FormulationBCS: A Machine Learning Platform Based on Diverse Molecular Representations for Biopharmaceutical Classification System (BCS) Class Prediction

作者：Zhenhua Wu, Nannan Wang, Zhuyifan Ye, Huanle Xu, Ging Chan, Defang Ouyang · 发表于：Molecular Pharmaceutics · 年份：2024 · DOI：10.1021/acs.molpharmaceut.4c00946 · 被引用次数：17 · 研究领域：Computational Drug Discovery Methods、Analytical Chemistry and Chromatography、Protein purification and stability

High Resolution Image Download MS PowerPoint Slide The Biopharmaceutics Classification System (BCS) has facilitated biowaivers and played a significant role in enhancing drug regulation and development efficiency. However, the productivity of measuring the key discriminative properties of BCS, solubility and permeability, still requires improvement, limiting high-throughput applications of BCS, which is essential for evaluating drug candidate developability and guiding formulation decisions in the early stages of drug development. In recent years, advancements in machine learning (ML) and molecular characterization have revealed the potential of quantitative structure–performance relationships (QSPR) for rapid and accurate in silico BCS classification. The present study aims to develop a web platform for high-throughput BCS classification based on high-performance ML models. Initially, four data sets of BCS-related molecular properties: log S, log P, log D, and log P app were curated. Subsequently, 6 ML algorithms or deep learning frameworks were employed to construct models, with diverse molecular representations ranging from one-dimensional molecular fingerprints, descriptors, and molecular graphs to three-dimensional molecular spatial coordinates. By comparing different combinations of molecular representations and learning algorithms, LightGBM exhibited excellent performance in solubility prediction, with an R 2 of 0.84; AttentiveFP outperformed others in permeability pre...