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Updated: Mar 9, 2026
Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays
Published on: October 23, 2019
Multiple machine learning based descriptive and predictive workflow for the identification of potential PTP1B
Sharat Chandra1, Jyotsana Pandey2, Akhilesh Kumar Tamrakar2
1Academy of Scientific and Innovative Research (AcSIR), CSIR-Central Drug Resaerch Institute, Campus, Lucknow 226031, India; Molecular and Structural Biology Division, CSIR-Central Drug Research Institute, Lucknow 226031, India.
Abstract:
In insulin and leptin signaling pathway, Protein-Tyrosine Phosphatase 1B (PTP1B) plays a crucial controlling role as a negative regulator, which makes it an attractive therapeutic target for both Type-2 Diabetes (T2D) and obesity. In this work, we have generated classification models by using the inhibition data set of known PTP1B inhibitors to identify new inhibitors of PTP1B utilizing multiple machine learning techniques like naïve Bayesian, random forest, support vector machine and k-nearest neighbors, along with structural fingerprints and selected molecular descriptors. Several models from each algorithm have been constructed and optimized, with the different combination of molecular descriptors and structural fingerprints. For the training and test sets, most of the predictive models showed more than 90% of overall prediction accuracies. The best model was obtained with support vector machine approach and has Matthews Correlation Coefficient of 0.82 for the external test set, which was further employed for the virtual screening of Maybridge small compound database. Five compounds were subsequently selected for experimental assay. Out of these two compounds were found to inhibit PTP1B with significant inhibitory activity in in-vitro inhibition assay. The structural fragments which are important for PTP1B inhibition were identified by naïve Bayesian method and can be further exploited to design new molecules around the identified scaffolds. The descriptive and predictive modeling strategy applied in this study is capable of identifying PTP1B inhibitors from the large compound libraries.
Insights
Machine learning models accurately identified new inhibitors for Protein-Tyrosine Phosphatase 1B (PTP1B), a key target for Type-2 Diabetes and obesity. Two novel compounds showed significant PTP1B inhibition in experimental assays.
Area of Science:
- Biochemistry
- Computational Chemistry
- Pharmacology
Background:
- Protein-Tyrosine Phosphatase 1B (PTP1B) is a critical negative regulator in insulin and leptin signaling pathways.
- PTP1B is a significant therapeutic target for managing Type-2 Diabetes (T2D) and obesity.
Purpose of the Study:
- To develop and optimize machine learning classification models for identifying novel PTP1B inhibitors.
- To utilize predictive modeling for virtual screening of compound libraries and experimental validation.
Main Methods:
- Employed machine learning techniques including Naïve Bayesian, Random Forest, Support Vector Machine, and K-Nearest Neighbors.
- Utilized structural fingerprints and molecular descriptors for model construction and optimization.
- Performed virtual screening on the Maybridge small compound database using the best predictive model.
Main Results:
- Multiple models achieved over 90% prediction accuracy on training and test sets.
- The best Support Vector Machine model demonstrated a Matthews Correlation Coefficient of 0.82 on an external test set.
- Experimental assays confirmed PTP1B inhibitory activity in two out of five screened compounds, with identified key structural fragments.
Conclusions:
- The developed machine learning strategy effectively identifies PTP1B inhibitors from large compound libraries.
- This approach aids in the design of new therapeutic molecules targeting PTP1B for T2D and obesity.
- The study highlights the potential of computational methods in drug discovery for metabolic diseases.
