Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917975470112768 |
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| author | Liu, Songtao Zhang, Dandan Tu, Zhengkai Dai, Hanjun Liu, Peng |
| author_facet | Liu, Songtao Zhang, Dandan Tu, Zhengkai Dai, Hanjun Liu, Peng |
| contents | A significant challenge in wet lab experiments with current drug design generative models is the trade-off between pharmacological properties and synthesizability. Molecules predicted to have highly desirable properties are often difficult to synthesize, while those that are easily synthesizable tend to exhibit less favorable properties. As a result, evaluating the synthesizability of molecules in general drug design scenarios remains a significant challenge in the field of drug discovery. The commonly used synthetic accessibility (SA) score aims to evaluate the ease of synthesizing generated molecules, but it falls short of guaranteeing that synthetic routes can actually be found. Inspired by recent advances in top-down synthetic route generation and forward reaction prediction, we propose a new, data-driven metric to evaluate molecule synthesizability. This novel metric leverages the synergistic duality between retrosynthetic planners and reaction predictors, both of which are trained on extensive reaction datasets. To demonstrate the efficacy of our metric, we conduct a comprehensive evaluation of round-trip scores across a range of representative molecule generative models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08306 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction Liu, Songtao Zhang, Dandan Tu, Zhengkai Dai, Hanjun Liu, Peng Machine Learning Quantitative Methods A significant challenge in wet lab experiments with current drug design generative models is the trade-off between pharmacological properties and synthesizability. Molecules predicted to have highly desirable properties are often difficult to synthesize, while those that are easily synthesizable tend to exhibit less favorable properties. As a result, evaluating the synthesizability of molecules in general drug design scenarios remains a significant challenge in the field of drug discovery. The commonly used synthetic accessibility (SA) score aims to evaluate the ease of synthesizing generated molecules, but it falls short of guaranteeing that synthetic routes can actually be found. Inspired by recent advances in top-down synthetic route generation and forward reaction prediction, we propose a new, data-driven metric to evaluate molecule synthesizability. This novel metric leverages the synergistic duality between retrosynthetic planners and reaction predictors, both of which are trained on extensive reaction datasets. To demonstrate the efficacy of our metric, we conduct a comprehensive evaluation of round-trip scores across a range of representative molecule generative models. |
| title | Evaluating Molecule Synthesizability via Retrosynthetic Planning and Reaction Prediction |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2411.08306 |