A genotype-to-drug diffusion model for generation of tailored anti-cancer small molecules

Hyunho Kim, Bongsung Bae, Minsu Park, Yewon Shin, Trey Ideker () and Hojung Nam ()
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Hyunho Kim: Gwangju Institute of Science and Technology
Bongsung Bae: Gwangju Institute of Science and Technology
Minsu Park: Gwangju Institute of Science and Technology
Yewon Shin: Gwangju Institute of Science and Technology
Trey Ideker: University of California San Diego
Hojung Nam: Gwangju Institute of Science and Technology

Nature Communications, 2025, vol. 16, issue 1, 1-16

Abstract: Abstract Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligence (AI) offers a promising opportunity to address this challenge by enabling the design of hit-like anti-cancer molecules conditioned on complex genomic features. We present Genotype-to-Drug Diffusion (G2D-Diff), a generative AI approach for creating small molecule-based drug structures tailored to specific cancer genotypes. G2D-Diff demonstrates exceptional performance in generating diverse, drug-like compounds that meet desired efficacy conditions for a given genotype. The model outperforms existing methods in diversity, feasibility, and condition fitness. G2D-Diff learns directly from drug response data distributions, ensuring reliable candidate generation without separate predictors. Its attention mechanism provides insights into potential cancer targets and pathways, enhancing interpretability. In triple-negative breast cancer case studies, G2D-Diff generated plausible hit-like candidates by focusing on relevant pathways. By combining realistic hit-like molecule generation with relevant pathway suggestions for specific genotypes, G2D-Diff represents a significant advance in AI-guided, personalized drug discovery. This approach has the potential to accelerate drug development for challenging cancers by streamlining hit identification.

Date: 2025
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DOI: 10.1038/s41467-025-60763-9

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