Harnessing Deep Learning and Generative AI for Molecular Docking Simulations: Tools, Challenges, and Future Directions
Bilal Shaker, Khaled Barakat · Biomedical engineering · 2025
Molecular docking has become a cornerstone in modern drug discovery, helping scientists predict how small molecules, or ligands, interact with target proteins. With the rise of artificial intelligence, particularly deep learning and generative models, traditional docking methods have been transformed. These AI-powered approaches not only boost accuracy but also dramatically reduce the time and cost of early-phase drug screening. This review explores how deep learning and generative AI are being applied to molecular docking simulations. Tools like AtomNet, DeepDock, KDEEP, and AutoDock Vina, when integrated with neural networks, have shown impressive improvements in predicting protein-ligand binding affinities compared to older docking methods. These AI models can rapidly screen massive chemical libraries, accelerating the identification of potential drug candidates. For instance, AtomNet, one of the first deep convolutional networks used in structure-based drug discovery, contributed to early drug leads for diseases like Ebola and cancer. AlphaFold, from DeepMind, has also revolutionized protein structure prediction, setting a new benchmark for accuracy. Beyond raw prediction power, these tools adapt over time. They learn from new data and minimizing human bias in compound selection. Their scalable architecture enables repeated simulations with increasing precision. This synergy of human intuition and AI precision marks a turning point in drug discovery. By using AI-driven molecular docking, researchers can now prioritize compounds more strategically, paving the way for faster, more efficient development of new treatments.