Exploring enantioselective molecular recognition mechanisms with chemoinformatic techniques

Abstract

A comprehensive review of chemoinformatic techniques and studies applied to the field of enantioselective molecular recognition is presented. Several approaches such as enantiophores/pharmacophore modelling, QSPRs, CoMFA and other insightful data mining procedures are discussed. The review focuses on the central role of chemoinformatic approaches on the establishment of connections between available experimental data, mainly HPLC separation data, and these algorithms that describe properties of chiral molecules. The general overview of the aforementioned calculations account for a use of these techniques as a valuable strategy to achieve reliable prediction systems, infer the mechanisms of chiral recognition, generate insight for the conception of new chiral receptors and corroborate and assist experimental techniques such as chiral LC. Moreover, it is pointed out that computer methods in this field promise a wide range of applications for both academia and industry, ranging from enantioselective reactions, drug discovery and analysis of high-throughput screenings, to analytical and semi-preparative separations or large-scale production of enantiopure compounds.

Predicting the physicochemical profile of diastereoisomeric histidine‐containing dipeptides by property space analysis

“…As stated by Thompson and coworkers, such problems would be resolved by producing a proper dataset composed by homogenous peptides that exhaustively cover the available chemical space. Moreover, although several computational approaches and many chirality descriptors have been proposed in the last years to predict enantioselective binding, asymmetric reactivity, or chiral separations, the physicochemical profiling of diastereoisomers has been scantly investigated and very few studies have tried to predict their differences.…”

Comparison of HPLC enantioseparation of substituted binaphthyls on CD‐, polysaccharide‐ and synthetic polymer‐based chiral stationary phases

“…Neither results obtained with some more sophisticated approaches, e.g. chemoinformatic methods, can give reliable information of general validity [17,[40][41][42][43]. Therefore, any new study focused on enantioseparation of a set of analytes on various CSP types (with structurally diverse chiral selectors bonded to silica support) can contribute to better understanding of interaction mechanism and thus can make optimization of the separation method easier.…”

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy

“…Chemoinformatic approaches have emerged as one of the most powerful tools for predicting enantioselectivity of chiral HPLC separations . Among them, machine learning approaches represent the latest evolution for computational chemistry .…”