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

Using AI in drug research has changed how we find and develop new medicines. This project looks at how modern machine learning and deep learning can improve target identification and design precise nanomedicines. With computer models, we try to predict drug‑target interactions (DTIs) and simulate how nanocarriers behave in complex biological environments. We use deep neural networks—specifically graph convolutional networks and recurrent neural networks—to study molecular structures, genomic data, and pharmacokinetic properties. We also build predictive algorithms that model how nanoparticles interact with cells, helping us create targeted delivery systems that reduce side effects. The framework includes clear ways to measure model performance, using metrics like the area under the

receiver operating characteristic curve (AUROC) and mean squared error (MSE). In the end, this work aims to create strong, scalable in‑silico pipelines that connect computational bioinformatics with experimental nanomedicine, giving a systematic method to find therapeutic targets and fine‑tune nanocarrier designs for personalized treatments.

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