Technical report: Improving the properties of molecules generated by LIMO
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910536747188224 |
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| author | Thumuluri, Vineet Eckmann, Peter Gilson, Michael K. Yu, Rose |
| author_facet | Thumuluri, Vineet Eckmann, Peter Gilson, Michael K. Yu, Rose |
| contents | This technical report investigates variants of the Latent Inceptionism on Molecules (LIMO) framework to improve the properties of generated molecules. We conduct ablative studies of molecular representation, decoder model, and surrogate model training scheme. The experiments suggest that an autogressive Transformer decoder with GroupSELFIES achieves the best average properties for the random generation task. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_14968 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Technical report: Improving the properties of molecules generated by LIMO Thumuluri, Vineet Eckmann, Peter Gilson, Michael K. Yu, Rose Machine Learning Biomolecules This technical report investigates variants of the Latent Inceptionism on Molecules (LIMO) framework to improve the properties of generated molecules. We conduct ablative studies of molecular representation, decoder model, and surrogate model training scheme. The experiments suggest that an autogressive Transformer decoder with GroupSELFIES achieves the best average properties for the random generation task. |
| title | Technical report: Improving the properties of molecules generated by LIMO |
| topic | Machine Learning Biomolecules |
| url | https://arxiv.org/abs/2407.14968 |