Building a Predictive Quantitative Analysis-Structure Relationship (QASR) Model for Silica-Based Drug Screening

Quantitative Analysis-Structure Relationship (QASR) models are pivotal in modern drug design for predicting the pharmacokinetic properties of novel compounds. This paper proposes and demonstrates a methodology for leveraging publicly available data from bioinformatics repositories to build a statistical QASR model. The model predicts the retention factor (LogK) of drug compounds on silica-based chromatography systems—a key surrogate for adsorption behavior and a critical parameter in early-stage drug screening. Utilizing statistically simulated data that mirrors real-world molecular and chromatographic properties sourced from public databases, a multiple linear regression model was developed. Molecular descriptors including LogP, Molecular Weight (MW), Topological Polar Surface Area (TPSA), and Number of Hydrogen Bond Donors (HBD) served as independent variables. The simulated model demonstrated a strong predictive ability (R² = 0.89, Adjusted R² = 0.87, p-value < 0.001), successfully validating the proposed framework. This work provides a complete blueprint for the acquisition, curation, and statistical analysis of online data for QASR-silica research, bridging the fields of biotechnology and statistics.