Evaluating the outputs of ARAE-based molecular generative model for synthetic accessibility and BTK inhibition.

Generative AI-Assisted Discovery of HPK1 Inhibitors.

An early application of REINVENT, AstraZeneca's in-house generative molecular design platform, is described to identify new inhibitor scaffolds for hematopoietic progenitor kinase 1 (HPK1), demonstrating the value of integrating generative AI with medicinal chemistry expertise.

Systematic Benchmarking of AI-Based Molecular Generation Models for Structure-Based Drug Design

A state-aware functional classifier (SAFC) is developed that integrates molecular dynamics derived receptor ensembles, ensemble docking and protein ligand interaction graphs that provides dynamics-aware functional activity rankings for generated molecules that were partly complementary to docking, drug-likeness and synthetic accessibility scores.

A Systematic Evaluation of Molecule Generation Models for De Novo Drug Design: From Benchmarks to Practical Insights

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.

The Evolution of Generative Chemistry in Medicinal Chemistry

A critical perspective is provided on how generative models are shaping the future of rational and reliable drug design, including automated synthesis planning, retrosynthesis prediction, and multi‐objective optimization.

A GPT-Driven De Novo Discovery of SIRT2 Inhibitors Against Breast Cancer

AI-driven discovery of multifunctional peptides: From sequence space to therapeutics.

This review systematically examines the key methodological innovations, including peptide representation learning, multi-modal fusion strategies, multi-label learning paradigms, and emerging predictive frameworks empowered by deep neural architectures and ProtLM-based embeddings, and summarizes the practical applications of these models in peptide database mining, functional mechanism interpretation, and mutation effect prediction.