New challenges for photopharmacology and computational modeling.

Mous et al. recently reported the molecular mechanism of chloride transport through a light-activated pumping rhodopsin, a key process involved in a range of cellular functions. Their results open exciting new challenges for photopharmacology and computational modeling that should be addressed in the coming years.

Sterically driven current reversal in a molecular motor model.

Simulations can help unravel the complicated ways in which molecular structure determines function. Here, we use molecular simulations to show how slight alterations of a molecular motor's structure can cause the motor's typical dynamical behavior to reverse directions. Inspired by autonomous synthetic catenane motors, we study the molecular dynamics of a minimal motor model, consisting of a shuttling ring that moves along a track containing interspersed binding sites and catalytic sites. The binding sites attract the shuttling ring while the catalytic sites speed up a reaction between molecular species, which can be thought of as fuel and waste. When that fuel and waste are held in nonequilibrium steady-state concentrations, the free energy from the reaction drives directed motion of the shuttling ring along the track. Using this model and nonequilibrium molecular dynamics, we show that the shuttling ring's direction can be reversed by simply adjusting the spacing between binding and catalytic sites on the track. We present a steric mechanism behind the current reversal, supported by kinetic measurements from the simulations. These results demonstrate how molecular simulation can guide future development of artificial molecular motors.

The pseudotorsional space of RNA.

The characterization of the conformational landscape of the RNA backbone is rather complex due to the ability of RNA to assume a large variety of conformations. These backbone conformations can be depicted by pseudotorsional angles linking RNA backbone atoms, from which Ramachandran-like plots can be built. We explore here different definitions of these pseudotorsional angles, finding that the most accurate ones are the traditional η (eta) and θ (theta) angles, which represent the relative position of RNA backbone atoms P and C4'. We explore the distribution of η - θ in known experimental structures, comparing the pseudotorsional space generated with structures determined exclusively by one experimental technique. We found that the complete picture only appears when combining data from different sources. The maps provide a quite comprehensive representation of the RNA accessible space, which can be used in RNA-structural predictions. Finally, our results highlight that protein interactions lead to significant changes in the population of the η - θ space, pointing toward the role of induced-fit mechanisms in protein-RNA recognition.

Machine Learning of Reactive Potentials.

In the past two decades, machine learning potentials (MLPs) have driven significant developments in chemical, biological, and material sciences. The construction and training of MLPs enable fast and accurate simulations and analysis of thermodynamic and kinetic properties. This review focuses on the application of MLPs to reaction systems with consideration of bond breaking and formation. We review the development of MLP models, primarily with neural network and kernel-based algorithms, and recent applications of reactive MLPs (RMLPs) to systems at different scales. We show how RMLPs are constructed, how they speed up the calculation of reactive dynamics, and how they facilitate the study of reaction trajectories, reaction rates, free energy calculations, and many other calculations. Different data sampling strategies applied in building RMLPs are also discussed with a focus on how to collect structures for rare events and how to further improve their performance with active learning.

The emergence of machine learning force fields in drug design.

In the field of molecular simulation for drug design, traditional molecular mechanic force fields and quantum chemical theories have been instrumental but limited in terms of scalability and computational efficiency. To overcome these limitations, machine learning force fields (MLFFs) have emerged as a powerful tool capable of balancing accuracy with efficiency. MLFFs rely on the relationship between molecular structures and potential energy, bypassing the need for a preconceived notion of interaction representations. Their accuracy depends on the machine learning models used, and the quality and volume of training data sets. With recent advances in equivariant neural networks and high-quality datasets, MLFFs have significantly improved their performance. This review explores MLFFs, emphasizing their potential in drug design. It elucidates MLFF principles, provides development and validation guidelines, and highlights successful MLFF implementations. It also addresses potential challenges in developing and applying MLFFs. The review concludes by illuminating the path ahead for MLFFs, outlining the challenges to be overcome and the opportunities to be harnessed. This inspires researchers to embrace MLFFs in their investigations as a new tool to perform molecular simulations in drug design.

