From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909223208615936 |
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| author | Gao, Bowen Tan, Haichuan Huang, Yanwen Ren, Minsi Huang, Xiao Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan |
| author_facet | Gao, Bowen Tan, Haichuan Huang, Yanwen Ren, Minsi Huang, Xiao Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan |
| contents | Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability of the Vina docking score, the current standard for assessing binding abilities, is increasingly questioned due to its susceptibility to overfitting. To address these limitations, we propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds, introducing a virtual screening-based metric for practical deployment capabilities, and re-evaluating binding affinity more rigorously. Our experiments reveal that while current SBDD models achieve high Vina scores, they fall short in practical usability metrics, highlighting a significant gap between theoretical predictions and real-world applicability. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models and aligning them more closely with the needs of pharmaceutical research and development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08980 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics Gao, Bowen Tan, Haichuan Huang, Yanwen Ren, Minsi Huang, Xiao Ma, Wei-Ying Zhang, Ya-Qin Lan, Yanyan Biomolecules Machine Learning Recent advancements in structure-based drug design (SBDD) have significantly enhanced the efficiency and precision of drug discovery by generating molecules tailored to bind specific protein pockets. Despite these technological strides, their practical application in real-world drug development remains challenging due to the complexities of synthesizing and testing these molecules. The reliability of the Vina docking score, the current standard for assessing binding abilities, is increasingly questioned due to its susceptibility to overfitting. To address these limitations, we propose a comprehensive evaluation framework that includes assessing the similarity of generated molecules to known active compounds, introducing a virtual screening-based metric for practical deployment capabilities, and re-evaluating binding affinity more rigorously. Our experiments reveal that while current SBDD models achieve high Vina scores, they fall short in practical usability metrics, highlighting a significant gap between theoretical predictions and real-world applicability. Our proposed metrics and dataset aim to bridge this gap, enhancing the practical applicability of future SBDD models and aligning them more closely with the needs of pharmaceutical research and development. |
| title | From Theory to Therapy: Reframing SBDD Model Evaluation via Practical Metrics |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2406.08980 |