Reactive orbital energy theory serving a theoretical foundation for the electronic theory of organic chemistry

It is established that the reactive orbital energy theory (ROET) theoretically reproduces the rule-based electronic theory diagrams of organic chemistry by a comparative study on the charge transfer natures of typical organic carbon-carbon and carbon-heteroatom bond formation reactions: aldol, Mannich, α-aminooxylation, and isogyric reactions. The ROET, which is an expansion of the reaction electronic theories (e.g., the frontier orbital theory) in terms of orbital energies, elucidates the reactive orbitals driving reactions and the charge transferability indices of the reactions. Performing the ROET analyses of these reactions shows that the charge transfer directions given in the rule-based diagrams of the electronic theory are reproduced even for the functional groups of charge transfer destinations in all but only two processes for 38 reaction processes. The ROET analyses also make clear the detailed orbital-based pictures of these bond formation reactions: that is, the use of the out-of-plane antibonding π orbitals in acidic conditions (enol-mode) and in-plane antibonding π orbitals in basic conditions (enolate-mode), which explain the experimentally assumed mechanisms such as the π-bond formations in acidic conditions and σ-bond formations at α-carbons in basic conditions. Furthermore, the ROET analyses explicate that the methyl group initially accepts electrons and then donates them to the bond formations in the target reactions. It is, consequently, suggested that the ROET serves a theoretical foundation for the electronic theory of organic chemistry.

Natural reaction orbitals for characterizing electron transfer responsive to nuclear coordinate displacement

The natural reaction orbital (NRO) is proposed as a new concept for analyzing chemical reactions from the viewpoint of the electronic theory. The pair of the occupied and virtual NROs that characterize electron transfer responsive to nuclear coordinate displacement along the reaction path is automatically extracted from the solution of the coupled-perturbed self-consistent-field (CPSCF) equation for the perturbation of the nuclear displacement. The NRO-based reaction analysis method is applied to several reactions. As a result, it is found that the sum of squares of the singular values, derived from the solution of the CPSCF equation, gives sharp peaks around the transition state structures and at the shoulders of the potential energy curve. The peaks around the transition states suggest a new physical meaning of transition state from the viewpoint of the electronic theory. Furthermore, the double peaks reveal the asynchronous processes of reactions, which are not always shown in potential energy analyses. Since the NRO-based reaction analysis method is universal and robust for describing reaction mechanisms from an electronic theory viewpoint, it is expected to lead to universal reaction analyses based on the electronic theory.

One-to-One Correspondence between Reaction Pathways and Reactive Orbitals

The one-to-one correspondence between reaction pathways in the potential energy theory and reactive orbitals in the electronic theory for reactions is presented. In this study, the reactive orbital energy method is applied to the intrinsic reaction coordinates of the global reaction route map generated by an automated reaction path search method. The reactive orbital energy method specifies the pairs of occupied and unoccupied reactive orbitals driving chemical reactions and determines whether the reactions are electron transfer-driven or dynamics-driven. Surprisingly, it is found that the reactive orbital pairs are determined one by one for the electron transfer-driven reaction pathways from an identical molecule. The reactive orbital energy method is also found to provide the sophisticated interpretations of reactions for the electronic motions. This one-to-one correspondence is expected to trigger the unification of the potential energy theory and the electronic theory for reactions that have been independently developed.

Artificial Intelligence and Machine Learning in Computational Nanotoxicology: Unlocking and Empowering Nanomedicine

Advances in nanomedicine, coupled with novel methods of creating advanced materials at the nanoscale, have opened new perspectives for the development of healthcare and medical products. Special attention must be paid toward safe design approaches for nanomaterial-based products. Recently, artificial intelligence (AI) and machine learning (ML) gifted the computational tool for enhancing and improving the simulation and modeling process for nanotoxicology and nanotherapeutics. In particular, the correlation of in vitro generated pharmacokinetics and pharmacodynamics to in vivo application scenarios is an important step toward the development of safe nanomedicinal products. This review portrays how in vitro and in vivo datasets are used in in silico models to unlock and empower nanomedicine. Physiologically based pharmacokinetic (PBPK) modeling and absorption, distribution, metabolism, and excretion (ADME)-based in silico methods along with dosimetry models as a focus area for nanomedicine are mainly described. The computational OMICS, colloidal particle determination, and algorithms to establish dosimetry for inhalation toxicology, and quantitative structure-activity relationships at nanoscale (nano-QSAR) are revisited. The challenges and opportunities facing the blind spots in nanotoxicology in this computationally dominated era are highlighted as the future to accelerate nanomedicine clinical translation.

DFT-Based QSAR Modelling of Inhibitory Activity of Coumarins and Sulfocoumarins on Carbonic Anhydrase (CA) Isoforms (CA I and CA II)

OBJECTIVE

We present three robust, validated and statistically significant quantitative structure-activity relationship (QSAR) models, which deal with the calculated molecular descriptors and experimental inhibition constant (Ki) of 42 coumarin and sulfocoumarin derivatives measured against CA I and II isoforms.

METHODS

The compounds were subjected to DFT calculations in order to obtain quantum chemical molecular descriptors. Multiple linear regression algorithms were applied to construct QSAR models. Separation of the compounds into training and test sets was accomplished using Kennard-Stone algorithm. Leverage approach was applied to determine Applicability Domain (AD) of the obtained models.

RESULTS

Three models were developed. The first model, CAI_model1 comprises 30/11 training/test compounds with the statistical parameters of R2=0.85, Q2=0.77, F=27.57, R2 (test) =0.72. The second one, CAII_model2 comprises 30/12 training/test compounds with the statistical parameters of R2=0.86, Q2=0.78, F=30.27, R2 (test) =0.85. The final model, ΔpKi_model3 consists of 25/3 training/ test compounds with the statistical parameters of R2=0.78, Q2=0.62, F=13.80 and R2(test) =0.99.

CONCLUSION

Interpretation of reactivity-related descriptors such as HOMO-1 and LUMO energies and visual inspection of their maps of orbital electron density leads to a conclusion that the binding free energy of the entire binding process may be modulated by the kinetics of the hydrolyzing step of coumarins.