Engineering of substrate specificity in a plant cell-wall modifying enzyme through alterations of carboxyl-terminal amino acid residues.

Structural determinants of substrate recognition remain inadequately defined in broad specific cell-wall modifying enzymes, termed xyloglucan xyloglucosyl transferases (XETs). Here, we investigate the Tropaeolum majus seed TmXET6.3 isoform, a member of the GH16_20 subfamily of the GH16 network. This enzyme recognises xyloglucan (XG)-derived donors and acceptors, and a wide spectrum of other chiefly saccharide substrates, although it lacks the activity with homogalacturonan (pectin) fragments. We focus on defining the functionality of carboxyl-terminal residues in TmXET6.3, which extend acceptor binding regions in the GH16_20 subfamily but are absent in the related GH16_21 subfamily. Site-directed mutagenesis using double to quintuple mutants in the carboxyl-terminal region - substitutions emulated on barley XETs recognising the XG/penta-galacturonide acceptor substrate pair - demonstrated that this activity could be gained in TmXET6.3. We demonstrate the roles of semi-conserved Arg238 and Lys237 residues, introducing a net positive charge in the carboxyl-terminal region (which complements a negative charge of the acidic penta-galacturonide) for the transfer of xyloglucan fragments. Experimental data, supported by molecular modelling of TmXET6.3 with the XG oligosaccharide donor and penta-galacturonide acceptor substrates, indicated that they could be accommodated in the active site. Our findings support the conclusion on the significance of positively charged residues at the carboxyl terminus of TmXET6.3 and suggest that a broad specificity could be engineered via modifications of an acceptor binding site. The definition of substrate specificity in XETs should prove invaluable for defining the structure, dynamics, and function of plant cell walls, and their metabolism; these data could be applicable in various biotechnologies.

Implementation of machine learning protocols to predict the hydrolysis reaction properties of organophosphorus substrates using descriptors of electron density topology.

Prediction of catalytic reaction efficiency is one of the most intriguing and challenging applications of machine learning (ML) algorithms in chemistry. In this study, we demonstrated a strategy for utilizing ML protocols applied to Quantum Theory of Atoms In Molecules (QTAIM) parameters to predict the ability of the A17 L47K catalytic antibody to covalently capture organophosphate pesticides. We found that the novel "composite" DFT functional B97-3c could be effectively employed for fast and accurate initial geometry optimization, aligning well with the input dataset creation. QTAIM descriptors proved to be well-established in describing the examined dataset using density-based and hierarchical clustering algorithms. The obtained clusters exhibited correlations with the chemical classes of the input compounds. The precise physical interpretation of the QTAIM properties simplifies the explanation of feature impact for both supervised and unsupervised ML protocols. It also enables acceleration in the search for entries with desired properties within large databases. Furthermore, our findings indicated that Ridge Regression with Laplacian kernel and CatBoost Regressor algorithms demonstrated suitable performance in handling small datasets with non-trivial dependencies. They were able to predict the actual reaction barrier values with a high level of accuracy. Additionally, the CatBoost Classifier proved reliable in discriminating between "active" and "inactive" compounds.

Task-Similarity is a Crucial Factor for Few-Shot Meta-Learning of Structure-Activity Relationships.

Machine learning models support computer-aided molecular design and compound optimization. However, the initial phases of drug discovery often face a scarcity of training data for these models. Meta-learning has emerged as a potentially promising strategy, harnessing the wealth of structure-activity data available for known targets to facilitate efficient few-shot model training for the specific target of interest. In this study, we assessed the effectiveness of two different meta-learning methods, namely model-agnostic meta-learning (MAML) and adaptive deep kernel fitting (ADKF), specifically in the regression setting. We investigated how factors such as dataset size and the similarity of training tasks impact predictability. The results indicate that ADKF significantly outperformed both MAML and a single-task baseline model on the inhibition data. However, the performance of ADKF varied across different test tasks. Our findings suggest that considerable enhancements in performance can be anticipated primarily when the task of interest is similar to the tasks incorporated in the meta-learning process.

