Flexible Organic Molecules Research Articles

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.

Soft Hydrogen-Bonded Organic Frameworks Constructed Using a Flexible Organic Cage Hinge.

Soft porous crystals combine flexibility and porosity, allowing them to respond structurally to external physical and chemical environments. However, striking the right balance between flexibility and sufficient rigidity for porosity is challenging, particularly for molecular crystals formed by using weak intermolecular interactions. Here, we report a flexible oxygen-bridged prismatic organic cage molecule, Cage-6-COOH, which has three pillars that exhibit "hinge-like" rotational motion in the solid state. Cage-6-COOH can form a range of hydrogen-bonded organic frameworks (HOFs) where the "hinge" can accommodate a remarkable 67° dihedral angle range between neighboring units. This stems both from flexibility in the noncovalent hydrogen-bonding motifs in the HOFs and the molecular flexibility in the oxygen-linked cage hinge itself. The range of structures for Cage-6-COOH includes two topologically complex interpenetrated HOFs, CageHOF-2α and CageHOF-2β. CageHOF-2α is nonporous, while CageHOF-2β has permanent porosity and a surface area of 458 m2 g-1. The flexibility of Cage-6-COOH allows this molecule to rapidly transform from a low-crystallinity solid into the two crystalline interpenetrated HOFs, CageHOF-2α and CageHOF-2β, under mild conditions simply by using acetonitrile or ethanol vapor, respectively. This self-healing behavior was selective, with the CageHOF-2β structure exhibiting structural memory behavior.

Assessment of protein-ligand complexes in CASP15.

CASP15 introduced a new category, ligand prediction, where participants were provided with a protein or nucleic acid sequence, SMILES line notation, and stoichiometry for ligands and tasked with generating computational models for the three-dimensional structure of the corresponding protein-ligand complex. These models were subsequently compared with experimental structures determined by x-ray crystallography or cryoEM. To assess these predictions, two novel scores were developed. The Binding-Site Superposed, Symmetry-Corrected Pose Root Mean Square Deviation (BiSyRMSD) evaluated the absolute deviations of the models from the experimental structures. At the same time, the Local Distance Difference Test for Protein-Ligand Interactions (lDDT-PLI) assessed the ability of models to reproduce the protein-ligand interactions in the experimental structures. The ligands evaluated in this challenge range from single-atom ions to large flexible organic molecules. More than 1800 submissions were evaluated for their ability to predict 23 different protein-ligand complexes. Overall, the best models could faithfully reproduce the geometries of more than half of the prediction targets. The ligands' size and flexibility were the primary factors influencing the predictions' quality. Small ions and organic molecules with limited flexibility were predicted with high fidelity, while reproducing the binding poses of larger, flexible ligands proved more challenging.

Ultrafast Electronic Coupling Estimators: Neural Networks versus Physics-Based Approaches.

Fast and accurate estimation of electronic coupling matrix elements between molecules is essential for the simulation of charge transfer phenomena in chemistry, materials science, and biology. Here we investigate neural-network-based coupling estimators combined with different protocols for sampling reference data (random, farthest point, and query by committee) and compare their performance to the physics-based analytic overlap method (AOM), introduced previously. We find that neural network approaches can give smaller errors than AOM, in particular smaller maximum errors, while they require an order of magnitude more reference data than AOM, typically one hundred to several hundred training points, down from several thousand required in previous ML works. A Δ-ML approach taking AOM as a baseline is found to give the best overall performance at a relatively small computational overhead of about a factor of 2. Highly flexible π-conjugated organic molecules like non-fullerene acceptors are found to be a particularly challenging case for ML because of the varying (de)localization of the frontier orbitals for different intramolecular geometries sampled along molecular dynamics trajectories. Here the local symmetry functions used in ML are insufficient, and long-range descriptors are expected to give improved performance.

WS22 database, Wigner Sampling and geometry interpolation for configurationally diverse molecular datasets

Multidimensional surfaces of quantum chemical properties, such as potential energies and dipole moments, are common targets for machine learning, requiring the development of robust and diverse databases extensively exploring molecular configurational spaces. Here we composed the WS22 database covering several quantum mechanical (QM) properties (including potential energies, forces, dipole moments, polarizabilities, HOMO, and LUMO energies) for ten flexible organic molecules of increasing complexity and with up to 22 atoms. This database consists of 1.18 million equilibrium and non-equilibrium geometries carefully sampled from Wigner distributions centered at different equilibrium conformations (either at the ground or excited electronic states) and further augmented with interpolated structures. The diversity of our datasets is demonstrated by visualizing the geometries distribution with dimensionality reduction as well as via comparison of statistical features of the QM properties with those available in existing datasets. Our sampling targets broader quantum mechanical distribution of the configurational space than provided by commonly used sampling through classical molecular dynamics, upping the challenge for machine learning models.

