  Background: The volume of distribution at steady state (VDss) is a
fundamental pharmacokinetics (PK) property of drugs, which measures how
effectively a drug molecule is distributed throughout the body. Along with the
clearance (CL), it determines the half-life and, therefore, the drug dosing
interval. However, the molecular data size limits the generalizability of the
reported machine learning models. Objective: This study aims to provide a clean
and comprehensive dataset for human VDss as the benchmarking data source,
fostering and benefiting future predictive studies. Moreover, several
predictive models were also built with machine learning regression algorithms.
Methods: The dataset was curated from 13 publicly accessible data sources and
the DrugBank database entirely from intravenous drug administration and then
underwent extensive data cleaning. The molecular descriptors were calculated
with Mordred, and feature selection was conducted for constructing predictive
models. Five machine learning methods were used to build regression models,
grid search was used to optimize hyperparameters, and ten-fold cross-validation
was used to evaluate the model. Results: An enriched dataset of VDss
(https://github.com/da-wen-er/VDss) was constructed with 2440 molecules. Among
the prediction models, the LightGBM model was the most stable and had the best
internal prediction ability with Q2 = 0.837, R2=0.814 and for the other four
models, Q2 was higher than 0.79. Conclusions: To the best of our knowledge,
this is the largest dataset for VDss, which can be used as the benchmark for
computational studies of VDss. Moreover, the regression models reported within
this study can be of use for pharmacokinetic related studies.
