Machine learning (ML) is transforming drug discovery by accelerating the identification of new drugs, reducing costs, and improving efficiency. This review explores the latest applications, opportunities, and challenges of ML in drug discovery. ML, particularly deep learning (DL), has shown great potential in various stages of drug discovery, including drug design, screening, repurposing, and chemical synthesis. Transformer-based models, which have achieved breakthroughs in natural language processing (NLP), are now being applied to drug discovery due to their ability to capture long-range dependencies and process input in parallel. These models have demonstrated superior performance in predicting protein-protein interactions (PPIs), drug-target interactions (DTIs), and de novo drug design. For example, DeepHomo2.0 and AFTGAN have achieved high accuracy in PPI prediction, while DeepMGT-DTI and GSATDTA have improved DTI prediction. In de novo drug design, models like AlphaDrug and cMolGPT have shown promising results in generating target-specific molecules. ML also plays a crucial role in predicting molecular properties and chemical synthesis, with models like K-BERT and SMILES-BERT improving the accuracy of property predictions. However, challenges remain, including the need for large, high-quality datasets, the complexity of model selection and tuning, and the lack of interpretability in deep learning models. Despite these challenges, ML is increasingly being integrated into drug discovery processes, offering new opportunities for innovation and efficiency. Future research should focus on improving data quality, enhancing model interpretability, and combining ML with human expertise to further advance drug discovery.Machine learning (ML) is transforming drug discovery by accelerating the identification of new drugs, reducing costs, and improving efficiency. This review explores the latest applications, opportunities, and challenges of ML in drug discovery. ML, particularly deep learning (DL), has shown great potential in various stages of drug discovery, including drug design, screening, repurposing, and chemical synthesis. Transformer-based models, which have achieved breakthroughs in natural language processing (NLP), are now being applied to drug discovery due to their ability to capture long-range dependencies and process input in parallel. These models have demonstrated superior performance in predicting protein-protein interactions (PPIs), drug-target interactions (DTIs), and de novo drug design. For example, DeepHomo2.0 and AFTGAN have achieved high accuracy in PPI prediction, while DeepMGT-DTI and GSATDTA have improved DTI prediction. In de novo drug design, models like AlphaDrug and cMolGPT have shown promising results in generating target-specific molecules. ML also plays a crucial role in predicting molecular properties and chemical synthesis, with models like K-BERT and SMILES-BERT improving the accuracy of property predictions. However, challenges remain, including the need for large, high-quality datasets, the complexity of model selection and tuning, and the lack of interpretability in deep learning models. Despite these challenges, ML is increasingly being integrated into drug discovery processes, offering new opportunities for innovation and efficiency. Future research should focus on improving data quality, enhancing model interpretability, and combining ML with human expertise to further advance drug discovery.