β Scribed by Jonas Nilsson; Sijmen de Jong; Age K. Smilde
No coin nor oath required. For personal study only.
We have introduced multilinear PLS in 3D QSAR and applied it to GRID descriptors from a set of benzamides with affinity to the dopamine D 3 receptor subtype, synthesized as potential drugs against schizophrenia. The key issue in 3D QSAR modelling is to obtain a predictive model that is easy to interpret. Each component in the multilinear PLS model explains clearly defined details, e.g. substituent positions, while the bilinear PLS solution is general and more difficult to interpret. The best models were obtained after four components with multilinear PLS (Q 2 = 51%) and after only one component with bilinear PLS (Q 2 = 50%). The external test set was predicted better with multilinear PLS (Q 2 = 31%) than with bilinear PLS (Q 2 = 25%). With multilinear PLS one loses in fit and gains in stability and simplicity owing to the fewer parameters that need to be estimated as compared with bilinear PLS. Finally, multilinear PLS is also less influenced by insignificant variation in the descriptor block, which is an advantage in 3D QSAR modelling
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VRI Variable Resolution Invariants is a new approach to quantitative structureαactivity relations that makes use of three-dimensional features of molecules at different levels of spatial resolution as well as levels of resolution in atomic properties. These descriptors are independent of any numberi
This paper describes variable selection of grid based 3D QSAR investigations. Three steroid data sets were studied using GOLPE like variable selection as well as variable selection with contiguous domains (boxes). The predictivity of the selected variable models of the training sets was in all cases
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The published microbial degradation rates of nineteen polychlorinated biphenyls (PCB) were correlated with their structure and properties. A Free-Wilson-like calculation of the de novo binding energy contributions of the chlorine substituents was applied, considering simultaneous realization of up t