Engineering Frustrated Lewis Pair Active Sites in Porous Organic Scaffolds for Catalytic CO2 Hydrogenation.

Frustrated Lewis pairs (FLPs), featuring reactive combinations of Lewis acids and Lewis bases, have been utilized for myriad metal-free homogeneous catalytic processes. Immobilizing the active Lewis sites to a solid support, especially to porous scaffolds, has shown great potential to ameliorate FLP catalysis by circumventing some of its inherent drawbacks, such as poor product separation and catalyst recyclability. Nevertheless, designing immobilized Lewis pair active sites (LPASs) is challenging due to the requirement of placing the donor and acceptor centers in appropriate geometric arrangements while maintaining the necessary chemical environment to perform catalysis, and clear design rules have not yet been established. In this work, we formulate simple guidelines to build highly active LPASs for direct catalytic hydrogenation of CO2 through a large-scale screening of a diverse library of 25,000 immobilized FLPs. The library is built by introducing boron-containing acidic sites in the vicinity of the existing basic nitrogen sites of the organic linkers of metal-organic frameworks collected in a "top-down" fashion from the CoRE MOF 2019 database. The chemical and geometrical appropriateness of these LPASs for CO2 hydrogenation is determined by evaluating a series of simple descriptors representing the intrinsic strength (acidity and basicity) of the components and their spatial arrangement in the active sites. Analysis of the leading candidates enables the formulation of pragmatic and experimentally relevant design principles which constitute the starting point for further exploration of FLP-based catalysts for the reduction of CO2.

3DReact: Geometric Deep Learning for Chemical Reactions.

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

Inverse Design of Singlet Fission Materials with Uncertainty-Controlled Genetic Optimization.

Singlet fission has shown potential for boosting the power conversion efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and singlet exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository.  Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger conjugated donor-acceptor systems, these units exhibit strong localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells. As the proposed candidates are composed of fragments from synthesized molecules, they are likely synthetically accessible.

The NCIWEB Server: A Novel Implementation of the Noncovalent Interactions Index for Biomolecular Systems.

It is well-known that the activity and function of proteins is strictly correlated with their secondary, tertiary, and quaternary structures. Their biological role is regulated by their conformational flexibility and global fold, which, in turn, is largely governed by complex noncovalent interaction networks. Because of the large size of proteins, the analysis of their noncovalent interaction networks is challenging, but can provide insights into the energetics of conformational changes or protein-protein and protein-ligand interactions. The noncovalent interaction (NCI) index, based on the reduced density gradient, is a well-established tool for the detection of weak contacts in biological systems. In this work, we present a web-based application to expand the use of this index to proteins, which only requires a molecular structure as input and provides a mapping of the number, type, and strength of noncovalent interactions. Structure preparation is automated and allows direct importing from the PDB database, making this server (https://nciweb.dsi.upmc.fr) accessible to scientists with limited experience in bioinformatics. A quick overview of this tool and concise instructions are presented, together with an illustrative application.

Toward in silico Catalyst Optimization.

In this minireview, we overview a computational pipeline developed within the framework of NCCR Catalysis that can be used to successfully reproduce the enantiomeric ratios of homogeneous catalytic reactions. At the core of this pipeline is the SCINE Molassembler module, a graph-based software that provides algorithms for molecular construction of all periodic table elements. With this pipeline, we are able to simultaneously functionalizenand generate ensembles of transition state conformers, which permits facile exploration of the influencenof various substituents on the overall enantiomeric ratio. This allows preconceived back-of-the-envelope designnmodels to be tested and subsequently refined by providing quick and reliable access to energetically low-lyingntransition states, which represents a key step in undertaking in silico catalyst optimization.

Genetic Algorithms for the Discovery of Homogeneous Catalysts.

In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts.

Helical versus linear Jahn-Teller distortions in allene and spiropentadiene radical cations.

The allene radical cation can be stabilized both by Jahn-Teller distortion of the bond lengths and by torsion of the end-groups. However, only the latter happens and the allene radical cation relaxes into a twisted D2 symmetry structure with equal double-bond lengths. Here we revisit the Jahn-Teller distortion of allene and spiropentadiene by assessing the possible implications of their helical π-systems in the radical cations. We describe a general relation between the structure and the number of π-electrons in spiroconjugated and linearly conjugated systems. Through constrained optimizations we compare the stabilization achieved by bond-length alternation and axial torsion in the radical cations, which we explain with a simple frontier molecular orbital (MO) picture. While structurally different, allene and spiropentadiene have similar helical frontier MOs. Both cations relax through torsion because the stabilization of their helical frontier MOs is bigger than that which can be achieved by linear π-conjugation. Electrohelicity thus manifests in molecular systems with partial occupation as a helical π-conjugation effect, which evidently provides more stabilization than its linear counterpart in terms of the Jahn-Teller distortion. This mechanism may be a driving factor for the relaxation in a range of spiroconjugated and linearly conjugated cationic systems.

