Computational Drug Design in the AI Era: A Systematic Review of Molecular Representations, Generative Architectures, and Performance Assessment
Generative drug design has emerged as a transformative approach in pharmaceutical research, leveraging deep learning models to create novel molecules with targeted properties. This systematic review analyzes the current landscape of computational approaches across three critical dimensions: molecular representation strategies (1D, 2D, and 3D), generative architectural frameworks (including variational autoencoders, generative adversarial networks, reinforcement learning systems, and diffusion models), and evaluation methodologies. We provide a comprehensive categorization of these approaches, critically assessing their relative advantages and limitations. Additionally, we examine the datasets driving development in this field and the metrics employed to evaluate model performance. Through structured analysis of these interconnected components, we identify significant research gaps and propose …
