PADME: A Deep Learning-based Framework for Drug-Target Interaction Prediction

TL;DR

PADME is a novel deep learning framework that predicts continuous drug-target interaction strengths, effectively handling cold-target problems by combining molecular graph convolution with protein descriptors, outperforming existing methods.

Contribution

First to integrate Molecular Graph Convolution with protein descriptors for DTI prediction, enabling real-valued interaction prediction and cold-target problem handling.

Findings

PADME outperforms baseline methods across multiple datasets.

It effectively predicts binding affinities in case studies.

The framework is scalable for large datasets.

Abstract

In silico drug-target interaction (DTI) prediction is an important and challenging problem in biomedical research with a huge potential benefit to the pharmaceutical industry and patients. Most existing methods for DTI prediction including deep learning models generally have binary endpoints, which could be an oversimplification of the problem, and those methods are typically unable to handle cold-target problems, i.e., problems involving target protein that never appeared in the training set. Towards this, we contrived PADME (Protein And Drug Molecule interaction prEdiction), a framework based on Deep Neural Networks, to predict real-valued interaction strength between compounds and proteins without requiring feature engineering. PADME takes both compound and protein information as inputs, so it is capable of solving cold-target (and cold-drug) problems. To our knowledge, we are the first to combine Molecular Graph Convolution (MGC) for compound featurization with protein descriptors for DTI prediction. We used multiple cross-validation split schemes and evaluation metrics to measure the performance of PADME on multiple datasets, including the ToxCast dataset, and PADME consistently dominates baseline methods. The results of a case study, which predicts the binding affinity between various compounds and androgen receptor (AR), suggest PADME's potential in drug development. The scalability of PADME is another advantage in the age of Big Data.