Comparison of four models to predict intrinsic solubility of drugs

The aqueous solubility of drugs/drug candidates (Sw) is one of the crucial physicochemical parameters in drug discovery studies and any computational method to predict the solubility is highly in demand in the pharmaceutical industry. This work is aimed to compare the accuracy of a recently proposed model (logSw=-1.120E-0.599ClogP) composed of two computational descriptors; excess molar refraction (E) and calculated partition coefficient of octanol to water (ClogP) with the accuracies of the Hansch model, general solubility equation and linear solvation energy relationship model. These results showed that the prediction capability of the proposed model is better than those of three famous models and the E is a crucial descriptor for aqueous solubility prediction of drugs and drug-like molecules.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Using the modified k-mean algorithm with an improved teaching-learning-based optimization algorithm for feedforward neural network training

In this paper we proposed a novel procedure for training a feedforward neural network. The accuracy of artificial neural network outputs after determining the proper structure for each problem depends on choosing the appropriate method for determining the best weights, which is the appropriate training algorithm. If the training algorithm starts from a good starting point, it is several steps closer to achieving global optimization. In this paper, we present an optimization strategy for selecting the initial population and determining the optimal weights with the aim of minimizing neural network error. Teaching-learning-based optimization (TLBO) is a less parametric algorithm rather than other evolutionary algorithms, so it is easier to implement. We have improved this algorithm to increase efficiency and balance between global and local search. The improved teaching-learning-based optimization (ITLBO) algorithm has added the concept of neighborhood to the basic algorithm, which improves the ability of global search. Using an initial population that includes the best cluster centers after clustering with the modified k-mean algorithm also helps the algorithm to achieve global optimum. The results are promising, close to optimal, and better than other approach which we compared our proposed algorithm with them

Solubility of Tris(hydroxymethyl)aminomethane in Water + Methanol +1-Propanol Mixtures at Various Temperatures

Article on the solubility of tris(hydroxymethyl)aminomethane in water + methanol + 1-propanol mixtures at various temperatures

Comparison of four models to predict intrinsic solubility of drugs

The aqueous solubility of drugs/drug candidates (Sw) is one of the crucial physicochemical parameters in drug discovery studies and any computational method to predict the solubility is highly in demand in the pharmaceutical industry. This work is aimed to compare the accuracy of a recently proposed model (logSw=-1.120E-0.599ClogP) composed of two computational descriptors; excess molar refraction (E) and calculated partition coefficient of octanol to water (ClogP) with the accuracies of the Hansch model, general solubility equation and linear solvation energy relationship model. These results showed that the prediction capability of the proposed model is better than those of three famous models and the E is a crucial descriptor for aqueous solubility prediction of drugs and drug-like molecules.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Solubility of Tris(hydroxymethyl)aminomethane in Water + 1-Propanol Mixtures at Various Temperatures

Article on the solubility of tris(hydroxymethyl)aminomethane in water + 1-propanol mixtures at various temperatures