Prediction of the net heat of combustion of organic compounds based on atom-type electrotopological state indices

Least Absolute Shrinkage and Selection Operator-Based Prediction of the Binding Constant of p-Sulfonatocalix[6]/[8]arenes with Alkaloids

p-Sulfonatocalix[n]arenes (SCnA) have demonstrated great potential for drug encapsulation through host-guest complexation to improve solubility, stability, and bioavailability. In this study, the solubilization effect of SCnA (n = 4, 6, 8) on 95 active compounds derived from traditional Chinese medicine (TCM) was investigated. Based on the significant solubilization effect on alkaloids, SC6A/SC8A and 76 alkaloids were selected as the host and guest, respectively, to determine the binding constant by competitive fluorescence titration. LASSO regression was adopted to investigate the mechanism of the complex of SCnA with alkaloids. The binding constant of alkaloids-SC6A and alkaloids-SC8A was related to the alkaloid alkalinity. Also, the electronegativity, polarization, first ionization potential, hydrogen bond potential, the molecular size, and shape of alkaloids are critical properties to determine alkaloids-SC6A binding constant as well as electronegativity, polarization, hydrophobicity, and the molecular size and shape of alkaloids play an important role for the alkaloids-SC8A binding constant.

Receptor- and ligand-based study of fullerene analogues: comprehensive computational approach including quantum-chemical, QSAR and molecular docking simulations

Fullerene and its derivatives have potential antiviral activity due to their specific binding interactions with biological molecules. In this study fullerene derivatives were investigated by the synergic combination of three approaches: quantum-mechanical calculations, protein-ligand docking and quantitative structure-activity relationship methods. The protein-ligand docking studies and improved structure-activity models have been able both to predict binding affinities for the set of fullerene-C60 derivatives and to help in finding mechanisms of fullerene derivative interactions with human immunodeficiency virus type 1 aspartic protease, HIV-1 PR. Protein-ligand docking revealed several important molecular fragments that are responsible for the interaction with HIV-1 PR. In addition, a density functional theory method has been utilized to identify the optimal geometries and predict physico-chemical parameters of the studied compounds. The 5-variable GA-MLRA based model showed the best predictive ability (r(2)training = 0.882 and r(2)test = 0.738), with high internal and external correlation coefficients.

On the rational formulation of alternative fuels: melting point and net heat of combustion predictions for fuel compounds using machine learning methods

We report the development of predictive models for two fuel specifications: melting points (T(m)) and net heat of combustion (Δ(c)H). Compounds inside the scope of these models are those likely to be found in alternative fuels, i.e. hydrocarbons, alcohols and esters. Experimental T(m) and Δ(c)H values for these types of molecules have been gathered to generate a unique database. Various quantitative structure-property relationship (QSPR) approaches have been used to build models, ranging from methods leading to multi-linear models such as genetic function approximation (GFA), or partial least squares (PLS) to those leading to non-linear models such as feed-forward artificial neural networks (FFANN), general regression neural networks (GRNN), support vector machines (SVM), or graph machines. Except for the case of the graph machines method for which the only inputs are SMILES formulae, previously listed approaches working on molecular descriptors and functional group count descriptors were used to develop specific models for T(m) and Δ(c)H. For each property, the predictive models return slightly different responses for each molecular structure. Therefore, models labelled as 'consensus models' were built by averaging values computed with selected individual models. Predicted results were then compared with experimental data and with predictions of models in the literature.

A new method for predicting the heats of combustion of polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds

A new method is presented for estimating the gross and net heats of combustion of important classes of energetic compounds including polynitro arene, polynitro heteroarene, acyclic and cyclic nitramine, nitrate ester and nitroaliphatic compounds. Elemental compositions as well as the presence of some specific polar groups and molecular fragments are important parameters in the new model. The novel method can be easily used for any complex organic compounds with at least one nitro, nitramine or nitrate functional groups by which the predictions of their heats of combustion by the available methods are inaccurate or difficult. The predicted results show that this method gives reliable predictions of heats of combustion with respect to group additivity method and computed values based on atom-type electrotopological state indices for several energetic compounds where the models can be applied.