Advancing Drug Discovery with AI: Machine and Deep Learning Strategies for Target Identification and Precision...

Area of Science:

• Pharmaceutical research
• Computational chemistry
• Biotechnology

Background:

• Machine learning (ML) and deep learning (DL) integration is transforming pharmaceutical research.
• AI enhances efficiency and translational potential for nano-enabled therapeutics.
• ML models predict drug-target interactions with up to 85% accuracy.

Purpose of the Study:

• To review the transformative roles of ML and DL in drug discovery and target identification.
• To emphasize the acceleration of development timelines and advancement of precision nano medicine.
• To analyze predictive modeling techniques and DL applications in drug development.

Main Methods:

• Analysis of ML and DL frameworks (CNNs, GNNs, transformers) for molecular property prediction.
• Evaluation of quantitative structure-activity relationship (QSAR) and ADME prediction models.
• Review of virtual screening, bioactivity prediction, and protein-ligand interaction modeling.

Main Results:

• DL improves molecular property predictions by up to 40%.
• AI-driven workflows reduce drug candidate attrition by up to 30% and accelerate timelines by 20%-40%.
• DL significantly impacts medical image analysis, genomic data interpretation, and protein structure prediction.

Conclusions:

• Synergizing ML and DL with multi-modal data fusion and XAI optimizes the drug discovery process.
• The integration of AI and nanotechnology drives a more efficient, predictive, and patient-centric approach.
• This evolution paves the way for groundbreaking therapies and improved clinical outcomes.