Gas-phase structure of polymer ions: Tying together theoretical approaches and ion mobility spectrometry.

An increasing number of studies take advantage of ion mobility spectrometry (IMS) coupled to mass spectrometry (IMS-MS) to investigate the spatial structure of gaseous ions. Synthetic polymers occupy a unique place in the field of IMS-MS. Indeed, due to their intrinsic dispersity, they offer a broad range of homologous ions with different lengths. To help rationalize experimental data, various theoretical approaches have been described. First, the study of trend lines is proposed to derive physicochemical and structural parameters. However, the evaluation of data fitting reflects the overall behavior of the ions without reflecting specific information on their conformation. Atomistic simulations constitute another approach that provide accurate information about the ion shape. The overall scope of this review is dedicated to the synergy between IMS-MS and theoretical approaches, including computational chemistry, demonstrating the essential role they play to fully understand/interpret IMS-MS data.

Scaffold-Oriented Asymmetric Catalysis: Conformational Modulation of Transition State Multivalency during a Catalyst-Controlled Assembly of a Pharmaceutically Relevant Atropisomer.

A new class of superbasic, bifunctional peptidyl guanidine catalysts is presented, which enables the organocatalytic, atroposelective synthesis of axially chiral quinazolinediones. Computational modeling unveiled the conformational modulation of the catalyst by a novel phenyl urea N-cap, that preorganizes the structure into the active, folded state. A previously unanticipated noncovalent interaction involving a difluoroacetamide acting as a hybrid mono- or bidentate hydrogen bond donor emerged as a decisive control element inducing atroposelectivity. These discoveries spurred from a scaffold-oriented project inspired from a fascinating investigational BTK inhibitor featuring two stable chiral axes and relies on a mechanistic framework that was foreign to the extant lexicon of asymmetric catalysis.

Machine Learning Interatomic Potentials for Catalysis.

Atomistic modeling can provide insights into the design of novel catalysts in modern industries of chemistry, materials science, and biology. Classical force fields and ab initio calculations have been widely adopted in molecular simulations. How- ever, these methods suffer from the drawbacks of either low accuracy or high cost. Recently, the development of machine learning interatomic potentials (MLIPs) has become more and more popular as they can tackle the problems in question and can deliver rather accurate results at significantly lower computational cost. In this review, the atomistic modeling of catalytic systems with the aid of MLIPs is discussed, showcasing recently developed MLIP models and selected applications for the modeling of catalytic systems. We also highlight the best practices, and challenges for MLIPs and give an outlook for future works on MLIPs in the field of catalysis.

Antiaromaticity of Cycloheptatrienyl Anions: Structure, Acidity, and Magnetic Properties.

Investigations of the nature and degree of antiaromaticity of cycloheptatrienyl anion derivatives using both experimental and computational tools are presented. The ground state of cycloheptatrienyl anion in the gas phase is triplet, planar and Baird-aromatic. In DMSO, it assumes a singlet distorted allylic form with a paratropic ring current. The other derivatives in both phases assume either allylic or diallylic conformations depending on the substituent pattern. A combination of experimental and computational methods was used to determine the pKa values of 16 derivatives in DMSO, which ranged from 36 to -10.7. We revealed that the stronger stabilization of the anionic system, which correlates with acidity, does not necessarily imply a lower degree of antiaromaticity in terms of magnetic properties. Conversely, the substitution pattern first affects the geometry of the ring through the bulkiness of the substituents and their better conjugation with a more distorted system. Consequently, the distortion reduces the cyclic conjugation in the π-system and thereby decreases the paratropic current in a magnetic field, which manifests itself as a decrease in the NICS. The triplet-state geometries and magnetic properties are nearly independent on the substitution pattern, which is typical for simple aromatic systems.

