Improving Antibody Design with Force-Guided Sampling in Diffusion Models
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913493567930368 |
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| author | Kulytė, Paulina Vargas, Francisco Mathis, Simon Valentin Wang, Yu Guang Hernández-Lobato, José Miguel Liò, Pietro |
| author_facet | Kulytė, Paulina Vargas, Francisco Mathis, Simon Valentin Wang, Yu Guang Hernández-Lobato, José Miguel Liò, Pietro |
| contents | Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs determines the antibody's affinity and specificity towards its target. Generative models, particularly denoising diffusion probabilistic models (DDPMs), have shown potential to advance the structure-based design of CDR regions. However, only a limited dataset of bound antibody-antigen structures is available, and generalization to out-of-distribution interfaces remains a challenge. Physics based force-fields, which approximate atomic interactions, offer a coarse but universal source of information to better mold designs to target interfaces. Integrating this foundational information into diffusion models is, therefore, highly desirable. Here, we propose a novel approach to enhance the sampling process of diffusion models by integrating force field energy-based feedback. Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions. Through extensive experiments, we demonstrate that our method guides the model to sample CDRs with lower energy, enhancing both the structure and sequence of the generated antibodies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_05832 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Improving Antibody Design with Force-Guided Sampling in Diffusion Models Kulytė, Paulina Vargas, Francisco Mathis, Simon Valentin Wang, Yu Guang Hernández-Lobato, José Miguel Liò, Pietro Quantitative Methods Machine Learning Biomolecules Antibodies, crucial for immune defense, primarily rely on complementarity-determining regions (CDRs) to bind and neutralize antigens, such as viruses. The design of these CDRs determines the antibody's affinity and specificity towards its target. Generative models, particularly denoising diffusion probabilistic models (DDPMs), have shown potential to advance the structure-based design of CDR regions. However, only a limited dataset of bound antibody-antigen structures is available, and generalization to out-of-distribution interfaces remains a challenge. Physics based force-fields, which approximate atomic interactions, offer a coarse but universal source of information to better mold designs to target interfaces. Integrating this foundational information into diffusion models is, therefore, highly desirable. Here, we propose a novel approach to enhance the sampling process of diffusion models by integrating force field energy-based feedback. Our model, DiffForce, employs forces to guide the diffusion sampling process, effectively blending the two distributions. Through extensive experiments, we demonstrate that our method guides the model to sample CDRs with lower energy, enhancing both the structure and sequence of the generated antibodies. |
| title | Improving Antibody Design with Force-Guided Sampling in Diffusion Models |
| topic | Quantitative Methods Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2406.05832 |