Narrowing the gap between machine learning scoring functions and free energy perturbation using augm...
Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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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 their application in drug discovery. Here, we address these is...
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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
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TN_cdi_doaj_primary_oai_doaj_org_article_29105d7176d647a788aa531784053348
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_29105d7176d647a788aa531784053348
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ISSN
2399-3669
E-ISSN
2399-3669
DOI
10.1038/s42004-025-01428-y
