A scientist's guide to AI-driven molecular discovery

Artificial intelligence (AI) is increasingly steering the discovery of functional molecules and materials, but its progress with generative modeling is held back by the messy, mixed-up nature of the experimental data and a scarcity of high-quality ground truth. This review synthesizes recent advances in data curation, representation, and generative modeling for molecular and materials discovery, and proposes a practical four-stage workflow that integrates structured data capture, intelligent featurization, generative design, and closed-loop experimental validation. Core algorithmic families (supervised, semi-supervised, unsupervised, reinforcement learning) and specialized generative architectures (VAEs, GANs, diffusion models, graph-based models) are surveyed, and discuss how each maps to real-world discovery tasks. The enabling infrastructure (e.g.as electronic lab notebooks (ELNs), knowledge graphs, autonomous laboratories) is likewise analyzed and highlight best practices for reproducibility, uncertainty quantification, and ethical safeguards. Finally, a prioritized checklist was provided for researchers and laboratories to adopt AI-compatible infrastructure and describe open challenges (data standards, causal inference, accessibility) to guide future work.