New venues in electron density analysis.

We provide a comprehensive overview of the chemical information from electron density: not only how to extract information, but also how to obtain and how to assess the quality of the electron density itself. After introducing several indexes derived from electron density, which allow bonding to be revealed, we focus on the various potential sources of electron density, and also explain the error trends they show so that a judicious choice of methods and limitations are clearly laid on the table. Computational, experimental-computational combinations, and machine learning efforts are covered in this work.

Assessing the persistence of chalcogen bonds in solution with neural network potentials.

Non-covalent bonding patterns are commonly harvested as a design principle in the field of catalysis, supramolecular chemistry, and functional materials to name a few. Yet, their computational description generally neglects finite temperature and environment effects, which promote competing interactions and alter their static gas-phase properties. Recently, neural network potentials (NNPs) trained on density functional theory (DFT) data have become increasingly popular to simulate molecular phenomena in condensed phase with an accuracy comparable to ab initio methods. To date, most applications have centered on solid-state materials or fairly simple molecules made of a limited number of elements. Herein, we focus on the persistence and strength of chalcogen bonds involving a benzotelluradiazole in condensed phase. While the tellurium-containing heteroaromatic molecules are known to exhibit pronounced interactions with anions and lone pairs of different atoms, the relevance of competing intermolecular interactions, notably with the solvent, is complicated to monitor experimentally but also challenging to model at an accurate electronic structure level. Here, we train direct and baselined NNPs to reproduce hybrid DFT energies and forces in order to identify what the most prevalent non-covalent interactions occurring in a solute-Cl--THF mixture are. The simulations in explicit solvent highlight the clear competition with chalcogen bonds formed with the solvent and the short-range directionality of the interaction with direct consequences for the molecular properties in the solution. The comparison with other potentials (e.g., AMOEBA, direct NNP, and continuum solvent model) also demonstrates that baselined NNPs offer a reliable picture of the non-covalent interaction interplay occurring in solution.

Mapping Active Site Geometry to Activity in Immobilized Frustrated Lewis Pair Catalysts.

The immobilization of molecular catalysts imposes spatial constraints on their active site. We reveal that in bifunctional catalysis such constraints can also be utilized as an appealing handle to boost intrinsic activity through judicious control of the active site geometry. To demonstrate this, we develop a pragmatic approach, based on nonlinear scaling relationships, to map the spatial arrangements of the acid-base components of frustrated Lewis pairs (FLPs) to their performance in the catalytic hydrogenation of CO2 . The resulting activity map shows that fixing the donor-acceptor centers at specific distances and locking them into appropriate orientations leads to an unforeseen many-fold increase in the catalytic activity of FLPs compared to their unconstrained counterparts.

Orbital energies and nuclear forces in DFT: Interpretation and validation.

The bonding and antibonding character of individual molecular orbitals has been previously shown to be related to their orbital energy derivatives with respect to nuclear coordinates, known as dynamical orbital forces. Albeit usually derived from Koopmans' theorem, in this work we show a more general derivation from conceptual DFT, which justifies application in a broader context. The consistency of the approach is validated numerically for valence orbitals in Kohn-Sham DFT. Then, we illustrate its usefulness by showcasing applications in aromatic and antiaromatic systems and in excited state chemistry. Overall, dynamical orbital forces can be used to interpret the results of routine ab initio calculations, be it wavefunction or density based, in terms of forces and occupations.

The "Inverted Bonds" Revisited: Analysis of "In Silico" Models and of [1.1.1]Propellane by Using Orbital Forces.

