# A new method for binary classification of proteins with Machine Learning

arXiv: https://arxiv.org/abs/2111.01976 · HTML: https://mlsanity.com/paper/2111.01976/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 28 references that are in the map.
- First posted on arXiv 3 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [A model-driven approach for a new generation of adaptive libraries](https://mlsanity.com/paper/1806.07060/) (2018)

## Built on by (most cited first, 1 of 1)

- [An example of use of Variational Methods in Quantum Machine Learning](https://mlsanity.com/paper/2208.04316/) (2022)

## Abstract (arXiv)

In this work we set out to find a method to classify protein structures using a Deep Learning methodology. Our Artificial Intelligence has been trained to recognize complex biomolecule structures extrapolated from the Protein Data Bank (PDB) database and reprocessed as images; for this purpose various tests have been conducted with pre-trained Convolutional Neural Networks, such as InceptionResNetV2 or InceptionV3, in order to extract significant features from these images and correctly classify the molecule. A comparative analysis of the performances of the various networks will therefore be produced.