ArticleFrontiers in chemistry2021
A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier.
Article in Frontiers in chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 23 citations in OpenAlex.
- PBPK Models for Fetal Drug Exposure: A Critical Assessment of Current Approaches and Regulatory Readiness.Journal of clinical pharmacology · 2026Review
- Transplacental Transfer of Telbivudine in Hepatitis B Virus-Infected Pregnant Women Treated with Telbivudine during the Third Trimester.ACS omega · 2026Article
- Transplacental Antibody Transfer: Mechanisms, Pregnancy-Related Disruptions, and Emerging Experimental Models.Antibodies (Basel, Switzerland) · 2026Review
- Psychedelic Drugs Rediscovered-In Silico Study of Potential Fetal Exposure to Analogues of Psychedelic Drugs During Pregnancy.Molecules (Basel, Switzerland) · 2026Article
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- Article
- Organic Sunscreens and Their Products of Degradation in Biotic and Abiotic Conditions-In Silico Studies of Drug-Likeness and Human Placental Transport.International journal of molecular sciences · 2024Article
- Toxicology knowledge graph for structural birth defects.Communications medicine · 2023Article
- Review
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.