From potential to practice: The prospective and pitfalls of generative AI and deep learning in molecular simulations

Generative AI and deep learning improve molecular simulations and drug development. Traditional computational methods like MD, MC, and QM/MM have been crucial in investigating biomolecular interactions and thermodynamics. However, processing power and speed restrict their scalability. This article provides a comprehensive review and comparative analysis of how advanced neural network architectures and generative AI models address these computational limitations. This review analyses how advanced neural network architectures and generative AI models satisfy these restrictions. Neural network potentials trained on high-quality quantum datasets achieve ab initio precision at low processing cost. We tested convolutional (CNNs), recurrent (RNNs), graph neural networks (GNNs), and transformers to evaluate how well they could describe molecular changes over time and predict structural changes. Researchers have investigated generative frameworks including variational autoencoders (VAEs), generative adversarial networks (GANs), and diffusion models to develop medications with superior binding affinity and pharmacokinetic characteristics. The findings reveal that AI-driven modelling and physics-based simulations create a closed-loop system where MD or QM/MM simulations enhance AI-generated molecules repeatedly. This feedback loop speeds up hit-to-lead optimisation, increases ADMET prediction, and enhances protein folding and shape information. This paradigm shift from descriptive to predictive and generative frameworks using AI and molecular modelling improves computational drug discovery's scalability, interpretability, and creativity. AI is used as a computational tool and a collaborator to speed up molecular discovery. Overall, this manuscript serves as a critical review summarizing state-of-the-art progress, challenges, and future prospects at the interface of AI and molecular simulation research.