Machine Learning for De Novo Molecular Generation: A Comprehensive Review

Molecular design, enabling efficient exploration of a vast chemical space that remains inaccessible to traditional experimental approaches. This review provides a comprehensive survey of machine learning-driven molecular generation, systematically organizing the field across three foundational pillars: molecular representations, model architectures, and evaluation frameworks. We present a detailed taxonomy of state-of-the-art generative models, including Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Transformers, Diffusion Models, Normalizing Flows, and Hybrid Architectures, analyzing their underlying mechanisms, comparative strengths, and inherent…

• NN
  National Natural Science Foundation of China

  Award: 22573012
• MO
  Ministry of Science and Technology of the People's Republic of China

  Award: A30YD250115-04
• SP
  Shanghai Polytechnic University

  Award: EGD25QD14