MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912390914768896 |
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| author | Ortega-Ochoa, Raul Vegge, Tejs Frellsen, Jes |
| author_facet | Ortega-Ochoa, Raul Vegge, Tejs Frellsen, Jes |
| contents | We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports conditional generation of molecules over twelve properties, enabling flexible control across physicochemical and structural targets. Molecules are built via symmetry-aware fragment attachments, with 3D geometry dynamically updated during generation using forcefields. A probabilistic conditioning mechanism allows users to specify any subset of target properties while sampling the rest. MolMiner achieves calibrated conditional generation across most properties and offers competitive unconditional performance. We also propose improved benchmarking methods for both unconditional and conditional generation, including distributional comparisons via Wasserstein distance and calibration plots for property control. To our knowledge, this is the first model to unify dynamic geometry, symmetry handling, order-agnostic fragment-based generation, and high-dimensional multi-property conditioning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06608 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design Ortega-Ochoa, Raul Vegge, Tejs Frellsen, Jes Machine Learning Materials Science We introduce MolMiner, a fragment-based, geometry-aware, and order-agnostic autoregressive model for molecular design. MolMiner supports conditional generation of molecules over twelve properties, enabling flexible control across physicochemical and structural targets. Molecules are built via symmetry-aware fragment attachments, with 3D geometry dynamically updated during generation using forcefields. A probabilistic conditioning mechanism allows users to specify any subset of target properties while sampling the rest. MolMiner achieves calibrated conditional generation across most properties and offers competitive unconditional performance. We also propose improved benchmarking methods for both unconditional and conditional generation, including distributional comparisons via Wasserstein distance and calibration plots for property control. To our knowledge, this is the first model to unify dynamic geometry, symmetry handling, order-agnostic fragment-based generation, and high-dimensional multi-property conditioning. |
| title | MolMiner: Towards Controllable, 3D-Aware, Fragment-Based Molecular Design |
| topic | Machine Learning Materials Science |
| url | https://arxiv.org/abs/2411.06608 |