Novel, Customizable Scoring Functions, Parameterized Using N-PLS, for Structure-Based Drug Discovery

• Published in: Journal of chemical information and modeling 2007-01, Vol.47 (1), p.85-91
• Main Authors: Catana, Cornel, Stouten, Pieter F. W
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Computational Biology; Drug Design; Drugs; Pharmaceutical industry; Protein Binding; Proteins; Quantitative Structure-Activity Relationship; Software
• ISSN: 1549-9596; 1549-960X
• DOI: 10.1021/ci600357t

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, “standard” scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning “customized” scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein−ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure−activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r 2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r 2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.