This article dwells on the nature of "inverted bonds", which refer to the σ interaction between two sp hybrids by their smaller lobes, and their presence in [1.1.1]propellane. Firstly, we study H3 C-C models of C-C bonds with frozen H-C-C angles reproducing the constraints of various degrees of "inversion". Secondly, the molecular orbital (MO) properties of [1.1.1]propellane and [1.1.1]bicyclopentane are analyzed with the help of orbital forces as a criterion of bonding/antibonding character and as a basis to evaluate bond energies. Triplet and cationic states of [1.1.1]propellane species are also considered to confirm the bonding/antibonding character of MOs in the parent molecule. These approaches show an essentially non-bonding character of the σ central C-C interaction in propellane. Within the MO theory, this bonding is thus only due to π-type MOs (also called "banana" MOs or "bridge" MOs) and its total energy is evaluated to approximately 50 kcal mol-1 . In bicyclopentane, despite a strong σ-type repulsion, a weak bonding (15-20 kcal mol-1 ) exists between both central C-C bonds, also due to π-type interactions, though no bond is present in the Lewis structure. Overall, the so-called "inverted" bond, as resulting from a σ overlap of the two sp hybrids by their smaller lobes, appears highly questionable.

Steric clash in real space: biphenyl revisited.

A textbook case of twisted structure due to hydrogen-hydrogen steric clash, the biphenyl molecule, has been studied in real space from a new perspective. Long-term discrepancies regarding the origin of the steric repulsion are now reconciled under the NCI (Non Covalent Interaction) method, which reflects in 3D the balance between attractive and repulsive interactions taking place in the region between the phenyl rings. The NCI method confirms that the steric repulsion does not merely come from the H-H interaction itself, but from the many-atom interactions arising from the Cortho-H region, therefore providing rigorous physical grounds for the steric clash. This method allows a continuous scan of all the subtle changes on the electron density on going from the planar to the perpendicular biphenyl structure. The NCI results agree with other topological approaches (IQA, ELF) and are in line with previous findings in the literature regarding controversial H-H interactions in steric clash situations: H-H interactions are attractive, but repulsion appears between (Cortho-H)(Cortho-H), raising the intraatomic energy of the ortho H. ELF is also used to support these conclusions. Indeed, deformations are observed in compressed basins that allow to visualize the intraatomic effect of steric repulsion. These results can be easily extrapolated to systems with similar topological features in which steric clash is claimed to be the reason for instability.

A Bond Charge Model Ansatz for Intrinsic Bond Energies: Application to C-C Bonds.

A simple bond charge model is proposed to predict intrinsic bond energies. Model parameters can be derived from the topology of the electron localization function and equilibrium geometries through classic considerations. Results for carbon-carbon covalent bonds are shown to be very accurate in different chemical environments. Insight can be extracted from the application of the model due to its elementary construction and simple mathematical formulation. The remarkable robustness of the fitted model highlights how different density functional approximations relate geometries, densities, and energies.

Localizing electron density errors in density functional theory.

The accuracy of different density functional approximations is assessed through the use of quantum chemical topology on molecular electron densities. In particular, three simple yet ever-important systems are studied: N2, CO and ethane. Our results exemplify how real-space descriptors can help understand the sources of errors in density functional theory, avoiding unwanted error compensation present in simplified statistical metrics. Errors in "well-built" functionals are shown to be concentrated in chemically meaningful regions of space, and hence they are predictable. Conversely, strongly parametrized functionals show isotropic errors that cannot be traced back to chemically transferable units. Moreover, we will show that energetic corrections are mapped back into improvements in the density in chemically meaningful regions. These results point at the relevance of real-space perspectives when parametrizing or relating energy and density errors.

A first step towards quantum energy potentials of electron pairs.

A first step towards the construction of a quantum force field for electron pairs in direct space is taken. Making use of topological tools (Interacting Quantum Atoms and the Electron Localisation Function), we have analysed the dependency of electron pairs electrostatic, kinetic and exchange-correlation energies upon bond stretching. Simple correlations were found, and can be explained with elementary models such as the homogeneous electron gas. The resulting energy model is applicable to various bonding regimes: from homopolar to highly polarized and even to non-conventional bonds. Overall, this is a fresh approach for developing real space-based force fields including an exchange-correlation term. It provides the relative weight of each of the contributions, showing that, in common Lewis structures, the exchange correlation contribution between electron pairs is negligible. However, our results reveal that classical approximations progressively fail for delocalised electrons, including lone pairs. This theoretical framework justifies the success of the classic Bond Charge Model (BCM) approach in solid state systems and sets the basis of its limits. Finally, this approach opens the door towards the development of quantitative rigorous energy models based on the ELF topology.

Proteasome versus Thioredoxin Reductase Competition as Possible Biological Targets in Antitumor Mixed Thiolate-Dithiocarbamate Gold(III) Complexes.

