{"ID":2921870,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T21:41:48.07062823Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01461","arxiv_id":"2606.01461","title":"Genotype-Conditioned Molecular Generation via Evidence-Grounded Multi-Objective Latent Perturbation in Diffusion Models","abstract":"Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative models conditioned on cancer genotypes offer a promising avenue for personalized drug discovery, yet existing approaches lack explicit optimization for simultaneous sensitivity, synthesizability, and mechanistic binding plausibility. We present a latent-space optimization approach for a pretrained genotype-to-drug diffusion model, introducing a learnable perturbation over the molecular latent space optimized via gradient ascent to maximize a composite reward combining predicted drug sensitivity (AUC), drug-likeness (QED), and synthetic accessibility (SAS). Critically, biological realism is enforced by grounding both reward design and evaluation in experimentally-derived cancer cell line data and validated pharmacologic signals, anchoring candidate generation in real-world clinical evidence. Mechanistic consistency plausibility is further assessed by a multi-agent LLM pipeline grounded in the diffusion model's attention mechanism. Experiments across 15 cancer cell lines from three held-out evaluation sets demonstrate consistent and noticeable improvements over competing baselines in sensitivity, drug-likeness, synthesizability, and chemical validity.","short_abstract":"Developing effective anticancer therapeutics remains challenging due to tumor heterogeneity and the absence of well-defined molecular targets across cancer subtypes. Generative models conditioned on cancer genotypes offer a promising avenue for personalized drug discovery, yet existing approaches lack explicit optimiza...","url_abs":"https://arxiv.org/abs/2606.01461","url_pdf":"https://arxiv.org/pdf/2606.01461v1","authors":"[\"Brenda Nogueira\",\"Gisela A. Gonzalez-Montiel\",\"Nitesh V. Chawla\",\"Nuno Moniz\"]","published":"2026-05-31T21:43:11Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.MA\"]","methods":"[\"Diffusion Model\",\"Large Language Model\"]","has_code":false}