Design and synthesis of aminophenol-based factor Xa inhibitors

Prediction of factor Xa inhibitors by machine learning methods

Factor Xa (FXa) inhibitors have been explored as anticoagulants for treatment and prevention of thrombotic diseases. Molecular docking, pharmacophore, quantitative structure-activity relationships, and support vector machines (SVM) have been used for computer prediction of FXa inhibitors. These methods achieve promising prediction accuracies of 69-80% for FXa inhibitors and 85-99% for non-inhibitors. Prediction performance, particularly for inhibitors, may be further improved by exploring methods applicable to more diverse range of compounds and by using more appropriate set of molecular descriptors. We tested the capability of several machine learning methods (C4.5 decision tree, k-nearest neighbor, probabilistic neural network, and support vector machine) by using a much more diverse set of 1098 compounds (360 inhibitors and 738 non-inhibitors) than those in other studies. A feature selection method was used for selecting molecular descriptors appropriate for distinguishing FXa inhibitors and non-inhibitors. The prediction accuracies of these methods are 89.1-97.5% for FXa inhibitors and 92.3-98.1% for non-inhibitors. In particular, compared to other studies, support vector machine gives a substantially improved accuracy of 94.6% for FXa non-inhibitors and maintains a comparable accuracy of 98.1% for inhibitors, based-on a more rigorous test with more diverse range of compounds. Our study suggests that machine learning methods such as SVM are useful for facilitating the prediction of FXa inhibitors.

Current trends in quantitative structure activity relationships on FXa inhibitors: Evaluation and comparative analysis

This article evaluates the quantitative structure activity relationships of FXa inhibitors, using the C-QSAR program of Biobyte. Diaryloxypyridines, aminophenols, biaryl isoxazoline derivatives, 1,2-dibenzamidobenzenes, 3-amidinophenylalanine derivatives, benzoxazinones, naphthoanilides, tetrazoles, glucolic and mandelic acid derivatives were included in this survey. Clog P plays a significant role in the QSAR, especially as hydrophilicity. In the most of the cases, CMR/MR molar refractivity as well as sterimol parameters (B5 and L) are important. Electronic effects with the exception of the Hammett's constant sigmam, are not found to govern the biological activity. Es was found to be important indicator variables were used after the best model was found to account for the usual structural features.

Design, synthesis, and activity of a novel series of factor Xa inhibitors: optimization of arylamidine groups

A novel series of diaryloxypyridines have been designed as selective nanomolar factor Xa (fXa) inhibitors for use as anticoagulants. In this paper, we describe our efforts to identify an additional interaction and a replacement for the distal amidine group that binds in the S3/S4 pocket of fXa. Introduction of a hydroxyl group para to the proximal amidine group increases the potency vs fXa by 1-2 orders of magnitude, which is the result of a hydrogen bond to Ser195 of the catalytic triad. A methyl imidazoline and a dimethylamide are good alternatives for the second amidine. These substitutions have increased the selectivity vs the related serine proteases trypsin and thrombin. The synthesis, in vitro activity, and hypothetical modes of binding to fXa based on trypsin crystallographic data are outlined.