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The process of finding new drugs is difficult,expensive, and time-consuming. Traditionally, itincludes molecular docking, high-throughputscreening, and a lot of lab work. This field hasundergone a revolution thanks to recentdevelopments in deep learning, which providestrong tools for accurately and efficiently modellingintricate biological and chemical interactions.Convolutional neural networks , recurrent neuralnetworks, and graph neural networks are examplesof deep learning techniques that are being used moreand more for tasks like virtual screening, drug–target interaction prediction, de novo drug design,and molecular property prediction. Promising drugcandidates can be quickly identified thanks to thesemodels' ability to learn from enormous datasets ofchemical structures, biological data, and clinicaloutcomes.