ADMET Property Prediction: The State of the Art and Current Challenges

TLDR

Progress in machine‑learning methods such as SVMs and Bayesian neural networks has been limited by inadequate molecular descriptors and the lack of publicly available large, high‑quality datasets, hindering full integration of ADMET modelling into drug discovery. This review surveys recent advances in ADMET prediction by QSAR and highlights the remaining challenges that must be addressed. The authors examine advances in statistical modelling, descriptor development, data set expansion, and evolving application of predictive ADMET models in drug‑discovery workflows. The full value of in silico ADMET models will not be realized until they are employed to inform multi‑parameter optimisation decisions.

Abstract

Abstract In this article, we review recent developments in the prediction of Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties by Quantitative Structure–Activity Relationships (QSAR). We consider advances in statistical modelling techniques, molecular descriptors and the sets of data used for model building and changes in the way in which predictive ADMET models are being applied in drug discovery. We also discuss the current challenges that remain to be addressed. While there has been progress in the adoption of non‐linear modelling techniques such as Support Vector Machines (SVM) and Bayesian Neural Networks (BNNs), the full advantages of these ‘machine learning’ techniques cannot be realised without further developments in molecular descriptors and availability of large, high‐quality datasets. The largest pharmaceutical companies have developed large in‐house databases containing consistently measured compound properties. However, these data are not yet available in the public domain and many models are still based on small ‘historical’ datasets taken from the literature. Probably, the largest remaining challenge is the full integration of predictive ADMET modelling in the drug discovery process. Until in silico models are applied to make effective decisions in a multi‐parameter optimisation process, the full value they could bring will not be realised.