Abstract The extent to which a drug administered to a mother reaches the fetus is determined by its ability to cross the blood–placental barrier. Accurate knowledge of blood–placental barrier permeability is not only crucial for the development of safe drugs but also provides essential guidance for pharmacotherapy in pregnant women, where safety concerns are paramount. However, experimental evaluation remains challenging because animal models do not adequately recapitulate the human placenta, and human-based approaches such as cord blood analysis or placental perfusion are ethically and technically constrained. In this study, we employ gradient boosting decision trees (GBDT) to construct predictive models of blood–placental barrier permeability with relatively low computational cost. Two endpoints derived from publicly available human data were modeled separately: (i) in vivo log-transformed fetal–maternal blood concentration ratios (logFM), and (ii) ex vivo clearance indices (CI) from placental perfusion experiments. In both cases, our LightGBM-based models achieved higher predictive accuracy and better generalization compared with previous approaches. To facilitate practical use, we implemented a freely accessible web application, PBPredictor ( https://pbpredictor.net ), which provides real-time predictions of logFM and CI from SMILES inputs, along with programmatic access via a REST API. By integrating reliable machine learning with an easy-to-use platform, PBPredictor offers a scalable tool to support safer drug development and evidence-based treatment strategies during pregnancy.
Ohue et al. (Fri,) studied this question.