Can Aromaticity Be Evaluated Using Atomic Partitions Based on the Hilbert-Space?

Aromaticity is a fundamental concept in chemistry that explains the stability and reactivity of many compounds by identifying atoms within a molecule that form an aromatic ring. Reliable aromaticity indices focus on electron delocalization and depend on atomic partitions, which give rise to the concept of an atom-in-the-molecule (AIM). Real-space atomic partitions present two important drawbacks: a high computational cost and numerical errors, limiting some aromaticity measures to medium-sized molecules with rings up to 12 atoms. This restriction hinders the study of large conjugated systems like porphyrins and nanorings. On the other hand, traditional Hilbert-space schemes are free of the latter limitations but can be unreliable for the large basis sets required in modern computational chemistry. This paper explores AIMs based on three robust Hilbert-space partitions - meta-Löwdin, Natural Atomic Orbitals (NAO), and Intrinsic Atomic Orbitals (IAO) - which combine the advantages of real-space partitions without their disadvantages. These partitions can effectively replace real-space AIMs for evaluating the aromatic character. For the first time, we report multicenter index (MCI) and Iring values for large rings and introduce ESIpy, an open-source Python code for aromaticity analysis in large conjugated rings.

Prediction of Covalent Metal-Metal Bonding in Cp-M-M'-Nacnac Complexes of Group 2 and 12 Metals (Be, Mg, Ca, Zn, Cd, Hg).

Bimetallic CpMM'Nacnac molecules with group 2 and 12 metals (M = Be, Mg, Ca, Zn, Cd, Hg) that contain novel metal-metal bonding have been investigated in a theoretical study of their molecular and electronic structure, thermodynamic stability, and metal-metal bonding. In all cases the metal-metal bonds are characterized as electron-sharing covalent single bonds from natural bond orbital (NBO) and energy-decomposition analysis with natural orbitals of chemical valence (EDA-NOCV) analysis. The sum of [MM'] charges is relatively constant, with all complexes exhibiting a [MM']2+ core. Quantum theory of atoms in molecules (QTAIM) analysis indicates the presence of non-nuclear attractors (NNA) in the metal-metal bonds of the BeBe, MgMg, and CaCa complexes. There is substantial electron density (0.75-1.33 e) associated with the NNAs, which indicates that these metal-metal bonds, while classified as covalent electron-sharing bonds, retain significant metallic character that can be associated with reducing reactivity of the complex. The predicted stability of these complexes, combined with their novel covalent metal-metal bonding and potential as reducing agents, make them appealing targets for the synthesis of new metal-metal bonds.

Luminescent Supramolecular Metallogels: Drug Loading and Eu(III) as Structural Probe.

Supramolecular metallogels combine the rheological properties of gels with the color, magnetism, and other properties of metal ions. Lanthanide ions such as Eu(III) can be valuable components of metallogels due to their fascinating luminescence. In this work, we combine Eu(III) and iminodiacetic acid (IDA) into luminescent hydrogels. We investigate the tailoring of the rheological properties of these gels by changes in their metal:ligand ratio. Further, we use the highly sensitive Eu(III) luminescence to obtain information about the chemical structure of the materials. In special, we take advantage of computational calculations to employ an indirect method for structural elucidation, in which the simulated luminescent properties of candidate structures are matched to the experimental data. With this strategy, we can propose molecular structures for different EuIDA gels. We also explore the usage of these gels for the loading of bioactive molecules such as OXA, observing that its aldose reductase activity remains present in the gel. We envision that the findings from this work could inspire the development of luminescent hydrogels with tunable rheology for applications such as 3D printing and imaging-guided drug delivery platforms. Finally, Eu(III) emission-based structural elucidation could be a powerful tool in the characterization of advanced materials.

