Towards Efficient Molecular Property Optimization with Graph Energy Based Models
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908412164440064 |
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| author | Miglior, Luca Simone, Lorenzo Podda, Marco Bacciu, Davide |
| author_facet | Miglior, Luca Simone, Lorenzo Podda, Marco Bacciu, Davide |
| contents | Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generate molecules that satisfy target properties without explicit conditional generation. We use Graph Energy Based Models and a training approach that does not require property labels. We validated our approach on well-established chemical benchmarks, showing superior results to state-of-the-art methods and demonstrating robustness and efficiency towards de novo drug design. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_12219 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Efficient Molecular Property Optimization with Graph Energy Based Models Miglior, Luca Simone, Lorenzo Podda, Marco Bacciu, Davide Biomolecules Machine Learning Optimizing chemical properties is a challenging task due to the vastness and complexity of chemical space. Here, we present a generative energy-based architecture for implicit chemical property optimization, designed to efficiently generate molecules that satisfy target properties without explicit conditional generation. We use Graph Energy Based Models and a training approach that does not require property labels. We validated our approach on well-established chemical benchmarks, showing superior results to state-of-the-art methods and demonstrating robustness and efficiency towards de novo drug design. |
| title | Towards Efficient Molecular Property Optimization with Graph Energy Based Models |
| topic | Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2502.12219 |