Modeling PROTAC Degradation Activity with Machine Learning

TL;DR

This paper introduces an open-source deep learning approach for predicting PROTAC degradation activity, utilizing curated data and pretrained embeddings, achieving high accuracy and facilitating drug discovery.

Contribution

It presents a novel curated dataset and a deep learning model leveraging pretrained embeddings for PROTAC activity prediction, improving reproducibility and reducing computational complexity.

Findings

Top test accuracy of 80.8% and ROC AUC of 0.865

Generalization to new targets with 62.3% accuracy and 0.604 ROC AUC

Model performance is comparable to state-of-the-art methods

Abstract

PROTACs are a promising therapeutic modality that harnesses the cell's built-in degradation machinery to degrade specific proteins. Despite their potential, developing new PROTACs is challenging and requires significant domain expertise, time, and cost. Meanwhile, machine learning has transformed drug design and development. In this work, we present a strategy for curating open-source PROTAC data and an open-source deep learning tool for predicting the degradation activity of novel PROTAC molecules. The curated dataset incorporates important information such as $pDC_{50}$, $D_{max}$, E3 ligase type, POI amino acid sequence, and experimental cell type. Our model architecture leverages learned embeddings from pretrained machine learning models, in particular for encoding protein sequences and cell type information. We assessed the quality of the curated data and the generalization ability of our model architecture against new PROTACs and targets via three tailored studies, which we recommend other researchers to use in evaluating their degradation activity models. In each study, three models predict protein degradation in a majority vote setting, reaching a top test accuracy of 80.8% and 0.865 ROC AUC, and a test accuracy of 62.3% and 0.604 ROC AUC when generalizing to novel protein targets. Our results are not only comparable to state-of-the-art models for protein degradation prediction, but also part of an open-source implementation which is easily reproducible and less computationally complex than existing approaches.