Looking for the Elusive Imine Tautomer of Creatinine: Different States of Aggregation Studied by Quantum Chemistry and Molecular Spectroscopy

Development of a Predictive Model for N-Dealkylation of Amine Contaminants Based on Machine Learning Methods

Amines are widespread environmental pollutants that may pose health risks. Specifically, the N-dealkylation of amines mediated by cytochrome P450 enzymes (P450) could influence their metabolic transformation safety. However, conventional experimental and computational chemistry methods make it difficult to conduct high-throughput screening of N-dealkylation of emerging amine contaminants. Machine learning has been widely used to identify sources of environmental pollutants and predict their toxicity. However, its application in screening critical biotransformation pathways for organic pollutants has been rarely reported. In this study, we first constructed a large dataset comprising 286 emerging amine pollutants through a thorough search of databases and literature. Then, we applied four machine learning methods-random forest, gradient boosting decision tree, extreme gradient boosting, and multi-layer perceptron-to develop binary classification models for N-dealkylation. These models were based on seven carefully selected molecular descriptors that represent reactivity-fit and structural-fit. Among the predictive models, the extreme gradient boosting shows the highest prediction accuracy of 81.0%. The SlogP_VSA2 descriptor is the primary factor influencing predictions of N-dealkylation metabolism. Then an ensemble model was generated that uses a consensus strategy to integrate three different algorithms, whose performance is generally better than any single algorithm, with an accuracy rate of 86.2%. Therefore, the classification model developed in this work can provide methodological support for the high-throughput screening of N-dealkylation of amine pollutants.

Accurate Geometries of Large Molecules by Integration of the Pisa Composite Scheme and the Templating Synthon Approach

An effective yet reliable computational workflow is proposed, which permits the computation of accurate geometrical structures for large flexible molecules at an affordable cost thanks to the integration of machine learning tools and DFT models together with reduced scaling computations of vibrational averaging effects. After validation of the different components of the overall strategy, a panel of molecules of biological interest have been analyzed. The results confirm that very accurate geometrical parameters can be obtained at reasonable cost for molecules including up to about 50 atoms, which are the largest ones for which comparison with high-resolution rotational spectra is possible. Since the whole computational workflow can be followed employing standard electronic structure codes, accurate results for large-sized molecules can be obtained at DFT cost also by nonspecialists.

Quantum chemistry meets high-resolution spectroscopy for characterizing the molecular bricks of life in the gas-phase

Computation of accurate geometrical structures and spectroscopic properties of large flexible molecules in the gas-phase is tackled at an affordable cost using a general exploration/exploitation strategy. The most distinctive feature of the approach is the careful selection of different quantum chemical models for energies, geometries and vibrational frequencies with the aim of maximizing the accuracy of the overall description while retaining a reasonable cost for all the steps. In particular, a composite wave-function method is used for energies, whereas a double-hybrid functional (with the addition of core-valence correlation) is employed for geometries and harmonic frequencies and a cheaper hybrid functional for anharmonic contributions. A thorough benchmark based on a wide range of prototypical molecular bricks of life shows that the proposed strategy is close to the accuracy of state-of-the-art composite wave-function methods, and is applicable to much larger systems. A freely available web-utility post-processes the geometries optimized by standard electronic structure codes paving the way toward the accurate yet not prohibitively expensive study of medium- to large-sized molecules by experimentally-oriented researchers.

Solvation Plays a Key Role in Antioxidant-Mediated Attenuation of Elevated Creatinine Level: An In Vitro Spectroscopic Investigation

An elevated level of creatinine (CRN) is a mark of kidney ailment, and prolonged retention of such condition could lead to renal failure, associated with severe ischemia. Antioxidants are clinically known to excrete CRN from the body through urine, thereby reducing its level in blood. The molecular mechanism of such an exclusion process is still illusive. As the excretion channel is urine, solvation of the solute is expected to play a pivotal role. Here, we report a detailed time-domain and frequency-domain terahertz (THz) spectroscopic investigation to understand the solvation of CRN in the presence of two model antioxidants, mostly used to treat elevated CRN level: N-Acetyl-l-cysteine (NAC) and ascorbic acid (ASC). FTIR spectroscopy in the mid-infrared region and UV absorption spectroscopy measurements coupled with quantum chemical calculations [at the B3LYP/6-311G++(d,p) level] reveal that both NAC and ASC form HBonded complexes with CRN and rapidly undergo a barrier-less proton transfer process to form creatinium ions. THz measurements provide explicit evidence of the formation of highly solvated complexes compared with bare CRN, which eventually enables its excretion through urine. These observations could provide a foundation for designing more beneficial drugs to resolve kidney diseases..

