# aMLProt: an automated machine learning library for protein applications

## TL;DR

aMLProt is an automated machine learning library designed for protein applications, simplifying model development with pre-trained models and a user-friendly interface.

## Contribution

aMLProt introduces a modular AutoML framework with 19 classifiers, 26 regressors, and integration with a GUI tool for protein-related tasks.

## Key findings

- aMLProt includes pre-trained protein language models and standalone applications for enzyme engineering and bioprospecting.
- The framework is integrated with Horus, a GUI-based application, to enhance usability for non-expert users.
- A demo and data from a pH optima regression model are publicly accessible for immediate use and validation.

## Abstract

Machine learning tools have become increasingly common in biological research, driven by the emergence of pre-trained large language models. However, training effective models remains a complex task, since many choices influence their performance. AutoML (automated machine learning) approaches help address these challenges by streamlining the entire model development pipeline.

We developed aMLProt, an AutoML framework tailored specifically for protein applications, such as enzyme engineering and bioprospecting. It features a modular design, allowing each component to be used independently or in combination. Notably, aMLProt integrates 19 classifiers and 26 regressors, along with pre-trained protein language models. It also includes standalone applications proven useful for protein-related workflows. To enhance usability, aMLProt is integrated with Horus, a GUI-based application with a visual interface.

aMLProt is available on https://github.com/etiur/aMLProt.git and https://doi.org/10.5281/zenodo.14971157; The aMLProt plugin is available via the official Horus Plugin Repository https://horus.bsc.es/repo/plugins/amlprot, and Horus itself can be freely downloaded from https://horus.bsc.es. Moreover, a demo of aMLProt can be found, without previous registration or download, at the horus.bsc.es/amlprot and horus.bsc.es/amlprot-suggest. The results and data from the pH optima regression model are available at: https://zenodo.org/records/15394097.

Graphical Abstract