Related Experiment Video
Updated: Jul 12, 2026
MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
Published on: October 26, 2017
Sequential virtual screening collaborated with machine-learning strategies for the discovery of precise medicine
Muthu Kumar Thirunavukkarasu1, Shanthi Veerappapillai1, Ramanathan Karuppasamy1
1Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
Abstract:
Dysregulation of MAPK pathway receptors are crucial in causing uncontrolled cell proliferation in many cancer types including non-small cell lung cancer. Due to the complications in targeting the upstream components, MEK is an appealing target to diminish this pathway activity. Hence, we have aimed to discover potent MEK inhibitors by integrating virtual screening and machine learning-based strategies. Preliminary screening was conducted on 11,808 compounds using the cavity-based pharmacophore model AADDRRR. Further, seven ML models were accessed to predict the MEK active compounds using six molecular representations. The LGB model with morgan2 fingerprints surpasses other models ensuing 0.92 accuracy and 0.83 MCC value versus test set and 0.85 accuracy and 0.70 MCC value with external set. Further, the binding ability of screened hits were examined using glide XP docking and prime-MM/GBSA calculations. Note that we have utilized three ML-based scoring functions to predict the various biological properties of the compounds. The two hit compounds such as DB06920 and DB08010 resulted excellent binding mechanism with acceptable toxicity properties against MEK. Further, 200 ns of MD simulation combined with MM-GBSA/PBSA calculations confirms that DB06920 may have stable binding conformations with MEK thus step forwarded to the experimental studies in the near future.Communicated by Ramaswamy H. Sarma.
Insights
Researchers identified potent MEK inhibitors for cancer therapy using virtual screening and machine learning. Two compounds, DB06920 and DB08010, show promising binding mechanisms and acceptable toxicity, advancing cancer drug discovery.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Drug Discovery
Background:
- Dysregulated MAPK pathway signaling drives uncontrolled cell proliferation in cancers like non-small cell lung cancer.
- MEK represents an attractive therapeutic target due to challenges in inhibiting upstream pathway components.
Purpose of the Study:
- To discover potent MEK inhibitors through integrated virtual screening and machine learning (ML).
- To identify novel compounds with potential anti-cancer activity by targeting the MEK pathway.
Main Methods:
- Virtual screening of 11,808 compounds using a pharmacophore model (AADDRRR).
- ML models, including LGB with morgan2 fingerprints, were employed for predicting MEK activity.
- Molecular docking (glide XP) and molecular dynamics (MD) simulations with MM/GBSA calculations assessed binding affinity and stability.
Main Results:
- The LGB ML model achieved high accuracy (0.92 test, 0.85 external set) in predicting MEK inhibitors.
- Two hit compounds, DB06920 and DB08010, demonstrated excellent binding mechanisms and favorable toxicity profiles.
- MD simulations indicated stable binding conformations for DB06920 with MEK.
Conclusions:
- Integrated virtual screening and ML strategies successfully identified promising MEK inhibitors.
- DB06920 and DB08010 warrant further experimental validation for potential cancer therapeutics.
- This study provides a foundation for developing novel MEK-targeted cancer therapies.
More Related Videos
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
07:15Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