Tautomeric Equilibrium in 1-Benzamidoisoquinoline Derivatives

In this study, the tautomeric equilibrium of a sequence of 1-benzamidoisoquinoline derivatives was investigated with the tools of NMR spectroscopy and computational chemistry. The equilibrium between different tautomers in these systems could be controlled via the substitution effect, and the relative content of the amide form varied from 74% for the strong electron-donating NMe2 substituent to 38% for the strong electron-accepting NO2 group in the phenyl ring. In contrast to the previously investigated 2-phenacylquinoline derivatives, the most stable and thus most abundant tautomer in the 1-benzamidoisoquinoline series except the two most electron-accepting substituents was an amide. The intramolecular hydrogen bond present in the enol tautomer competed with the intermolecular hydrogen bonds created with the solvent molecules and thus was not a sufficient factor to favor this tautomer in the mixture. Although routinely computational studies of tautomeric equilibrium are performed within the continuum solvent models, it is proven here that the inclusion of the explicit solvent is mandatory in order to reproduce the experimental tendencies observed for this type of system, facilitating strong intermolecular hydrogen bonds.

Shape‐Shifting Molecules: Unveiling the Valence Tautomerism Phenomena in Bare Barbaralones

We report a state‐of‐the‐art spectroscopic study of an archetypical barbaralone, conclusively revealing the valence tautomerism phenomena for this bistable molecular system. The two distinct 1‐ and 5‐substituted valence tautomers have been isolated in a supersonic expansion for the first time and successfully characterized by high‐resolution rotational spectroscopy. This work provides irrefutable experimental evidence of the [3,3]‐rearrangement in barbaralones and highlights the use of rotational spectroscopy to analyze shape‐shifting mixtures. Moreover, this observation opens the window toward the characterization of new fluxional systems in the isolation conditions of the gas phase and should serve as a reference point in the general understanding of valence tautomerism.

Development, Validation, and Pilot Application of a Generalized Fluctuating Charge Model for Computational Spectroscopy in Solution

A general approach enforcing nonperiodic boundary conditions for the computation of spectroscopic properties in solution has been improved including an effective description of charge-transfer contributions and coordination number adjustment for explicit solvent molecules. Both contributions are obtained from a continuous description of intermolecular hydrogen bonds, which has been employed also for an effective clustering of molecular dynamics trajectories. Fine tuning of the model has been performed for several water clusters, and then its efficiency and reliability have been demonstrated by computing the absorption spectra of different creatinine tautomers in aqueous solution.

Integration of theory, simulation, artificial intelligence and virtual reality: a four-pillar approach for reconciling accuracy and interpretability in computational spectroscopy

The established pillars of computational spectroscopy are theory and computer based simulations. Recently, artificial intelligence and virtual reality are becoming the third and fourth pillars of an integrated strategy for the investigation of complex phenomena. The main goal of the present contribution is the description of some new perspectives for computational spectroscopy, in the framework of a strategy in which computational methodologies at the state of the art, high-performance computing, artificial intelligence and virtual reality tools are integrated with the aim of improving research throughput and achieving goals otherwise not possible. Some of the key tools (e.g., continuous molecular perception model and virtual multifrequency spectrometer) and theoretical developments (e.g., non-periodic boundaries, joint variational-perturbative models) are shortly sketched and their application illustrated by means of representative case studies taken from recent work by the authors. Some of the results presented are already well beyond the state of the art in the field of computational spectroscopy, thereby also providing a proof of concept for other research fields.