Graph theory and group contributions in the estimation of boiling points

New PIN1 inhibitors identified through a pharmacophore-driven, hierarchical consensus docking strategy

PIN1 is considered as a therapeutic target for a wide variety of tumours. However, most of known inhibitors are devoid of cellular activity despite their good enzyme inhibitory profile. Hence, the lack of effective compounds for the clinic makes the identification of novel PIN1 inhibitors a hot topic in the medicinal chemistry field. In this work, we reported a virtual screening study for the identification of new promising PIN1 inhibitors. A receptor-based procedure was applied to screen different chemical databases of commercial compounds. Based on the whole workflow, two compounds were selected and biologically evaluated. Both ligands, compounds VS1 and VS2, showed a good enzyme inhibitory activity and VS2 also demonstrated a promising antitumoral activity in ovarian cancer cells. These results confirmed the reliability of our in silico protocol and provided a structurally novel ligand as a valuable starting point for the development of new PIN1 inhibitors.

Understanding and predicting the diffusivity of organic compounds in polydimethylsiloxane material for passive sampler applications using a simple quantitative structure-property relationship model

The diffusivity of 145 compounds in polydimethylsiloxane (PDMS) material was determined in the laboratory using a film stacking technique. The results were pooled with available literature data, providing a final data set of 198 compounds with diffusivity (D_PDMS ) spanning over approximately 5 log units. The principal variables controlling the diffusivity of penetrants were investigated by comparing D_PDMS within and between different homologous series. The dipole moment, molecular size, and flexibility of penetrants appear to be the most prevalent factors controlling a compound's diffusivity. A nonlinear quantitative structure-property relationship is proposed using as predicting variables the molecular volume, the number of rotatable bonds, the topological polar surface area, and the number of O and N atoms. The final relationship has a correlation coefficient of R²  = 0.81 and a mean absolute error of 0.26 m² s^-1 (log unit), approaching the average error for the experimentally determined values (0.12 m² s^-1 ). The model, based on a heuristic approach, is ready for use by analytical chemists with no specific background in theoretical chemistry (notably for passive sampler development). Environ Toxicol Chem 2018;37:1291-1300. © 2018 SETAC.

Comparison of weighting schemes for molecular graph descriptors: application in quantitative structure-retention relationship models for alkylphenols in gas-liquid chromatography

Organic compounds containing heteroatoms or multiple bonds can be conveniently represented as vertex- and edge-weighted molecular graphs. These atom and bond parameters can be computed for any organic compound with two parameter sets that we have recently defined, namely, the relative electronegativity X and the relative covalent radius Y weighting schemes. Structural descriptors computed with these two weighting schemes and the previously defined atomic number Z parameter set are used to develop quantitative structure-retention relationship (QSRR) models for alkylphenols in gas-liquid chromatography. The QSRR models are generated with structural descriptors computed with several newly introduced graph operators, namely, the Wiener, hyper-Wiener, minimum eigenvalue, maximum eigenvalue, Ivanciuc-Balaban, and information on distance operators. These molecular graph operators were applied to the distance D and the reciprocal distance RD matrixes.

Prediction of human intestinal absorption of drug compounds from molecular structure

The absorption of a drug compound through the human intestinal cell lining is an important property for potential drug candidates. Measuring this property, however, can be costly and time-consuming. The use of quantitative structure-property relationships (QSPRs) to estimate percent human intestinal absorption (%HIA) is an attractive alternative to experimental measurements. A data set of 86 drug and drug-like compounds with measured values of %HIA taken from the literature was used to develop and test a QSPR mode. The compounds were encoded with calculated molecular structure descriptors. A nonlinear computational neural network model was developed by using the genetic algorithm with a neural network fitness evaluator. The calculated %HIA (cHIA) model performs wells, with root-mean-square (rms) errors of 9.4%HIA units for the training set, 19.7%HIA units for the cross-validation (CV) set, and 16.0%HIA units for the external prediction set.