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2016 ; 30
(2
): 127-52
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Extrapolative prediction using physically-based QSAR
#MMPMID26860112
Cleves AE
; Jain AN
J Comput Aided Mol Des
2016[Feb]; 30
(2
): 127-52
PMID26860112
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Surflex-QMOD integrates chemical structure and activity data to produce
physically-realistic models for binding affinity prediction . Here, we apply QMOD
to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of
targets. Testing new ligands within the QMOD model employs automated flexible
molecular alignment, with the model itself defining the optimal pose for each
ligand. QMOD performance was compared to that of four approaches that depended on
manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed
comparable performance to the other methods on a challenging, but structurally
limited, test set. The QMOD models were also applied to test a large and
structurally diverse dataset of ligands from ChEMBL, nearly all of which were
synthesized years after those used for model construction. Extrapolation across
diverse chemical structures was possible because the method addresses the ligand
pose problem and provides structural and geometric means to quantitatively
identify ligands within a model's applicability domain. Predictions for such
ligands for the four tested targets were highly statistically significant based
on rank correlation. Those molecules predicted to be highly active (pK(i) ? 7.5)
had a mean experimental pK(i) of 7.5, with potent and structurally novel ligands
being identified by QMOD for each target.
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