Deep learning architectures like convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are particularly suited for modeling the spatial and temporal complexities of molecular interactions. These models have been used to develop new docking algorithms that can automatically learn from structural data. Reinforcement learning (RL) has been applied to optimize the docking process itself. In this approach, the docking algorithm, modeled as an agent, learns to improve its strategy for exploring the conformational space of the ligand and receptor to maximize the predicted binding affinity. Transfer learning involves applying knowledge gained from one problem domain to a different but related problem. In the context of docking, models trained on large datasets of molecular interactions can be fine-tuned with smaller, specific datasets to improve performance on particular types of targets or ligands .