An algorithmic framework for synthetic cost-aware decision making in molecular design
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866929361332994048 |
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| author | Fromer, Jenna C. Coley, Connor W. |
| author_facet | Fromer, Jenna C. Coley, Connor W. |
| contents | Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing, and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. We propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction, and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate through three case studies that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules. SPARROW is open source and can be found at http://github.com/coleygroup/sparrow. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_02187 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | An algorithmic framework for synthetic cost-aware decision making in molecular design Fromer, Jenna C. Coley, Connor W. Quantitative Methods Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing, and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. We propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction, and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate through three case studies that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules. SPARROW is open source and can be found at http://github.com/coleygroup/sparrow. |
| title | An algorithmic framework for synthetic cost-aware decision making in molecular design |
| topic | Quantitative Methods |
| url | https://arxiv.org/abs/2311.02187 |