Title:Improving ADMET Prediction Accuracy for Candidate Drugs: Factors to
Consider in QSPR Modeling Approaches
Volume: 24
Issue: 3
Author(s): Meilun Chen, Jie Yang, Chunhua Tang, Xiaoling Lu, Zheng Wei, Yijie Liu, Peng Yu*HuanHuan Li*
Affiliation:
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan,
410013, China
- Xiangya School of Pharmaceutical Sciences, Central South University, No. 172, Tongzipo Road, Changsha, Hunan,
410013, China
Keywords:
QSPR, ADMET, Molecular descriptors, Prediction, Modeling approaches, Accuracy.
Abstract: Quantitative Structure-Property Relationship (QSPR) employs mathematical and statistical
methods to reveal quantitative correlations between the pharmacokinetics of compounds and
their molecular structures, as well as their physical and chemical properties. QSPR models have
been widely applied in the prediction of drug absorption, distribution, metabolism, excretion, and
toxicity (ADMET). However, the accuracy of QSPR models for predicting drug ADMET properties
still needs improvement. Therefore, this paper comprehensively reviews the tools employed in
various stages of QSPR predictions for drug ADMET. It summarizes commonly used approaches
to building QSPR models, systematically analyzing the advantages and limitations of each modeling
method to ensure their judicious application. We provide an overview of recent advancements
in the application of QSPR models for predicting drug ADMET properties. Furthermore, this review
explores the inherent challenges in QSPR modeling while also proposing a range of considerations
aimed at enhancing model prediction accuracy. The objective is to enhance the predictive
capabilities of QSPR models in the field of drug development and provide valuable reference and
guidance for researchers in this domain.
