materials 2022 article cited by 26

Source Open-access PDF More in Engineering & Materials

importance 4/10 partially solved Advanced biosensing and bioanalysis techniques nanosensors stage: research

Problem

No generalized scheme exists to predict de novo the molecular recognition of DNA-SWCNT corona phases, due to their sequence-dependent three-dimensional complexity.

Why it matters

Corona-phase molecular recognition underpins sensing applications, but designing such sensors currently relies on trial and error.

What the AI did

Machine learning using local and high-level features was trained on a 1408-element DNA-SWCNT photoluminescence response library to predict sensor responses and understand sequence dependence.

Results

Out-of-sample model predictions significantly correlated with actual sensor responses in 6 of 8 experimental conditions; combining local and high-level features improved over high-level features alone.

Impact

ML enabled partial prediction of sensor responses, aiding corona-phase sensor design, but 2 of 8 conditions were not achieved.

Record

Venue	npj Computational Materials
Published	2022
Work type	article
Authors	Xun Gong, Nicholas Renegar, Retsef Levi, Michael S. Strano
Citations	26 (OpenAlex, at import time)
References	44
Model or method	not stated in source
Organization	not stated in source
Primary source	https://doi.org/10.1038/s41524-022-00795-7
Open access	gold
Keywords	carbon nanotubes, corona phase, molecular recognition, machine learning, DNA, photoluminescence sensors

Importance rating

4 / 10

real but narrower advance

Corona-phase molecular recognition underpins sensing applications, but abstract gives no scale of impact; a real yet narrow advance in nanosensor design methodology.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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