Towards DNA-Encoded Library Generation with GFlowNets
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910411811454976 |
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| author | Koziarski, Michał Abukalam, Mohammed Shah, Vedant Vaillancourt, Louis Schuetz, Doris Alexandra Jain, Moksh van der Sloot, Almer Bourgey, Mathieu Marinier, Anne Bengio, Yoshua |
| author_facet | Koziarski, Michał Abukalam, Mohammed Shah, Vedant Vaillancourt, Louis Schuetz, Doris Alexandra Jain, Moksh van der Sloot, Almer Bourgey, Mathieu Marinier, Anne Bengio, Yoshua |
| contents | DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds. One of the key challenges in using DELs is library design, which involves choosing the building blocks that will be combinatorially combined to produce the final library. In this paper we consider the task of protein-protein interaction (PPI) biased DEL design. To this end, we evaluate several machine learning algorithms on the PPI modulation task and use them as a reward for the proposed GFlowNet-based generative approach. We additionally investigate the possibility of using structural information about building blocks to design a hierarchical action space for the GFlowNet. The observed results indicate that GFlowNets are a promising approach for generating diverse combinatorial library candidates. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_10094 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards DNA-Encoded Library Generation with GFlowNets Koziarski, Michał Abukalam, Mohammed Shah, Vedant Vaillancourt, Louis Schuetz, Doris Alexandra Jain, Moksh van der Sloot, Almer Bourgey, Mathieu Marinier, Anne Bengio, Yoshua Machine Learning Quantitative Methods DNA-encoded libraries (DELs) are a powerful approach for rapidly screening large numbers of diverse compounds. One of the key challenges in using DELs is library design, which involves choosing the building blocks that will be combinatorially combined to produce the final library. In this paper we consider the task of protein-protein interaction (PPI) biased DEL design. To this end, we evaluate several machine learning algorithms on the PPI modulation task and use them as a reward for the proposed GFlowNet-based generative approach. We additionally investigate the possibility of using structural information about building blocks to design a hierarchical action space for the GFlowNet. The observed results indicate that GFlowNets are a promising approach for generating diverse combinatorial library candidates. |
| title | Towards DNA-Encoded Library Generation with GFlowNets |
| topic | Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2404.10094 |