ArticleThe journal of physical chemistry. A2024
Predicting Solvatochromism of Chromophores in Proteins through QM/MM and Machine Learning.
Article in The journal of physical chemistry. A, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules.Journal of chemical theory and computation · 2026Article
- Perspective on Theoretical Modeling of Soft Molecular Machines and Devices: A Fusion of Data-Driven Approaches with Traditional Computational Chemistry Algorithms.ChemistryOpen · 2025Article
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
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5 authors.
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Abstract
Solvatochromism occurs in both homogeneous solvents and more complex biological environments, such as proteins. While in both cases the solvatochromic effects report on the surroundings of the chromophore, their interpretation in proteins becomes more complicated not only because of structural effects induced by the protein pocket but also because the protein environment is highly anisotropic. This is particularly evident for highly conjugated and flexible molecules such as carotenoids, whose excitation energy is strongly dependent on both the geometry and the electrostatics of the environment. Here, we introduce a machine learning (ML) strategy trained on quantum mechanics/molecular mechanics calculations of geometrical and electrochromic contributions to carotenoids' excitation energies. We employ this strategy to compare solvatochromism in protein and solvent environments. Despite the important specifities of the protein, ML models trained on solvents can faithfully predict excitation energies in the protein environment, demonstrating the robustness of the chosen descriptors.
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