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A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was developed for fast evaluation of aqueous solubility. The model was able to predict the molar solubility of a diverse set of 1312 organic compounds with an overall correlation coefficient of 0.92 and a standard deviation of 0.72 log unit between the calculated and(More)
A new molecular lipoaffinity descriptor was introduced in this paper to account for the effect of molecular hydrophobicity on blood-brain barrier penetration. The descriptor was defined based on Kier and Hall's atom-type electrotopological state indices. Its evaluation requires 2-D molecular bonding information only. A multiple linear regression equation(More)
As novel and drug-resistant bacterial strains continue to present an emerging health threat, the development of new antibacterial agents is critical. This includes making improvements to existing antibacterial scaffolds as well as identifying novel ones. The aim of this study is to apply a Bayesian classification QSAR approach to rapidly screen chemical(More)
QSPR studies, using scores of SciTegic's extended connectivity fingerprint as raw descriptors, were extended to the prediction of melting points and aqueous solubility of organic compounds. Robust partial least-squares models were developed that perform as well as the best published QSPR models for structurally diverse organic compounds. Satisfactory(More)
Cytochrome P450 (CYP) 3A4, 2D6, 2C9, 2C19, and 1A2 are the most important drug-metabolizing enzymes in the human liver. Knowledge of which parts of a drug molecule are subject to metabolic reactions catalyzed by these enzymes is crucial for rational drug design to mitigate ADME/toxicity issues. SMARTCyp, a recently developed 2D ligand structure-based(More)
Using SciTegic's extended connectivity fingerprint as raw descriptors, a robust partial least-squares model for logP prediction was developed. The PLS model is based on 39 latent variables. An additional 8 correction factors are employed to account for effects such as intramolecular hydrogen bonding. The model performs similarly to ClogP for compounds with(More)
BACKGROUND The use of structural alerts to de-prioritize compounds with undesirable features as drug candidates has been gaining in popularity. Hundreds of molecular structural moieties have been proposed as structural alerts. An emerging issue is that strict application of these alerts will result in a significant reduction of the chemistry space for new(More)
High-precision measurements implemented with light are desired in all fields of science. However, light acts as a wave, and the Rayleigh criterion in classical optics yields a diffraction limit that prevents obtaining a resolution smaller than the wavelength. Sub-wavelength interference has potential application in lithography because it beats the classical(More)
Using a benchmark Ames mutagenicity data set, we evaluated the performance of molecular fingerprints as descriptors for developing quantitative structure-activity relationship (QSAR) models and defining applicability domains with two machine-learning methods: random forest (RF) and variable nearest neighbor (v-NN). The two methods focus on complementary(More)