New mixed gold(III) derivatives with dithiocarbamate and thiolate ligands have been synthesized and characterized. They display high anticancer activity against colon cancer cell lines without affecting to differentiated enterocytes, high stability in phosphate-buffered saline solution, and resistance to gold reduction in the presence of reducing agents in the majority of the derivatives. Some of them show interaction with thioredoxin reductase as derived from in vitro analysis and computational studies. However, a competition between this enzyme and proteasome is detected in cells, which is corroborated by the determination of proteasomal chymotrypsin-like activity inhibition. In addition, some of these dithiocarbamate gold(III) derivatives reduce cell viability and proliferation by intrinsic apoptotic pathway, with changes in mitochondrial membrane potential, cytochrome c release and caspase-3 activation. Consequently, our results show new complexes with proteasome as possible target in colorectal cancer.

Identification of Inhibitors Targeting Ferredoxin-NADP⁺ Reductase from the Xanthomonas citri subsp. citri Phytopathogenic Bacteria.

Ferredoxin-NADP(H) reductases (FNRs) deliver NADPH or low potential one-electron donors to redox-based metabolism in plastids and bacteria. Xanthomonas citri subsp. citri (Xcc) is a Gram-negative bacterium responsible for citrus canker disease that affects commercial citrus crops worldwide. The Xcc fpr gene encodes a bacterial type FNR (XccFPR) that contributes to the bacterial response to oxidative stress conditions, usually found during plant colonization. Therefore, XccFPR is relevant for the pathogen survival and its inhibition might represent a strategy to treat citrus canker. Because of mechanistic and structural differences from plastidic FNRs, XccFPR is also a potential antibacterial target. We have optimized an activity-based high-throughput screening (HTS) assay that identifies XccFPR inhibitors. We selected 43 hits from a chemical library and narrowed them down to the four most promising inhibitors. The antimicrobial effect of these compounds was evaluated on Xcc cultures, finding one with antimicrobial properties. Based on the functional groups of this compound and their geometric arrangement, we identified another three XccFPR inhibitors. Inhibition mechanisms and constants were determined for these four XccFPR inhibitors. Their specificity was also evaluated by studying their effect on the plastidic Anabaena PCC 7119 FNR, finding differences that can become interesting tools to discover Xcc antimicrobials.

Data-Driven Discovery of Organic Electronic Materials Enabled by Hybrid Top-Down/Bottom-Up Design.

The high-throughput exploration and screening of molecules for organic electronics involves either a 'top-down' curation and mining of existing repositories, or a 'bottom-up' assembly of user-defined fragments based on known synthetic templates. Both are time-consuming approaches requiring significant resources to compute electronic properties accurately. Here, 'top-down' is combined with 'bottom-up' through automatic assembly and statistical models, thus providing a platform for the fragment-based discovery of organic electronic materials. This study generates a top-down set of 117K synthesized molecules containing structures, electronic and topological properties and chemical composition, and uses them as building blocks for bottom-up design. A tool is developed to automate the coupling of these building blocks at their C(sp2/sp)-H bonds, providing a fundamental link between the two dataset construction philosophies. Statistical models are trained on this dataset and a subset of resulting top-down/bottom-up compounds, enabling on-the-fly prediction of ground and excited state properties with high accuracy across organic compound space. With access to ab initio-quality optical properties, this bottom-up pipeline may be applied to any materials design campaign using existing compounds as building blocks. To illustrate this, over a million molecules are screened for singlet fission. tThe leading candidates provide insight into the features promoting this multiexciton-generating process.

Automated prediction of ground state spin for transition metal complexes.

Exploiting crystallographic data repositories for large-scale quantum chemical computations requires the rapid and accurate extraction of the molecular structure, charge and spin from the crystallographic information file. Here, we develop a general approach to assign the ground state spin of transition metal complexes, in complement to our previous efforts on determining metal oxidation states and bond order within the cell2mol software. Starting from a database of 31k transition metal complexes extracted from the Cambridge Structural Database with cell2mol, we construct the TM-GSspin dataset, which contains 2063 mononuclear first row transition metal complexes and their computed ground state spins. TM-GSspin is highly diverse in terms of metals, metal oxidation states, coordination geometries, and coordination sphere compositions. Based on TM-GSspin, we identify correlations between structural and electronic features of the complexes and their ground state spins to develop a rule-based spin state assignment model. Leveraging this knowledge, we construct interpretable descriptors and build a statistical model achieving 98% cross-validated accuracy in predicting the ground state spin across the board. Our approach provides a practical way to determine the ground state spin of transition metal complexes directly from crystal structures without additional computations, thus enabling the automated use of crystallographic data for large-scale computations involving transition metal complexes.