A Meta-model for ADMET Property Prediction Analysis

In drug discovery analysis chemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties play a critical role. These properties allow the quantitative evaluation of a designed drugs efficacy. Several machine learning models have been designed for the prediction of ADMET properties. However, no single method seems to enable the accurate prediction of these properties. In this paper, we build a meta-model that learns the best possible way to combine the scores from multiple heterogeneous machine learning models to effectively predict the ADMET properties. We evaluate the performance of our proposed model against the Therapeutics Data Commons (TDC) ADMET benchmark dataset. The proposed meta-model outperforms state-of-the-art methods such as XGBoost in the TDC leaderboard, and it ranks first in five and in the top three positions for fifteen out of twenty-two prediction tasks.