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| Autori principali: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2503.06687 |
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| _version_ | 1866912555325194240 |
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| author | Zhang, Gongbo Li, Yanting Luo, Renqian Hu, Pipi Yang, Yang Zhao, Zeru Li, Lingbo Liu, Guoqing Wang, Zun Bi, Ran Gao, Kaiyuan Guo, Liya Xie, Yu Liu, Chang Zhang, Jia Xie, Tian Pinsler, Robert Zeni, Claudio Lu, Ziheng Hao, Hongxia Xia, Yingce Segler, Marwin Riechert, Maik Yang, Wei Jiang, Hao Zhang, Wen-Bin Zeng, Zhijun Zhu, Yi Dong, Li Hu, Xiuyuan Yuan, Li Chen, Lei Liu, Haiguang Qin, Tao |
| author_facet | Zhang, Gongbo Li, Yanting Luo, Renqian Hu, Pipi Yang, Yang Zhao, Zeru Li, Lingbo Liu, Guoqing Wang, Zun Bi, Ran Gao, Kaiyuan Guo, Liya Xie, Yu Liu, Chang Zhang, Jia Xie, Tian Pinsler, Robert Zeni, Claudio Lu, Ziheng Hao, Hongxia Xia, Yingce Segler, Marwin Riechert, Maik Yang, Wei Jiang, Hao Zhang, Wen-Bin Zeng, Zhijun Zhu, Yi Dong, Li Hu, Xiuyuan Yuan, Li Chen, Lei Liu, Haiguang Qin, Tao |
| contents | Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are under-modeled. We present UniGenX, a unified generative foundation model that addresses these gaps by co-generating sequences and coordinates under direct functional and property objectives across proteins, molecules, and materials. UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens, where a decoder-only autoregressive transformer provides global context and a conditional diffusion head generates numeric fields steered by task-specific tokens. Besides the new high SOTAs on structure prediction tasks, the model demonstrates state-of-the-art or competitive performance for the function-aware generation across domains: in materials, it achieves "conflicted" multi-property conditional generation, yielding 436 crystal candidates meeting triple constraints, including 11 with novel compositions; in chemistry, it sets new benchmarks on five property targets and conformer ensemble generation on GEOM; and in biology, it improves success in modeling protein induced fit (RMSD < 2 Å) by over 23-fold and enhances EC-conditioned enzyme design. Ablation studies and cross-domain transfer substantiate the benefits of joint discrete-continuous training, establishing UniGenX as a significant advance from prediction to controllable, function-aware generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_06687 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials Zhang, Gongbo Li, Yanting Luo, Renqian Hu, Pipi Yang, Yang Zhao, Zeru Li, Lingbo Liu, Guoqing Wang, Zun Bi, Ran Gao, Kaiyuan Guo, Liya Xie, Yu Liu, Chang Zhang, Jia Xie, Tian Pinsler, Robert Zeni, Claudio Lu, Ziheng Hao, Hongxia Xia, Yingce Segler, Marwin Riechert, Maik Yang, Wei Jiang, Hao Zhang, Wen-Bin Zeng, Zhijun Zhu, Yi Dong, Li Hu, Xiuyuan Yuan, Li Chen, Lei Liu, Haiguang Qin, Tao Machine Learning Materials Science Artificial Intelligence Biological Physics Chemical Physics Function in natural systems arises from one-dimensional sequences forming three-dimensional structures with specific properties. However, current generative models suffer from critical limitations: training objectives seldom target function directly, discrete sequences and continuous coordinates are optimized in isolation, and conformational ensembles are under-modeled. We present UniGenX, a unified generative foundation model that addresses these gaps by co-generating sequences and coordinates under direct functional and property objectives across proteins, molecules, and materials. UniGenX represents heterogeneous inputs as a mixed stream of symbolic and numeric tokens, where a decoder-only autoregressive transformer provides global context and a conditional diffusion head generates numeric fields steered by task-specific tokens. Besides the new high SOTAs on structure prediction tasks, the model demonstrates state-of-the-art or competitive performance for the function-aware generation across domains: in materials, it achieves "conflicted" multi-property conditional generation, yielding 436 crystal candidates meeting triple constraints, including 11 with novel compositions; in chemistry, it sets new benchmarks on five property targets and conformer ensemble generation on GEOM; and in biology, it improves success in modeling protein induced fit (RMSD < 2 Å) by over 23-fold and enhances EC-conditioned enzyme design. Ablation studies and cross-domain transfer substantiate the benefits of joint discrete-continuous training, establishing UniGenX as a significant advance from prediction to controllable, function-aware generation. |
| title | UniGenX: a unified generative foundation model that couples sequence, structure and function to accelerate scientific design across proteins, molecules and materials |
| topic | Machine Learning Materials Science Artificial Intelligence Biological Physics Chemical Physics |
| url | https://arxiv.org/abs/2503.06687 |