An improved QSPR study of the toxicity of aliphatic carboxylic acids using genetic algorithm

Surface Tension of Liquid Organic Acids: An Artificial Neural Network Model

An artificial neural network model is proposed for the surface tension of liquid organic fatty acids covering a wide temperature range. A set of 2051 data collected for 98 acids (including carboxylic, aliphatic, and polyfunctional) was considered for the training, testing, and prediction of the resulting network model. Different architectures were explored, with the final choice giving the best results, in which the input layer has the reduced temperature (temperature divided by the critical point temperature), boiling temperature, and acentric factor as an independent variable, a 41-neuron hidden layer, and an output layer consisting of one neuron. The overall absolute percentage deviation is 1.33%, and the maximum percentage deviation is 14.53%. These results constitute a major improvement over the accuracy obtained using corresponding-states correlations from the literature.

Predicting the DPP-IV inhibitory activity pIC₅₀ based on their physicochemical properties

The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.

Development predictive QSAR models for artemisinin analogues by various feature selection methods: a comparative study

Quantitative structure-activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. The activities of these compounds were investigated by means of multiple linear regression (MLR). To select relevant descriptors, several methods including stepwise selection, successive projection algorithm and an ant colony optimization algorithm (called memorized_ACS) were employed. A wide variety of molecular descriptors belonging to various structural properties were calculated for each molecule. Two matrixes (D1 and D2) of molecular properties were built. The D1 matrix included the calculated descriptors and the D2 matrix contained the first to third orders of the calculated descriptors and the logarithm of absolute values of the calculated descriptors. For both data matrixes, significant QSAR models were obtained by the memorized_ACS algorithm. The reactive and PEOE (partial equalization of orbital electronegativity) descriptors represented the highest impact on the antimalarial activity. The PEOE descriptors belong to partial charge descriptors and the reactive descriptor is an indicator of the presence of the reactive groups in the molecule. The best MLR model has a training error of 0.71 log RA units (r (2 )= 0.81) and a prediction error of 0.48 log RA units (r (2) = 0.88).

Inhibition of three algae species using chemicals released from barley straw

Algal blooms are a significant problem in the UK, particularly in water sources that supply potable water treatment works. A wide range of methods to control algae have been tested and, whilst many are effective, they all have disadvantages. The use of barley straw to control algal growth in reservoirs is one option that is gaining popularity, but little is known about its mode of action. One suggested mechanism is that, as the straw is broken down, algastatic chemicals such as phenolics are released. Here we have used an algae inhibition test to evaluate the effect of chemicals reported to be released from straw on three common algal species: Chlorella vulgaris, Microcystis aeruginosa and Scenedesmus subspicatus. It was shown that, of the chemicals assessed, many produced an algastatic effect on the growth of the three algal species tested, with 2 phenyl-phenol being the most effective, whilst p-cresol and benzaldehyde were shown to be effective at concentrations similar to those that have been reported downstream of rotted straw. Scenedesmus subspicatus proved to be much more resistant to the chemicals tested than the other species.