ArticleInternational journal of molecular sciences2010
Prediction of PKCθ inhibitory activity using the Random Forest Algorithm.
Article in International journal of molecular sciences, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AiKPro: deep learning model for kinome-wide bioactivity profiling using structure-based sequence alignments and molecular 3D conformer ensemble descriptors.Scientific reports · 2023Article
- Rhizoma Atractylodis Macrocephalae-Assessing the influence of herbal processing methods and improved effects on functional dyspepsia.Frontiers in pharmacology · 2023Article
- The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors.Scientific reports · 2022Article
- A safe-by-design tool for functionalised nanomaterials through the Enalos Nanoinformatics Cloud platform.Nanoscale advances · 2019Article
- Toward the prediction of FBPase inhibitory activity using chemoinformatic methods.International journal of molecular sciences · 2012Article
- A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.PloS one · 2012Article
- A classification study of human β₃-adrenergic receptor agonists using BCUT descriptors.Molecular diversity · 2011Article
- A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest.International journal of molecular sciences · 2011Article
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Authors and funding
4 authors.
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Abstract
This work is devoted to the prediction of a series of 208 structurally diverse PKCθ inhibitors using the Random Forest (RF) based on the Mold(2) molecular descriptors. The RF model was established and identified as a robust predictor of the experimental pIC(50) values, producing good external R(2) (pred) of 0.72, a standard error of prediction (SEP) of 0.45, for an external prediction set of 51 inhibitors which were not used in the development of QSAR models. By using the RF built-in measure of the relative importance of the descriptors, an important predictor-the number of group donor atoms for H-bonds (with N and O)-has been identified to play a crucial role in PKCθ inhibitory activity. We hope that the developed RF model will be helpful in the screening and prediction of novel unknown PKCθ inhibitory activity.
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