Current Updates on Recent Developments in Artificial Intelligence in QSAR Modelling for Drug Discovery against Lung...

Area of Science:

• Computational chemistry and pharmacology
• Oncology drug discovery
• Artificial intelligence in medicine

Background:

• Lung cancer remains a major global cause of cancer mortality.
• There is an urgent need for innovative and targeted drug discovery strategies.
• Quantitative Structure-Activity Relationship (QSAR) modeling offers a promising avenue for therapeutic development.

Purpose of the Study:

• To critically review the role of AI-integrated QSAR in accelerating lung cancer drug discovery.
• To analyze recent advancements in multi-target approaches, machine learning, and molecular descriptors.
• To focus on the clinical translation of AI-powered QSAR methodologies.

Main Methods:

• Analysis of recent progress in AI-powered QSAR for lung cancer therapeutics.
• Evaluation of how AI-QSAR addresses drug development bottlenecks like data imbalance and ADMET prediction.
• Examination of case studies highlighting translational success in lung cancer pathways.

Main Results:

• AI-powered QSAR models show significant potential in identifying and optimizing lung cancer therapeutics.
• Integration of machine learning and advanced molecular descriptors enhances predictive accuracy.
• AI-QSAR effectively tackles challenges in data imbalance and model interpretability.

Conclusions:

• AI-enhanced QSAR methodologies are crucial for advancing lung cancer drug discovery.
• Addressing current gaps can further improve the real-world application of these computational tools.
• Future directions involve refining AI-QSAR for more effective oncological drug development.