ArticleJournal of computer-aided molecular design
Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility.
Article in Journal of computer-aided molecular design. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Will we ever be able to accurately predict solubility?Scientific data · 2024Article
- Direct Comparison of the Prediction of the Unbound Brain-to-Plasma Partitioning Utilizing Machine Learning Approach and Mechanistic Neuropharmacokinetic Model.The AAPS journal · 2021Article
- Pruned Machine Learning Models to Predict Aqueous Solubility.ACS omega · 2020Article
- Investigation of 3D pharmacophore of N-benzyl benzamide molecules of melanogenesis inhibitors using a new descriptor Klopman index: uncertainties in model.Journal of molecular modeling · 2019Article
- The role of multidrug resistance protein (MRP-1) as an active efflux transporter on blood-brain barrier (BBB) permeability.Molecular diversity · 2017Article
- Mode of Action Analyses of Neferine, a Bisbenzylisoquinoline Alkaloid of Lotus (Frontiers in pharmacology · 2017Article
- Advances in computationally modeling human oral bioavailability.Advanced drug delivery reviews · 2015Review
- Role of breast cancer resistance protein (BCRP) as active efflux transporter on blood-brain barrier (BBB) permeability.Molecular diversity · 2015Article
- A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.BioMed research international · 2015Article
- Computational methods in drug discovery.Pharmacological reviews · 2014Review
- Chemical predictive modelling to improve compound quality.Nature reviews. Drug discovery · 2013Review
- Article
- Guiding effective decisions: an interview with Matthew Segall, CEO of Optibrium. Interview by Wendy A. Warr.Journal of computer-aided molecular design · 2011Article
- The C1C2: a framework for simultaneous model selection and assessment.BMC bioinformatics · 2008Article
- Prediction of pharmacokinetic parameters using a genetic algorithm combined with an artificial neural network for a series of alkaloid drugs.Scientia pharmaceuticaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this article, we present an automatic model generation process for building QSAR models using Gaussian Processes, a powerful machine learning modeling method. We describe the stages of the process that ensure models are built and validated within a rigorous framework: descriptor calculation, splitting data into training, validation and test sets, descriptor filtering, application of modeling techniques and selection of the best model. We apply this automatic process to data sets of blood-brain barrier penetration and aqueous solubility and compare the resulting automatically generated models with 'manually' built models using external test sets. The results demonstrate the effectiveness of the automatic model generation process for two types of data sets commonly encountered in building ADME QSAR models, a small set of in vivo data and a large set of physico-chemical data.
Indexed as
Identifiers
18273554What OpenQuestion holds
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.