Key factors in the adsorption of natural organic matter to metal (hydr)oxides: Fractionation and conformational change

Adsorption of natural organic matter (NOM) to mineral surfaces is an important process determining the environmental fate and biogeochemical cycling of many elements. Natural organic matter consists of a heterogeneous mixture of soft and flexible organic molecules. Upon adsorption, size fractionation may occur, as well as changes in molecular conformation. Although very important, these phenomena have been omitted in existing adsorption models. Filling this gap, a novel framework for NOM adsorption to metal (hydr)oxides is presented. Humic acid (HA) was used as an analog for studying experimentally the NOM adsorption to goethite and its size fractionation as a function of pH, ionic strength, and surface loading. Size fractionation was evaluated for adsorption isotherms collected at pH 4 and 6, showing HA molecules of low molar mass were preferentially adsorbed. This phenomenon was incorporated into the new model. Consistent description of the HA adsorption data over the entire range of pH (3–11), ionic strength (2–100 mM), and surface loading (0.1–3 mg m−2) indicated that the spatial distribution of HA molecules adsorbed in the interface is a trade-off between maximizing the interaction of the HA ligands with the oxide surface and minimizing the electrostatic repulsion between HA particles as a result of interfacial crowding. Our advanced consistent framework is able to quantify changes in molar mass and molecular conformation, thereby significantly contributing to an improved understanding of the competitive power of HA for interacting on oxides with other adsorbed small organic acids as well as environmentally important oxyanions, such as phosphate, arsenate, and others.

DFT investigations of AgMC7H10N2 (M = Cl, Br, and I) metal organic molecules: NMR, optoelectronic, and transport properties.

The full-potential linearized augmented plane wave (FP-LAPW) method was used for the calculation of the structural, nuclear magnetic resonance (NMR), optoelectronic, and thermoelectric properties of AgMC7H10N2 (M = Cl, Br, and I) compounds. The calculated wide band gap of AgMC7H10N2 (M = Cl, Br, and I) metal organic molecules with the density of states approach were 3.32, 3.29, and 3.10eV, respectively. The NMR parameters are calculated for the Ag, Cl, Br, I, C, N, O, and H elements. It is found that by decreasing bandgap, the isotropic NMR chemical shielding values of Cl, Br, and I elements increase. The strong hybridization of Ag-4d, Cl-3p, Br-4p, and I-5p states are observed at the top of the valence band. The birefringence and anisotropic properties are observed in the optical spectra with high plasmon energies, and the figure of merit, ZT, of 0.98 for AgCl(C7H10N2) compound is found at 300K. Hence, these compounds are attractive flexible metal organic molecules for optoelectronic and transport applications.

Reorganization energies of flexible organic molecules as a challenging target for machine learning enhanced virtual screening

Reorganization energies of flexible molecules pose a challenge for machine learning (ML) models. Combining them with semiempirical electronic structure methods helps, but the benefit for virtual screening is surprisingly small.

2,3-diaminophenazine as a high-rate rechargeable aqueous zinc-ion batteries cathode

Organic materials are attracting extensive attention as promising cathodes for rechargeable aqueous zinc-ion batteries (ZIBs). However, most of them fail to implement the requirement of batteries with combined high-rate and long-cycle performance. Herein, we report a flexible organic molecule 2,3-diaminophenazine (DAP) which exhibits ultrahigh rate performance up to 500C and high capacity retention of 80% after 10,000 cycles at 100C (25.5 A g−1). Moreover, the Zn2+ storage mechanism in the DAP electrode is revealed by ex-situ characterization technologies and theoretical calculation, and the redox active centers CN participate in the reversible electrochemical reaction process. Furthermore, electrochemical analyses show that surface-controlled electrochemical behavior contributes to the high-rate performance of DAP cathodes. Besides, its excellent long-cycle performance can be ascribed to the suppressed DAP dissolubility by using a modified glass fiber separator with carbon nanotubes (CNT) film. Our work provides useful insight into the design of high-rate and long-life ZIBs.

A Scalable Graph Neural Network Method for Developing an Accurate Force Field of Large Flexible Organic Molecules

An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accurate correlated wave function (CW) methods scale poorly with system size, so this poses a great challenge to the development of an extendible ab initio force field for large flexible organic molecules at the CW level of accuracy. In this work, we combine the physics-driven nonbonding potential with a data-driven subgraph neural network bonding model (named sGNN). Tests on polyethylene glycol, polyethene, and their block polymers show that our strategy is highly accurate and robust for molecules of different sizes and chemical compositions. Therefore, one can develop a parameter library of small molecular fragments (with sizes easily accessible to CW methods) and assemble them to predict the energy of large polymers, thus opening a new path to next-generation organic force fields.

