BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866915838668308480 |
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| author | Lee, Joongwon Zhung, Wonho Seo, Jisu Kim, Woo Youn |
| author_facet | Lee, Joongwon Zhung, Wonho Seo, Jisu Kim, Woo Youn |
| contents | Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is introduced to address this limitation by co-generating molecules and their interactions with a target protein. This approach ensures balanced consideration of key objectives, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations demonstrate that BInD achieves robust performance across all objectives, matching or surpassing state-of-the-art methods. Additionally, an NCI-driven molecule design and optimization method is proposed, enabling the enhancement of target binding and specificity by elaborating the adequate interaction patterns. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16861 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design Lee, Joongwon Zhung, Wonho Seo, Jisu Kim, Woo Youn Biomolecules Machine Learning Biological Physics Recent remarkable advancements in geometric deep generative models, coupled with accumulated structural data, enable structure-based drug design (SBDD) using only target protein information. However, existing models often struggle to balance multiple objectives, excelling only in specific tasks. BInD, a diffusion model with knowledge-based guidance, is introduced to address this limitation by co-generating molecules and their interactions with a target protein. This approach ensures balanced consideration of key objectives, including target-specific interactions, molecular properties, and local geometry. Comprehensive evaluations demonstrate that BInD achieves robust performance across all objectives, matching or surpassing state-of-the-art methods. Additionally, an NCI-driven molecule design and optimization method is proposed, enabling the enhancement of target binding and specificity by elaborating the adequate interaction patterns. |
| title | BInD: Bond and Interaction-generating Diffusion Model for Multi-objective Structure-based Drug Design |
| topic | Biomolecules Machine Learning Biological Physics |
| url | https://arxiv.org/abs/2405.16861 |