AI-Designed Molecules in Drug Discovery, Structural Novelty Evaluation, and Implications

Achieving structural novelty in drug discovery remains
a critical
challenge. Artificial intelligence (AI) has demonstrated remarkable
potential in deciphering the complex relationships between molecular
structures and activities from vast amounts of chemical and biological
information. However, its ability to explore novel chemical spaces
is underexplored. This study evaluates the structural novelty of AI-designed
active compounds across 71 cases published in recent years. Ligand-based
models often yield molecules with relatively low novelty (Tcmax > 0.4 in 58.1% of cases), whereas structure-based approaches
exhibit
better performance (17.9% with Tcmax > 0.4). Screening
workflows significantly influence the novelty, with underexplored
targets benefiting from structure-based methods. However, fingerprint-based
similarity metrics may fail to detect scaffold-level similarities.
Systematic novelty assessment and manual verification are essential
to avoid structural homogenization. This Review provides insights
for optimizing AI-driven drug discovery and underscores the need for
interdisciplinary collaboration to balance novelty and activity. Specifically,
we recommend the use of diverse training data sets, scaffold-hopping
aware similarity metrics, and careful consideration of similarity
filters in AI-driven drug discovery workflows.