Data-driven discovery of molecular photoswitches with multioutput Gaussian processes

Accessing a Diverse Set of Functional Red-Light Photoswitches by Selective Copper-Catalyzed Indigo N-Arylation

The ability to correlate the structure of a molecule with its properties is the key to the rational and accelerated design of new functional compounds and materials. Taking photoswitches as an example, the thermal stability of the metastable state is a crucial property that dictates their application in molecular systems. Indigos have recently emerged as an attractive motif for designing photoswitchable molecules due to their red-light addressability, which can be advantageous in biomedical and material applications. The lack of synthetic techniques to derivatize the abundant parent dye and a thorough understanding of the impact of structural factors on the photochemical and thermal properties hinder broad applications of this emerging photoswitch class. Herein, we report an efficient copper-catalyzed indigo N-arylation that enables the installation of a wide variety of aryl moieties carrying useful functional groups. The exclusive selectivity for monoarylation likely originates from a bimetallic cooperative mechanism through a binuclear copper-indigo intermediate. Functional N-aryl-N'-alkylindigos were prepared and shown to photoisomerize efficiently under red light. Moreover, this design allows for the modulation of thermal half-lives through N-aryl substituents, while the N'-alkyl groups enable the independent attachment of functional moieties without affecting the photochromic properties. A strong correlation between the structure of the N-aryl moiety and the thermal stability of the photogenerated Z-isomers was achieved by multivariate linear regression models obtained through a data-science workflow. This work thus builds an avenue leading to versatile red-light photoswitches and a general method for structure-property correlation that is expected to be broadly applicable to the design of photoresponsive molecules.

Visible-Light-Activated Heteroaryl Azoswitches: Toward a More Colorful Future

Azobenzenes (Ph-N═N-Ph) are known as the most widely studied molecular photoswitches, and the recent rise of azoheteroarenes (Het-N═N-Ph or Het-N═N-Het) offers great opportunities to advance this already mature field. A common limitation is that azo-switches generally require harmful UV light for activation, which hinders their application across various fields. Despite great efforts in developing visible-light azobenzenes over the past few decades, the potential of visible-light heteroaryl azoswitches remains largely unexplored. This Perspective summarizes the state-of-the-art advancements in visible-light heteroaryl azoswitches, covering molecular design strategies, the structure-property relationship, and potential applications. We highlight the distinctive advantages of azoheteroarenes over azobenzenes in the research and development of visible-light switches. Furthermore, we discuss the opportunities and challenges in this emerging field and propose potential solutions to address crucial issues such as spectral red-shift and thermal half-life. Through this Perspective paper, we aim to provide inspiration for further exploration in this field, in anticipation of the growing prosperity and bright future of visible-light azoheteroarene photoswitches.

From Organic Fragments to Photoswitchable Catalysts: The OFF-ON Structural Repository for Transferable Kernel-Based Potentials

Structurally and conformationally diverse databases are needed to train accurate neural networks or kernel-based potentials capable of exploring the complex free energy landscape of flexible functional organic molecules. Curating such databases for species beyond "simple" drug-like compounds or molecules composed of well-defined building blocks (e.g., peptides) is challenging as it requires thorough chemical space mapping and evaluation of both chemical and conformational diversities. Here, we introduce the OFF-ON (organic fragments from organocatalysts that are non-modular) database, a repository of 7869 equilibrium and 67,457 nonequilibrium geometries of organic compounds and dimers aimed at describing conformationally flexible functional organic molecules, with an emphasis on photoswitchable organocatalysts. The relevance of this database is then demonstrated by training a local kernel regression model on a low-cost semiempirical baseline and comparing it with a PBE0-D3 reference for several known catalysts, notably the free energy surfaces of exemplary photoswitchable organocatalysts. Our results demonstrate that the OFF-ON data set offers reliable predictions for simulating the conformational behavior of virtually any (photoswitchable) organocatalyst or organic compound composed of H, C, N, O, F, and S atoms, thereby opening a computationally feasible route to explore complex free energy surfaces in order to rationalize and predict catalytic behavior.

Searching the Chemical Space of Bicyclic Dienes for Molecular Solar Thermal Energy Storage Candidates

Photoswitches are molecular systems that are chemically transformed subsequent to interaction with light and they find potential application in many new technologies. The design and discovery of photoswitch candidates require intricate molecular engineering of a range of properties to optimize a candidate to a specific applications, a task which can be tackled efficiently using quantum chemical screening procedures. In this paper, we perform a large scale screening of approximately half a million bicyclic diene photoswitches in the context of molecular solar thermal energy storage using ab initio quantum chemical methods. We further device an efficient strategy for scoring the systems based on their predicted solar energy conversion efficiency and elucidate potential pitfalls of this approach. Our search through the chemical space of bicyclic dienes reveals systems with unprecedented solar energy conversion efficiencies and storage densities that show promising design guidelines for next generation molecular solar thermal energy storage systems.

Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling

Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window.

An Ecosystem for Digital Reticular Chemistry

The vastness of the materials design space makes it impractical to explore using traditional brute-force methods, particularly in reticular chemistry. However, machine learning has shown promise in expediting and guiding materials design. Despite numerous successful applications of machine learning to reticular materials, progress in the field has stagnated, possibly because digital chemistry is more an art than a science and its limited accessibility to inexperienced researchers. To address this issue, we present mofdscribe, a software ecosystem tailored to novice and seasoned digital chemists that streamlines the ideation, modeling, and publication process. Though optimized for reticular chemistry, our tools are versatile and can be used in nonreticular materials research. We believe that mofdscribe will enable a more reliable, efficient, and comparable field of digital chemistry.

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.