Unraveling the Source of Self-Induced Diastereomeric Anisochronism in Chiral Dipeptides.

Mastering of analytical methods for accurate quantitative determinations of enantiomeric excess is a crucial aspect in asymmetric catalysis, chiral synthesis, and pharmaceutical applications. In this context, the phenomenon of Self-Induced Diastereomeric Anisochronism (SIDA) can be exploited in NMR spectroscopy for accurate determinations of enantiomeric composition, without using a chiral auxiliary that could interfere with the spectroscopic investigation. This phenomenon can be particularly useful for improving the quantitative analysis of mixtures with low enantiomeric excesses, where direct integration of signals can be tricky. Here, we describe a novel analysis protocol to correctly determine the enantiomeric composition of scalemic mixtures and investigate the thermodynamic and stereochemical features at the basis of SIDA. Dipeptide derivatives were chosen as substrates for this study, given their central role in drug design. By integrating the experiments with a conformational stochastic search that includes entropic contributions, we provide valuable information on the dimerization thermodynamics, the nature of non-covalent interactions leading to self-association, and the differences in the chemical environment responsible for the anisochrony, highlighting the importance of different stereochemical arrangement and tight association for the distinction between homochiral and heterochiral adducts. An important role played by the counterion was pointed out by computational studies.

NBN- and BNB-Phenalenyls: the Yin and Yang of Heteroatom-doped π Systems.

NBN- and BNB-doped phenalenyls are isoelectronic to phenalenyl anions and cations, respectively. They represent a pair of complementary molecules that have essentially identical structures but opposite properties as electron donors and acceptors. The NBN-phenalenyls 1-4 considered here were prepared from N,N'-dimethyl-1,8-diaminonaphthalene and readily available boron-containing building blocks (i. e., BH3⋅SMe2 (1), p-CF3-C6H4B(OH)2 (2), C6H5B(OH)2 (3), or MesBCl2/iPr2NEt (4)). Treatment of 1 with 4-Me2N-2,6-Me2-C6H2Li gave the corresponding NBN derivative 5. The BNB-phenalenyl 6 was synthesized from 1,8-naphthalenediyl-bridged diborane(6), PhNH2, and MesMgBr. A computational study reveals that the photoemission of 1, 4, and 5 originates from locally excited (LE) states at the NBN-phenalenyl fragments, while that of 2 is dominated by charge transfer (CT) from the NBN-phenalenyl to the p-CF3-C6H4 fragment. Depending on the dihedral angle θ between its Ph and NBN planes, compound 3 emits mainly from a less polar LE (θ >55°) or more polar CT state (θ <55°). In turn, the energetic preference for either state is governed by the polarity of the solvent used. An equimolar aggregate of the NBN- and BNB-phenalenyls 3 and 6 (in THF/H2O) shows a distinct red-shifted emission compared to that of the individual components, which originates from an intermolecular CT state.

The Excited State Dynamics of a Mutagenic Guanosine Etheno Adduct Investigated by Femtosecond Fluorescence Spectroscopy and Quantum Mechanical Calculations.

Femtosecond fluorescence upconversion experiments were combined with CASPT2 and time dependent DFT calculations to characterize the excited state dynamics of the mutagenic etheno adduct 1,N2-etheno-2'-deoxyguanosine (ϵdG). This endogenously formed lesion is attracting great interest because of its ubiquity in human tissues and its highly mutagenic properties. The ϵdG fluorescence is strongly modified with respect to that of the canonical nucleoside dG, notably by an about 6-fold increase in fluorescence lifetime and quantum yield at neutral pH. In addition, femtosecond fluorescence upconversion experiments reveal the presence of two emission bands with maxima at 335 nm for the shorter-lived and 425 nm for the longer-lived. Quantum mechanical calculations rationalize these findings and provide absorption and fluorescence spectral shapes similar to the experimental ones. Two different bright minima are located on the potential energy surface of the lowest energy singlet excited state. One planar minimum, slightly more stable, is associated with the emission at 335 nm, whereas the other one, with a bent etheno ring, is associated with the red-shifted emission.

