Building 2D classification models and 3D CoMSIA models on small-molecule inhibitors of both wild-type and T790M/L858R double-mutant EGFR.

Epidermal growth factor receptor (EGFR) has received widespread attention because it is an important target for anticancer drug design. Mutations in the EGFR, especially the T790M/L858R double mutation, have made cancer treatment more difficult. We herein built the structure-activity relationship models of small-molecule inhibitors on wild-type and T790M/L858R double-mutant EGFR with a whole dataset of 379 compounds. For 2D classification models, we used ECFP4 fingerprints to build support vector machine and random forest models and used SMILES to build self-attention recurrent neural network models. Each of all six models resulted in an accuracy of above 0.87 and the Matthews correlation coefficient value of above 0.76 on the test set, respectively. We concluded that inhibitors containing anilinoquinoline and methoxy or fluoro phenyl are highly active against wild EGFR. Substructures such as anilinopyrimidine, acrylamide, amino phenyl, methoxy phenyl, and thienopyrimidinyl amide appeared more in highly active inhibitors against double-mutant EGFR. We also used self-organizing map to cluster the inhibitors into six subsets based on ECFP4 fingerprints and analyzed the activity characteristics of different scaffolds in each subset. Among them, three datasets, which are based on pteridin, anilinopyrimidine, and anilinoquinoline scaffold, were selected to build 3D comparative molecular similarity analysis models individually. Models with the leave-one-out coefficient of determination (q2) above 0.65 were selected, and five descriptor types (steric, electrostatic, hydrophobic, donor, and acceptor) were used to study the effects of side chains of inhibitors on the activity against wild-type and mutant-type EGFR.

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification models to predict bioactivities of CysLT1 receptor antagonists. We built a dataset with 503 CysLT1 receptor antagonists which were divided into two groups: highly active molecules (IC50 < 1000 nM) and weakly active molecules (IC50 ≥ 1000 nM). The molecules were characterized by several descriptors including CORINA descriptors, MACCS fingerprints, Morgan fingerprint and molecular SMILES. For CORINA descriptors and two types of fingerprints, we used the random forests (RF) and deep neural networks (DNN) to build models. For molecular SMILES, we used recurrent neural networks (RNN) with the self-attention to build models. The accuracies of test sets for all models reached 85%, and the accuracy of the best model (Model 2C) was 93%. In addition, we made structure-activity relationship (SAR) analyses on CysLT1 receptor antagonists, which were based on the output from the random forest models and RNN model. It was found that highly active antagonists usually contained the common substructures such as tetrazoles, indoles and quinolines. These substructures may improve the bioactivity of the CysLT1 receptor antagonists.

Effect and Mechanism of Aluminum(III) for Guaiacol-Glyoxylic Acid Condensation Reaction in Vanillin Production.

3-methoxy-4-hydroxymandelic acid (VMA) was the critical intermediate for the synthesis of vanillin by the glyoxylic acid method. Meanwhile, a valuable byproduct (2-hydroxy-3-methoxy-mandelic acid, o-VMA) was obtained during the reaction. Al3+ was found to be a helpful catalyst in increasing the selectivity for VMA and o-VMA. In the presence of Al3+, the selectivity for VMA and o-VMA increased from 83 to 88% and from 3 to 8%, respectively, while that of the helpless byproduct 2-hydroxy-3-methoxy-1,5-mandelic acid (di-VMA) decreased from 14% to less than 4%. The kinetics based on the kinetic equation of the condensation reaction was studied by the initial concentration method. The results indicated that the involvement of Al3+ could reduce the activation energy of the reaction on the basis of the Arrhenius equation. Combined with thermogravimetric analysis, in situ Fourier transform-infrared spectroscopy, and 1H NMR research, Al3+ was found to interact with guaiacol through Al-O and Al···H, which further improved the selectivity of the VMA and o-VMA and reduced the selectivity of di-VMA by adding the electronegativity of the ortho- and para-positions of hydroxyl groups of guaiacol.