HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer

TL;DR

HelixADMET is a versatile ADMET prediction system that uses self-supervised learning and transfer learning to improve accuracy and allow customization for various drug discovery needs.

Contribution

The paper introduces HelixADMET, a novel ADMET system that leverages self-supervised pre-training and multi-task fine-tuning for improved extrapolation and endpoint extensibility.

Findings

Achieves 4% overall improvement over existing systems.

Pre-trained model enables customization for new ADMET endpoints.

Demonstrates robustness in predicting unobserved scaffolds.

Abstract

Accurate ADMET (an abbreviation for "absorption, distribution, metabolism, excretion, and toxicity") predictions can efficiently screen out undesirable drug candidates in the early stage of drug discovery. In recent years, multiple comprehensive ADMET systems that adopt advanced machine learning models have been developed, providing services to estimate multiple endpoints. However, those ADMET systems usually suffer from weak extrapolation ability. First, due to the lack of labelled data for each endpoint, typical machine learning models perform frail for the molecules with unobserved scaffolds. Second, most systems only provide fixed built-in endpoints and cannot be customised to satisfy various research requirements. To this end, we develop a robust and endpoint extensible ADMET system, HelixADMET (H-ADMET). H-ADMET incorporates the concept of self-supervised learning to produce a robust pre-trained model. The model is then fine-tuned with a multi-task and multi-stage framework to transfer knowledge between ADMET endpoints, auxiliary tasks, and self-supervised tasks. Our results demonstrate that H-ADMET achieves an overall improvement of 4%, compared with existing ADMET systems on comparable endpoints. Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.