Rechargeable Aqueous Aluminum Organic Batteries.

Aqueous aluminum-ion batteries (AABs) are regarded as promising next-generation energy storage devices, and the current reported cathodes for AABs mainly focused on inorganic materials which usually implement a typical Al3+ ions (de)insertion mechanism. However, the strong electrostatic forces between Al3+ and the host materials usually lead to sluggish kinetics, poor reversibility and inferior cycling stability. Herein, we employ an organic compound with redox-active moieties, phenazine (PZ), as the cathode material in AABs. Different from conventional inorganic materials confined by limited lattice spacing and rigid structure, the flexible organic molecules allow a large-size Al-complex co-intercalation through reversible redox active centers (-C=N-) of PZ. This co-intercalation behavior can effectively reduce desolvation penalty, and substantially lower the Coulombic repulsion during the ion (de)insertion process. Consequently, this organic cathode exhibits a high capacity and excellent cyclability, which exceeds those of most reported electrode materials for AABs. This work highlights the anion co-intercalation chemistry of redox-active organic materials, which is expected to boost the development of high-performance multivalent-ion battery systems.

Rechargeable Aqueous Aluminum Organic Batteries

AbstractAqueous aluminum‐ion batteries (AABs) are regarded as promising next‐generation energy storage devices, and the current reported cathodes for AABs mainly focused on inorganic materials which usually implement a typical Al3+ ions (de)insertion mechanism. However, the strong electrostatic forces between Al3+ and the host materials usually lead to sluggish kinetics, poor reversibility and inferior cycling stability. Herein, we employ an organic compound with redox‐active moieties, phenazine (PZ), as the cathode material in AABs. Different from conventional inorganic materials confined by limited lattice spacing and rigid structure, the flexible organic molecules allow a large‐size Al‐complex co‐intercalation through reversible redox active centers (‐C=N‐) of PZ. This co‐intercalation behavior can effectively reduce desolvation penalty, and substantially lower the Coulombic repulsion during the ion (de)insertion process. Consequently, this organic cathode exhibits a high capacity and excellent cyclability, which exceeds those of most reported electrode materials for AABs. This work highlights the anion co‐intercalation chemistry of redox‐active organic materials, which is expected to boost the development of high‐performance multivalent‐ion battery systems.

Evaluation of fluorine-mediated intermolecular interactions in tetrafluorinated tetrahydroisoquinoline derivatives: synthesis and computational studies.

Intermolecular interactions involving the aromatic C-F group in the absence of other strong hydrogen bond acceptors is the theme of this article. Weak interactions involving fluorine are known to generate various supramolecular synthons, thereby altering the crystal structures of small organic molecules. It is demonstrated that the weak interactions involving organic fluorine play a major role in directing crystal packing of highly flexible organic molecules like diphenyl tetrahydroisoquinolines reported herein. The intramolecular C-H...F hydrogen bonds are found to be significant in controlling the molecular conformation in specific cases wheras the intermolecular interactions involving the C-F groups result in a wide range of supramolecular synthons involving C-H...F and C-F...F-C interactions. The interactions are studied computationally to provide insight into their energies and the topology of the interactions is studied using Atoms in Molecules. C-H...F-C interactions are found to be quite stabilizing in nature with the stabilization energy of -13.9 kcal mol-1.

Assembly of Wells‐Dawson Polyoxometalate based Crystal Compound for Uric Acid Electrochemical Detection

A new Wells‐Dawson polyoxometalates (POMs) based crystal supramolecular compound modified by flexible organic molecule bix, (H2bix)3(P2W18O62)·H2O (bixP2W18), [bix = 1,4‐bis(imidazol‐1‐ylmethyl)benzene], was synthesized, characterized and explored as electrochemical biosensor to detect uric acid (UA) for the first time. Under optimized conditions, the bixP2W18 based sensor shows acceptable analytical performances for UA in terms of good linearity in the concentration range from 2.5 × 10–7 to 6.99 × 10–4 M and the lower detection limit 5.85 × 10–7 M (S/N = 3). More important is that bixP2W18 based UA sensor exhibits excellent stability testified by the result of XRD and IR, reproducibility, high selectivity in the presence of ascorbic acid (AA), dopamine (DA) and anti‐interference immunity than some other reported materials. The bixP2W18 based biosensor could be applied for the determination of UA in the human urine samples with satisfactory results.

