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DrugBank mining with machine learning reveals novel candidates for BCL-2 inhibition
Juwon Park1, Soyoung Cho1, Hyundo Lee1
1College of Pharmacy , Dongduk Women's University , 02748, Seoul, South Korea.
Abstract:
Apoptosis, also known as programmed cell death, is a fundamental biological process essential for development and cellular homeostasis. An imbalance in the levels of pro- and antiapoptotic proteins of the BCL-2 family can inhibit apoptosis and contribute to tumor formation. Although small-molecule inhibitors targeting BCL-2 proteins have been developed, their clinical efficacy remains limited, highlighting the need for new approaches to discover effective inhibitors. In this study, we used a machine learning-based approach to identify potential BCL-2 inhibitors. The activity data for BCL-2 ligands were curated from the ChEMBL database and used to train and evaluate multiple classification models. Of the seven algorithms tested, the LightGBM model performed the best and was used to predict novel BCL-2 inhibitors in the DrugBank database. This strategy identified two candidate compounds, Opelconazole and Zongertinib, from the curated DrugBank All dataset, which covered all categories. These results demonstrate the potential of machine learning-based drug repositioning for the discovery of effective BCL-2 inhibitors, which could contribute to the development of targeted antiapoptotic therapeutics.
Insights
Machine learning identified novel BCL-2 inhibitors for cancer therapy. This approach aids in discovering new drugs targeting programmed cell death (apoptosis) pathways, potentially improving cancer treatment outcomes.
Area of Science:
- Biochemistry
- Computational Biology
- Pharmacology
Background:
- Apoptosis, or programmed cell death, is crucial for development and homeostasis.
- Dysregulation of BCL-2 family proteins, which control apoptosis, is linked to tumor formation.
- Existing small-molecule BCL-2 inhibitors have limited clinical efficacy, necessitating novel discovery methods.
Purpose of the Study:
- To employ a machine learning strategy for identifying novel inhibitors of BCL-2 proteins.
- To leverage drug repositioning for discovering effective anti-apoptotic therapeutics.
Main Methods:
- Curated BCL-2 ligand activity data from the ChEMBL database.
- Trained and evaluated multiple classification models, with LightGBM showing superior performance.
- Utilized the optimized LightGBM model to predict BCL-2 inhibitors within the DrugBank database.
Main Results:
- The LightGBM model demonstrated the highest performance among seven tested algorithms.
- Two novel BCL-2 inhibitor candidates, Opelconazole and Zongertinib, were identified from the DrugBank database.
- The study successfully applied machine learning for drug repositioning to find potential anti-cancer agents.
Conclusions:
- Machine learning-based drug repositioning is a viable strategy for discovering effective BCL-2 inhibitors.
- Identified compounds may advance the development of targeted anti-apoptotic cancer therapeutics.
- This approach holds promise for accelerating the discovery of novel therapeutic agents for various diseases.
