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iDCNNPred: an interpretable deep learning model for virtual screening and identification of PI3Ka inhibitors against
Ravishankar Jaiswal1,2, Girdhar Bhati1, Shakil Ahmed1
1Biochemistry and Structural Biology Division, CSIR-Central Drug Research Institute, Sector 10, Jankipuram Extension, Sitapur Road, Lucknow, 226031, India.
Abstract:
Triple-negative breast cancer (TNBC) lacks estrogen, progesterone, and HER2 expression, accounting for 15-20% of breast cancer cases. It is challenging due to low therapeutic response, heterogeneity, and aggressiveness. The PI3Ka isoform is a promising therapeutic target, often hyperactivated in TNBC, contributing to uncontrolled growth and cancer cell formation. We have proposed an interpretable deep convolutional neural network prediction (iDCNNPred) system using 2D molecular images to classify bioactivity and identify potential PI3Ka inhibitors. We built Custom-DCNN models and pre-trained models such as AlexNet, SqueezeNet, and VGG19 by using the Bayesian optimization algorithm, and found that our Custom-DCNN model performed better than a pre-trained model with lower complexity and memory usage. All top-performed models were screened with the Maybridge Chemical library to find predictive hit molecules. The screened molecules were further evaluated for protein-ligand interaction with molecular docking and finally 12 promising hits were shortlisted for biological validation using in-vitro PI3K inhibition studies. After biological evaluation, 4 potent molecules with different structural moieties were identified, and these molecules present new starting scaffolds for further improvement in terms of their potency and selectivity as PI3K inhibitors with the help of medicinal chemistry efforts. Furthermore, we also showed the significance of the interpretation and visualization of the model's predictions by the Grad-CAM technique, enhancing the robustness, transparency, and interpretability of the model's predictions. The data and script files and prediction run of models used for this study to reproduce the experiment are available in the GitHub repository at https://github.com/ravishankar1307/iDCNNPred.git .
Insights
Researchers developed an interpretable deep convolutional neural network (iDCNNPred) to identify novel PI3Ka inhibitors for triple-negative breast cancer (TNBC). This AI approach successfully identified four potent drug candidates with new scaffolds for further development.
Area of Science:
- Computational chemistry
- Drug discovery
- Bioinformatics
Background:
- Triple-negative breast cancer (TNBC) is aggressive and lacks targeted therapies.
- The PI3K/AKT pathway, particularly the PI3Ka isoform, is frequently hyperactivated in TNBC, driving tumor growth.
- Developing novel PI3Ka inhibitors is crucial for effective TNBC treatment.
Purpose of the Study:
- To develop and validate an interpretable deep convolutional neural network prediction (iDCNNPred) system for classifying bioactivity and identifying potential PI3Ka inhibitors.
- To screen chemical libraries and identify novel hit molecules with potential therapeutic value against TNBC.
- To enhance model interpretability and transparency using visualization techniques.
Main Methods:
- Construction and optimization of Custom-DCNN models using Bayesian optimization.
- Screening of the Maybridge Chemical library using the developed iDCNNPred system.
- Molecular docking for protein-ligand interaction analysis.
- In vitro PI3K inhibition assays for biological validation.
- Application of Grad-CAM for model prediction interpretation and visualization.
Main Results:
- The Custom-DCNN model demonstrated superior performance compared to pre-trained models with reduced complexity.
- 12 promising hit molecules were identified through library screening and molecular docking.
- 4 potent PI3Ka inhibitors with distinct structural moieties were validated through in vitro studies.
- Grad-CAM visualization enhanced the interpretability and robustness of the iDCNNPred system.
Conclusions:
- The iDCNNPred system is an effective tool for identifying novel PI3Ka inhibitors for TNBC.
- The identified potent molecules represent promising starting scaffolds for medicinal chemistry optimization.
- The study highlights the importance of interpretable AI in drug discovery for complex diseases like TNBC.
