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Stereoisomerism

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Analytical Quality by Design for Chiral Pharmaceuticals: A Robust HPLC Method for Upadacitinib Enantiomeric Quantification.

Chirality
Ensuring the enantiomeric purity of chiral pharmaceuticals is paramount for patient safety and therapeutic efficacy. Upadacitinib (UPA), a vital Janus kinase 1 (JAK-1) inhibitor for rheumatoid arthritis treatment, exemplifies this need. This study re...

Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation.

Journal of chemical information and modeling
Absolute stereochemical configurations and optical rotations were computed for 121,416 molecular structures from the QM9 quantum chemistry data set using density functional theory. A representation for the molecules was developed using Cartesian coor...

Merging enzymatic and synthetic chemistry with computational synthesis planning.

Nature communications
Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability to leverage rare chemical transformations. This challenge is acute for enzymatic reactions, which...

Fusing 2D and 3D molecular graphs as unambiguous molecular descriptors for conformational and chiral stereoisomers.

Briefings in bioinformatics
The rapid progress of machine learning (ML) in predicting molecular properties enables high-precision predictions being routinely achieved. However, many ML models, such as conventional molecular graph, cannot differentiate stereoisomers of certain t...

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers.

Journal of chemical information and modeling
Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being ...

Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.

Journal of chemical information and modeling
Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A no...

Prediction of Human Liver Microsome Clearance with Chirality-Focused Graph Neural Networks.

Journal of chemical information and modeling
In drug candidate design, clearance is one of the most crucial pharmacokinetic parameters to consider. Recent advancements in machine learning techniques coupled with the growing accumulation of drug data have paved the way for the construction of co...

Stereoisomers Are Not Machine Learning's Best Friends.

Journal of chemical information and modeling
This study addresses the challenge of accurately identifying stereoisomers in cheminformatics, which originates from our objective to apply machine learning to predict the association constant between cyclodextrin and a guest. Identifying stereoisome...

Alkenyl pheromones: Raman spectroscopic analysis, DFT modeling, and machine learning for stereoisomerism evaluation.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Alkenyl pheromones are a class of insect sex pheromones that are characterized by the presence of one or more double bonds, which can be either in the E(trans) or Z(cis) configuration. This structural variation is essential in mating, as it influence...

AI-Augmented R-Group Exploration in Medicinal Chemistry.

Journal of chemical information and modeling
Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmaco...