Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Abstract Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting the...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Communications Chemistry |
| Online Access: | https://doi.org/10.1038/s42004-025-01428-y |
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