Novel Approach for the Numerical Characterization of Molecular Chirality

Machine Learning Classification of One-Chiral-Center Organic Molecules According to Optical Rotation

In this study, machine learning algorithms were investigated for the classification of organic molecules with one carbon chiral center according to the sign of optical rotation. Diverse heterogeneous data sets comprising up to 13,080 compounds and their corresponding optical rotation were retrieved from Reaxys and processed independently for three solvents: dichloromethane, chloroform, and methanol. The molecular structures were represented by chiral descriptors based on the physicochemical and topological properties of ligands attached to the chiral center. The sign of optical rotation was predicted by random forests (RF) and artificial neural networks for independent test sets with an accuracy of up to 75% for dichloromethane, 82% for chloroform, and 82% for methanol. RF probabilities and the availability of structures in the training set with the same spheres of atom types around the chiral center defined applicability domains in which the accuracy is higher.

Machine learning to predict the specific optical rotations of chiral fluorinated molecules

A chemoinformatics method was applied to the assignment of absolute configurations and to the quantitative prediction of specific optical rotations using a data set of 88 chiral fluorinated molecules (44 pairs of enantiomers). Counterpropagation neural networks were explored for the classification of enantiomers as dextrorotatory or levorotatory. Regression models were trained using multilayer perceptrons (MLP), random forests (RF) or multilinear regressions (MLR), on the basis of physicochemical atomic stereo (PAS) descriptors. New descriptors were also derived considering the common structural features of the data set (cPAS descriptors), which enabled RF models to predict the whole data set with R = 0.964, mean absolute error (MAE) of 9.8° and root mean square error (RMSE) of 12.5° in leave-one-pair-out cross-validation experiments. The predictions for the 30 compounds measured in chloroform were obtained with R = 0.971, MAE = 9.1° and RMSE = 12.5°, which compares favorably with quantum chemistry calculations reported in the literature.

2D and 3D structure-activity modelling of mosquito repellents: a review $

Repellents disrupt the behaviour of blood-seeking mosquitoes protecting humans against their bites which can transmit serious diseases. Since the mid-1950s, N,N-diethyl-m-toluamide (DEET) is considered as the standard mosquito repellent worldwide. However, DEET presents numerous shortcomings. Faced with the heightening risk of mosquito expansion caused by global climate changes and increasing international exchanges, there is an urgent need for a better repellent than DEET and the very few other commercialised repelling molecules such as picaridin and IR3535. In silico approaches have been used to find new repellents and to provide better insights into their mechanism of action. In this context, the goal of our study was to retrieve from the literature all the papers dealing with qualitative and quantitative structure-activity relationships on mosquito repellents. A critical analysis of the SAR and QSAR models was made focusing on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed. The hypotheses to compute homology and pharmacophore models, their interest to find new repellents and to provide insights into the mechanisms of repellency in mosquitoes were analysed. The interest to consider the mosquito olfactory system as the target to compute new repellents was discussed. The potential environmental impact of these chemicals as well as new ways of research were addressed.

Chiral Cliffs: Investigating the Influence of Chirality on Binding Affinity

Chirality is understood by many as a binary concept: a molecule is either chiral or it is not. In terms of the action of a structure on polarized light, this is indeed true. When examined through the prism of molecular recognition, the answer becomes more nuanced. In this work, we investigated chiral behavior on protein-ligand binding: when does chirality make a difference in binding activity? Chirality is a property of the 3D structure, so recognition also requires an appreciation of the conformation. In many situations, the bioactive conformation is undefined. We set out to address this by defining and using several novel 2D descriptors to capture general characteristic features of the chiral center. Using machine-learning methods, we built different predictive models to estimate if a chiral pair (a set of two enantiomers) might exhibit a chiral cliff in a binding assay. A set of about 3800 chiral pairs extracted from the ChEMBL23 database was used to train and test our models. By achieving an accuracy of up to 75 %, our models provide good performance in discriminating chiral cliffs from non-cliffs. More importantly, we were able to derive some simple guidelines for when one can reasonably use a racemate and when an enantiopure compound is needed in an assay. We critically discuss our results and show detailed examples of using our guidelines. Along with this publication we provide our dataset, our novel descriptors, and the Python code to rebuild the predictive models.

Chiral recognition of tryptophan enantiomers using chitosan-capped silver nanoparticles: Scanometry and spectrophotometry approaches

A new, fast and inexpensive colorimetric sensor was developed for chiral recognition of tryptophan enantiomers using chitosan-capped silver nanoparticles. The function of the sensor was based on scanometry and spectrophotometry of the colored product of a reaction solution containing a mixture of chitosan-capped silver nanoparticles, phosphate buffer and tryptophan enantiomers. The image of the colored solution was taken using the scanometer and the corresponding color values were obtained using Photoshop software which subsequently were used for optimization of the experimental parameters as the analytical signal. Two types of color values system were investigated: RGB (red, green and blue values) and CMYK (cyan, magenta, yellow and black values). The color values indicated that L-tryptophan had better interaction than D-tryptophan with chitosan-capped silver nanoparticles. A linear relationship between the analytical signal and the concentration of L-tryptophan was obtained in the concentration range of 1.3 × 10^-5-4.6 × 10^-4molL^-1. Detection limits, were obtained to be 2.1 × 10^-6, 2.4 × 10^-6 and 3.8 × 10^-6molL^-1 for L-tryptophan based on R (red), G (green) and B (blue) values, respectively.

QSAR models for the human H(+)/peptide symporter, hPEPT1: affinity prediction using alignment-independent descriptors

A data set comprising the major known chemical classes of hPEPT1 ligands was compiled from the literature. For these compounds, alignment-independent descriptors (VolSurf, GRIND/Almond, and MOE) were computed. Using hierarchical partial least-squares projection to latent structures (H-PLS), a one-component model with r2 = 0.77 and q2 = 0.75 was obtained. The model satisfied a set of rigorous validation criteria and performed well in the prediction of an external test set. Mechanistic interpretation of the model reveals polarity properties to be the dominant factors in determining hPEPT1 affinity, with hydrophobic interactions contributing to a lesser extent. The model is superior to previously reported models due to its combination of quality and speed. Accordingly, it is suitable for ligand-based virtual screening, such as QSAR-based database mining.