Auto-ADMET: An Effective and Interpretable AutoML Method for Chemical ADMET Property Prediction

TL;DR

Auto-ADMET is an interpretable AutoML approach using evolutionary algorithms and Bayesian networks to improve chemical ADMET property prediction, addressing generalization and interpretability issues in drug discovery models.

Contribution

This work introduces Auto-ADMET, a novel AutoML method combining Grammar-based Genetic Programming and Bayesian Networks for enhanced, interpretable ADMET property prediction.

Findings

Auto-ADMET outperforms baseline models on 12 datasets.

Bayesian Network integration improves interpretability.

Auto-ADMET achieves comparable or better accuracy.

Abstract

Machine learning (ML) has been playing important roles in drug discovery in the past years by providing (pre-)screening tools for prioritising chemical compounds to pass through wet lab experiments. One of the main ML tasks in drug discovery is to build quantitative structure-activity relationship (QSAR) models, associating the molecular structure of chemical compounds with an activity or property. These properties -- including absorption, distribution, metabolism, excretion and toxicity (ADMET) -- are essential to model compound behaviour, activity and interactions in the organism. Although several methods exist, the majority of them do not provide an appropriate model's personalisation, yielding to bias and lack of generalisation to new data since the chemical space usually shifts from application to application. This fact leads to low predictive performance when completely new data is being tested by the model. The area of Automated Machine Learning (AutoML) emerged aiming to solve this issue, outputting tailored ML algorithms to the data at hand. Although an important task, AutoML has not been practically used to assist cheminformatics and computational chemistry researchers often, with just a few works related to the field. To address these challenges, this work introduces Auto-ADMET, an interpretable evolutionary-based AutoML method for chemical ADMET property prediction. Auto-ADMET employs a Grammar-based Genetic Programming (GGP) method with a Bayesian Network Model to achieve comparable or better predictive performance against three alternative methods -- standard GGP method, pkCSM and XGBOOST model -- on 12 benchmark chemical ADMET property prediction datasets. The use of a Bayesian Network model on Auto-ADMET's evolutionary process assisted in both shaping the search procedure and interpreting the causes of its AutoML performance.