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

The integration of machine learning (ML) and deep learning (DL) into drug discovery and target identification has catalyzed a paradigm shift in pharmaceutical research, enhancing efficiency and translational potential for nano-enabled therapeutics. ML models have demonstrated up to 85% accuracy in predicting drug–target interactions, whereas DL frameworks, such as convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer architectures, can improve molecular property predictions by 40%. AI-driven drug discovery workflows have curtailed drug candidate attrition rates by up to 30% and accelerated discovery timelines by 20%– 40%, accentuating their rising industrial and clinical impact. This critical review evaluates the transformative roles of ML and DL in the drug discovery pipeline, emphasizing their capacity to accelerate development timelines and advance precision nano medicine. We analyzed predictive modelling techniques, including quantitative structure–activity relationship (QSAR) and absorption, distribution, metabolism, and excretion (ADME) predictions, which streamline the identification of viable drug candidates, including nanocarrier-enabled drug systems. Virtual screening and bioactivity prediction further refine candidate prioritization, whereas target identification and validation leverage protein–ligand interaction modelling and biological pathway analysis to ensure therapeutic specificity. Additionally, we discuss the profound impact of DL on medical image analysis, genomic data interpretation, and protein structure prediction (PSP), which collectively advance structural bioinformatics and enable optimized targeted nano medicine. By synergizing ML and DL, multi-modal data fusion, explainable artificial intelligence (XAI), and nanotechnology-driven datasets, the drug discovery process is evolving into a more efficient, predictive, and patient-centric endeavor, paving the way for ground-breaking therapies and improved clinical outcomes. A multi-box infographic titled “Machine learning and deep learning interventions in drug discovery and drug targeting”. A left section shows a human torso silhouette beside a network of computer screens around an Rx symbol, with the text “Machine learning (ML) and deep learning (DL) applications in pharmaceuticals”, plus “T TECHNOLOGY” and a chip icon. Top row, three titled boxes. “Predictive modeling & screening” lists “Quantitative Structure-Activity Relationship”, “Ligand and structure based virtual screening”, “Absorption, distribution, metabolism and excretion”, and “Bioactivity predictions”, illustrated with a QR-like block, a receptor diagram, a blood vessel and a lab scientist with glassware. “Target identification and validation” lists “Protein-ligand interactions”, “Molecular Docking”, “Molecular Dynamics Simulations”, and “Biological pathway analysis”, illustrated with a lab worker, a person at a computer, a gear-like symbol and a pathway graphic. “DL in Pharmaceuticals” lists “Medical diagnoses”, “Microscopy data for cellular analysis”, “Medical imaging for drug delivery optimization”, and “Genomic data analysis for personalized medicine”, illustrated with a joint image, a checklist, a brain with gears and DNA. Bottom row, three titled boxes. “ML and DL in Pharma Industry” lists “Protein structure prediction”, “Text and literature mining process”, “Biopharmaceutical Manufacturing”, and “Personalized Medicine”, illustrated with a protein ribbon, a grid, manufacturing equipment and a laptop with chat bubbles. “ML in biosimilar & drug delivery” lists “Predictive Toxicology”, “Drug Target Identification”, “De Novo Drug Design”, and “Biotechnological products”, illustrated with blood cells, a target-like symbol, a chemical structure and a funnel. “Challenges for ML in Pharma” lists “Ethics & legal issues”, “Regulatory compliance”, “Transparent interpretation”, and “Data availability & quality”, illustrated with a suited figure, a discussion icon, a person at a desk and a group of people. No numeric statistics are shown.An infographic mapping ML and DL uses in pharmaceuticals across modeling, targets, delivery and challenges.