Utilizing Artificial Intelligence to Reveal the Intricate Spatial Arrangement of Complex Chiral Molecules
Introduction:
The University of Amsterdam’s Van ‘t Hoff Institute for Molecular Sciences has developed a powerful machine learning approach to determine the absolute configuration and conformational landscape of complex chiral molecules. In a recent publication in Angewandte Chemie, researchers describe how they combine a genetic algorithm with a hierarchical clustering algorithm to predict and boost the performance of chiroptical spectroscopies. This approach was demonstrated in a study on natural product alkaloids isolated from marine sponges, which contain multiple chiral centers. The researchers’ artificial intelligence toolbox improves the analysis of spectra for complex systems and enables reliable assignments of absolute configurations. This advancement has significant implications for understanding the biosynthetic pathways of chiral compounds. To read the full study, click here.
Full Article: Utilizing Artificial Intelligence to Reveal the Intricate Spatial Arrangement of Complex Chiral Molecules
Researchers from the University of Amsterdam’s Van ’t Hoff Institute for Molecular Sciences have developed a powerful machine learning approach to elucidate the absolute configuration and conformational landscape of complex chiral molecules. In a recent paper in Angewandte Chemie, they describe how the combination of a genetic algorithm with a hierarchical clustering algorithm can predict and boost the performance of chiroptical spectroscopies. The power of this approach is demonstrated in a study together with researchers from the National Institutes of Health (USA) on natural product alkaloids isolated from marine sponges. Containing as many as eight chiral centres, an unambiguous determination of their stereochemistry is key for understanding their biosynthetic pathways.
The Challenges of Determining Chiral Molecules
Determining the precise spatial structure of a chiral molecule, the absolute configuration, is far from easy. The common approach is to use circularly polarized light that itself displays chirality. Techniques such as electronic circular dichroism (ECD), vibrational circular dichroism (VCD) and Raman optical activity (ROA) spectroscopy apply such light to yield a spectroscopic fingerprint. This is unique for each molecule and can even distinguish the mirror image of the same molecule.
However, to derive an absolute configuration from a recorded spectrum, a comparison with simulated spectra is needed. The latter can be generated, for example, using Density Functional Theory (DFT), but for complex molecules in particular this has serious limitations. Firstly, the simulation can become time-consuming and computationally expensive. Secondly, for such a comparison, an average needs to be taken over all possible conformations the molecule can adopt. And here comes the snag because this average critically depends on the energies of these conformers. As there is an intrinsic uncertainty in these energies, there can be a large variation in simulated spectra. The larger the number of chiral centres and the larger the conformational flexibility, the poorer the quality of the stereochemical determination.
An Artificial Intelligence Approach
In their Angewandte paper, the researchers describe how the strategic use of artificial intelligence helps to overcome this impasse. They present an elaborate protocol that combines two artificial intelligence (AI) algorithms. The first is a genetic algorithm that explicitly takes the uncertainties in the computed energies into account in the comparison between experimental and predicted spectra to help identify the most relevant conformations out of all possible conformations. The second is a hierarchical clustering algorithm that analyses the trends in the spectra of the considered conformations. It also identifies on-the-fly when a given chiroptical technique is not able to make any reliable conformational predictions.
The paper shows how the novel “AI toolbox” leads to reliable assignments of absolute configuration and provides insight into conformational heterogeneities – even for difficult molecules that are flexible and feature a large number of chiral centres. According to the researchers, their approach is quite general and applicable to the analysis of any type of molecular spectra. They expect their approach to improve the analysis of spectra of complex systems in general.
A Case Study
As a case study, the researchers applied their protocol to two natural products isolated from the marine sponge Haliclona. The compounds papuamine and haliclonadiamine always occur together in the sponges, and determination of their absolute configuration is key for establishing their exact biosynthetic pathway. Both molecules are very flexible, have very similar structures, and share eight chiral centres. Earlier research by the Laboratory of Bioorganic Chemistry at the National Institutes of Health (Bethesda, US) has shown that it is very difficult to unambiguously determine their configuration with current approaches. Papuamine and haliclonadiamine therefore were the ideal examples for illustrating the weakness of the standard protocol and the effectiveness of the newly proposed one, enabling highly reliable assignments of their absolute configurations.
For more information, you can read the full paper: An Artificial Intelligence Approach for Tackling Conformational Energy Uncertainties in Chiroptical Spectroscopies.
Summary: Utilizing Artificial Intelligence to Reveal the Intricate Spatial Arrangement of Complex Chiral Molecules
Researchers from the University of Amsterdam have developed a machine learning approach to determine the absolute configuration and conformational landscape of complex chiral molecules. By combining a genetic algorithm and a hierarchical clustering analysis, they were able to predict and enhance the performance of chiroptical spectroscopies. This approach was demonstrated in a study on natural product alkaloids isolated from marine sponges. The researchers believe that their AI toolbox can be applied to the analysis of any type of molecular spectra and can improve the analysis of complex systems. The full paper can be found in Angewandte Chemie.
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