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J Comput Aided Mol Des. 2016 Feb;30(2):127-52. doi: 10.1007/s10822-016-9896-1. Epub 2016 Feb 10.

Extrapolative prediction using physically-based QSAR.

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Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA.
Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.


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.


Affinity prediction; Binding mode prediction; Extrapolation; QMOD; QSAR; Surflex

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