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Life sciences · Preprint
arXiv · September 9, 2026
Raises a question worth testing. It does not answer one.
This is a comprehensive computational review synthesizing 82 molecule generation models across five deep learning architectures, evaluating their performance on existing benchmarks and summarizing reported case studies. The work provides a methodological overview and research roadmap but does not include new experimental validation or clinical evidence.
Systematic review of computational methods. 82 molecule generation models across five deep learning framework classes (RNN, Transformer, VAE, GAN, flow-based, diffusion models) evaluated on reported benchmark performance and case studies. Intervention: Comprehensive evaluation of molecule generation models for de novo drug design, including benchmarks, molecular representations, and methodological principles. Compared with: Comparative analysis across model families and reported performance on commonly used benchmarks and evaluation metrics. n = 82.
Systematic evaluation of 82 molecule generation methods across five deep generative frameworks: RNN, Transformer, VAE, GAN, flow-based, and diffusion models Comparative analysis of reported performance across commonly used benchmarks and evaluation metrics for de novo drug design Summary of representative experimentally validated case studies from the literature
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
This review provides computational researchers and drug discovery teams with a structured analysis of available generative models and their relative performance on benchmarks, identifying gaps and future priorities for improving experimental relevance; however, it does not directly inform clinical practice or provide evidence on any drug molecule's efficacy or safety.
This is a systematic review of computational methods for molecular generation without new experimental validation, reporting comparative analysis of existing benchmarks and models to identify future research directions rather than establishing clinical or preclinical evidence.
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Quoted from the source exactly as published.
This review provides computational researchers and drug discovery teams with a structured analysis of available generative models and their relative performance on benchmarks, identifying gaps and future priorities for improving experimental relevance; however, it does not directly inform clinical practice or provide evidence on any drug molecule's efficacy or safety.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Molecule generation has emerged as a powerful computational tool for de novo drug design, enabling the exploration of chemical space beyond the limits of conventional virtual screening. The field has progressed rapidly, driven by advances in molecular representations, generative architectures, and target-aware modeling strategies. However, existing reviews typically address specific model families or application scenarios in isolation, rather than offering an integrated perspective on how these components collectively form a coherent generation workflow. In this review, we present a comprehensive evaluation of molecule generation models for de novo drug design, covering 82 methods across five deep generative frameworks, including recurrent neural network (RNN)- and Transformer-based models, variational autoencoders (VAEs), generative adversarial networks (GANs), flow-based models, and diffusion models. We first summarize widely used benchmarks and molecular representations, and then examine the methodological principles underlying both general and pocket-conditioned generation. A central contribution of this work is a systematic synthesis and comparative analysis of reported performance across commonly used benchmarks and evaluation metrics. We also summarize representative experimentally validated case studies. Looking ahead, we discuss future directions in standardized 3D data, interaction-aware generation, receptor flexibility, and multi-objective molecular design, with the aim of improving the reliability and experimental relevance of molecule generation. All collected benchmark resources, evaluation metrics, and model references are provided in a publicly accessible repository at https://github.com/JacklinGroup/molecule-generation-review.
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