Hamiltonian-Reservoir Replica Exchange and Machine Learning Potentials for Computational Organic Chemistry.

This work combines a machine learning potential energy function with a modular enhanced sampling scheme to obtain statistically converged thermodynamical properties of flexible medium-size organic molecules at high ab initio level. We offer a modular environment in the python package MORESIM that allows custom design of replica exchange simulations with any level of theory including ML-based potentials. Our specific combination of Hamiltonian and reservoir replica exchange is shown to be a powerful technique to accelerate enhanced sampling simulations and explore free energy landscapes with a quantum chemical accuracy unattainable otherwise (e.g., DLPNO-CCSD(T)/CBS quality). This engine is used to demonstrate the relevance of accessing the ab initio free energy landscapes of molecules whose stability is determined by a subtle interplay between variations in the underlying potential energy and conformational entropy (i.e., a bridged asymmetrically polarized dithiacyclophane and a widely used organocatalyst) both in the gas phase and in solution (implicit solvent).

A machine learning based intramolecular potential for a flexible organic molecule.

Quantum mechanical predictive modelling in chemistry and biology is often hindered by the long time scales and large system sizes required of the computational model. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that our machine learning based intramolecular model is transferable to the condensed phase, and demonstrate that the use of a faithful representation of the quantum mechanical potential energy surface can result in corrections to absolute protein-ligand binding free energies of up to 2 kcal mol-1 in the example studied here.

Quantum Chemistry Meets Machine Learning.

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the power brought by kernel-based approaches in the large-scale screening of homogeneous catalysis, the prediction of fundamental quantum chemical properties and the free-energy landscapes of flexible organic molecules. While certainly non-exhaustive, these examples provide an intriguing glimpse into our own research efforts.

Structure Determination of a Chloroenyne from Laurencia majuscula Using Computational Methods and Total Synthesis.

Despite numerous advances in spectroscopic methods through the latter part of the 20th century, the unequivocal structure determination of natural products can remain challenging, and inevitably, incorrect structures appear in the literature. Computational methods that allow the accurate prediction of NMR chemical shifts have emerged as a powerful addition to the toolbox of methods available for the structure determination of small organic molecules. Herein, we report the structure determination of a small, stereochemically rich natural product from Laurencia majuscula using the powerful combination of computational methods and total synthesis, along with the structure confirmation of notoryne, using the same approach. Additionally, we synthesized three further diastereomers of the L. majuscula enyne and have demonstrated that computations are able to distinguish each of the four synthetic diastereomers from the 32 possible diastereomers of the natural product. Key to the success of this work is to analyze the computational data to provide the greatest distinction between each diastereomer, by identifying chemical shifts that are most sensitive to changes in relative stereochemistry. The success of the computational methods in the structure determination of stereochemically rich, flexible organic molecules will allow all involved in structure determination to use these methods with confidence.

Probing Crystal Nucleation of Fenoxycarb from Solution through the Effect of Solvent

Induction time experiments, spectroscopic and calorimetric analysis, and molecular modeling were used to probe the influence of solvent on the crystal nucleation of fenoxycarb (FC), a medium-sized, flexible organic molecule. A total of 800 induction times covering a range of supersaturations and crystallization temperatures in four different solvents were measured to elucidate the relative ease of nucleation. To achieve similar induction times, the required thermodynamic driving force, RT ln S, increases in the order: ethyl acetate < toluene < ethanol < isopropanol. This is roughly matched by the order of interfacial energies calculated using the classical nucleation theory. Solvent–solute interaction strengths were estimated using three methods: solvent–solute enthalpies derived from calorimetric solution enthalpies, solvent–solute interactions from molecular dynamics simulations, and the FTIR shifts in the carbonyl stretching corresponding to the solvent–solute interaction. The three methods gave an ove...

Determination of the absolute configuration of conformationally flexible molecules by simulation of chiro-optical spectra: a case study.

The assignment of the absolute configuration (AC) of two conformational flexible organic molecules by means of TD-DFT simulation of the electronic circular dichroism (ECD) spectra is presented. The factors leading to a reliable assignment were evaluated in the various steps of the process. The effects of different functionals and basis sets in the geometry optimization step is very limited in terms of the resulting optimized geometries, whereas the inclusion of the solvent in the calculations has a much larger effect on the correct evaluation of the conformational ratio. B3LYP and M06-2x were found to be the most accurate functionals for geometry optimization. CAM-B3LYP and ωB97X-D provided the best results in the TD-DFT simulations.