Theoretical Rotational and Vibrational Spectral Data for the Hypermagnesium Oxide Species Mg2O and Mg2O.

While magnesium is astronomically observed in small molecules, it largely serves as a contributor to silicate grains, though how these grains form is not well-understood. The smallest hypermagnesium oxide compounds (Mg 2 ${{}_{2}}$ O/Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ ) may play a role in silicate formation, but little vibrational reference data exist. As such, anharmonic spectroscopic data are computed for X ˜ 1 Σ g + ${{{\tilde{\rm {X}}}}^1 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O, a ˜ 1 Σ u + ${{{\tilde{\rm {a}}}}^1 {\rm{\Sigma }}_u^+ }$ Mg 2 ${{}_{2}}$ O, and X ˜ 2 Σ g + ${{{\tilde{\rm {X}}}}^2 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ using quartic force fields (QFFs). Explicitly-correlated coupled-cluster QFFs for the neutral species perform well, implying that full multireference treatment may not be necessary for such systems if enough electron correlation is included. Equation-of-motion ionization potential (EOMIP) methods for X ˜ 2 Σ g + ${{{\tilde{\rm {X}}}}^2 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ QFFs circumvent previous symmetry breaking issues even in explicitly-correlated coupled-cluster results, motivating the need for EOMIP treatments at minimum for such systems. All three species are found to have high-intensity vibrational frequencies. Even so, the highly intense frequency ( X ˜ 1 Σ g + ${{{\tilde{\rm {X}}}}^1 {\rm{\Sigma }}_g^+ }$ Mg 2 ${{}_{2}}$ O: 894.7 cm-1/11.18 µm; a ˜ 1 Σ u + ${{{\tilde{\rm {a}}}}^1 {\rm{\Sigma }}_u^+ }$ Mg 2 ${{}_{2}}$ O: 915.0 cm-1/10.91 µm) for either neutral state may be astronomically obscured by the polycyclic aromatic hydrocarbon 11.2 µm band. Mg 2 ${{}_{2}}$ O + ${{}^{+}}$ may be less susceptible to such obfuscation, and its ν 1 ${{\nu }_{1}}$ intensity is computed to be a massive 4793 km mol-1.

Toward Rational Design of Mononuclear Nickel Complexes as Water Oxidation Catalysts Exploring the Ligand Effects on the Rate-Determining Step.

The major impediment in realizing a carbon-neutral hydrogen fuel economy is the cost and inadequacy of contemporary electrochemical water splitting approaches towards the energy intensive oxygen evolution reaction (OER). The O-O bond formation in the water oxidation half-cell reaction is both kinetically and thermodynamically challenging and amplifies the overpotential requirement in most of the active water oxidation catalysts. Herein, density functional theory is employed to interrogate 20 Ni(II) complexes, out of which 17 are in silico designed molecular water oxidation catalysts, coordinated to electron-rich tetra-anionic redox non-innocent phenylenebis(oxamidate) and dibenzo-1,4,7,10-tetraazacyclododecane-2,3,8,9-tetraone parent ligands and their structural analogues, and identify the role of substituent changes or ligand effects in the order of their reactivity. Importantly, our computational mechanistic analyses predict that the activation free energy of the rate-determining O-O bond formation step obeys an inverse scaling relationship with the global electrophilicity index of the intermediate generated on two-electron oxidation of the starting complex. Additionally, the driving force is directly correlated with this OER descriptor which enables two-dimensional volcano representation and thereby extrapolation towards the ideal substitution with the chosen ligand. Our study, therefore, establish fundamental insights to overcome the imperative overpotential issue with simple and precise computational rationalization preceding experimental validation.