{"ID":2896726,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.07201","arxiv_id":"2507.07201","title":"MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation","abstract":"Three-dimensional molecular generators based on diffusion models can now reach near-crystallographic accuracy, yet they remain fragmented across tasks. SMILES-only inputs, two-stage pretrain-finetune pipelines, and one-task-one-model practices hinder stereochemical fidelity, task alignment, and zero-shot transfer. We introduce MODA, a diffusion framework that unifies fragment growing, linker design, scaffold hopping, and side-chain decoration with a Bayesian mask scheduler. During training, a contiguous spatial fragment is masked and then denoised in one pass, enabling the model to learn shared geometric and chemical priors across tasks. Multi-task training yields a universal backbone that surpasses six diffusion baselines and three training paradigms on substructure, chemical property, interaction, and geometry. Model-C reduces ligand-protein clashes and substructure divergences while maintaining Lipinski compliance, whereas Model-B preserves similarity but trails in novelty and binding affinity. Zero-shot de novo design and lead-optimisation tests confirm stable negative Vina scores and high improvement rates without force-field refinement. These results demonstrate that a single-stage multi-task diffusion routine can replace two-stage workflows for structure-based molecular design.","short_abstract":"Three-dimensional molecular generators based on diffusion models can now reach near-crystallographic accuracy, yet they remain fragmented across tasks. SMILES-only inputs, two-stage pretrain-finetune pipelines, and one-task-one-model practices hinder stereochemical fidelity, task alignment, and zero-shot transfer. We i...","url_abs":"https://arxiv.org/abs/2507.07201","url_pdf":"https://arxiv.org/pdf/2507.07201v1","authors":"[\"Dong Xu\",\"Zhangfan Yang\",\"Sisi Yuan\",\"Jenna Xinyi Yao\",\"Jiangqiang Li\",\"Junkai Ji\"]","published":"2025-07-09T18:19:50Z","proceeding":"q-bio.BM","tasks":"[\"q-bio.BM\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Diffusion Model\",\"Generative Adversarial Network\"]","has_code":false}