A clear-cut definition of lone pairs has been offered in terms of characteristics of minima in molecular electrostatic potential (MESP). The largest eigenvalue and corresponding eigenvector of the Hessian at the minima are shown to distinguish lone pair regions from the other types of electron localization (such as π bonds). A comparative study of lone pairs as depicted by various other scalar fields such as the Laplacian of electron density and electron localization function is made. Further, an attempt has been made to generalize the definition of lone pairs to the case of cations.

An electrostatics-based approach has been proposed for probing the weak interactions between lone pair containing molecules and π deficient molecular systems. For electron-rich molecules, the negative minima in molecular electrostatic potential (MESP) topography give the location of electron localization and the MESP value at the minimum (Vmin) quantifies the electron-rich character of that region. Interactive behavior of a lone pair bearing molecule with electron deficient π-systems, such as hexafluorobenzene, 1,3,5-trinitrobenzene, 2,4,6-trifluoro-1,3,5-triazine and 1,2,4,5-tetracyanobenzene explored within DFT brings out good correlation of the lone pair-π interaction energy (E(int)) with the Vmin value of the electron-rich system. Such interaction is found to be portrayed well with the Electrostatic Potential for Intermolecular Complexation (EPIC) model. On the basis of the precise location of MESP minimum, a prediction for the orientation of a lone pair bearing molecule with an electron deficient π-system is possible in the majority of the cases studied.

Accurate force‐field (FF) parameters are key to reliable prediction of properties obtained from molecular modeling (MM) and molecular dynamics (MD) simulations. With ever‐widening applicability of MD simulations, robust parameters need to be generated for a wider range of chemical species. The CHARMM General Force Field program (CGenFF, https://cgenff.umaryland.edu/) is a tool for obtaining initial parameters for a given small molecule based on analogy with the available CGenFF parameters. However, improvement of these parameters is often required and performing their optimization remains tedious and time consuming. In addition, tools for optimization of small molecule parameters in the context of the Drude polarizable FF are not yet available. To overcome these issues, the FFParam package has been designed to facilitate the parametrization process. The package includes a graphical user interface (GUI) created using Qt libraries. FFParam supports Gaussian and Psi4 for performing quantum mechanical calculations and CHARMM and OpenMM for MM calculations. A Monte Carlo simulated annealing (MCSA) algorithm has been implemented for automated fitting of partial atomic charge, atomic polarizabilities and Thole scale parameters. The LSFITPAR program is called for automated fitting of bonded parameters. Accordingly, FFParam provides all the features required for generation and analysis of CHARMM and Drude FF parameters for small molecules. FFParam‐GUI includes a text editor, graph plotter, molecular visualization, and text to table converter to meet various requirements of the parametrization process. It is anticipated that FFParam will facilitate wider use of CGenFF as well as promote future use of the Drude polarizable FF.

Nonstandard amino acids are both abundant in nature, where they play a key role in various cellular processes, and can be synthesized in laboratories, for example, for the manufacture of a range of pharmaceutical agents. In this work, we have extended the additive all-atom CHARMM36 and CHARMM General force field (CGenFF) to a large set of 333 nonstandard amino acids. These include both amino acids with nonstandard side chains, such as post-translationally modified and artificial amino acids, as well as amino acids with modified backbone groups, such as chromophores composed of several amino acids. Model compounds representative of the nonstandard amino acids were parametrized for protonation states that are likely at the physiological pH of 7 and, for some more common residues, in both D-and L-stereoisomers. Considering all protonation, tautomeric, and stereoisomeric forms, a total of 406 nonstandard amino acids were parametrized. Emphasis was placed on the quality of both intra-and intermolecular parameters. Partial charges were derived using quantum mechanical (QM) data on model compound dipole moments, electrostatic potentials, and interactions with water. Optimization of all intramolecular parameters, including torsion angle parameters, was performed against information from QM adiabatic potential energy surface (PES) scans. Special emphasis was put on the quality of terms corresponding to PES around rotatable dihedral angles. Validation of the force field was based on molecular dynamics simulations of 20 protein complexes containing different nonstandard amino acids. Overall, the presented parameters will allow for computational studies of a wide range of proteins containing nonstandard amino acids, including natural and artificial residues.

DAMQT-2.1.0 is a new version of DAMQT package which includes topographical analysis of molecular electron density (MED) and molecular electrostatic potential (MESP), such as mapping of critical points (CPs), creating molecular graphs, and atomic basins. Mapping of CPs is assisted with algorithmic determination of Euler characteristic in order to provide a necessary condition for locating all possible CPs. Apart from the mapping of CPs and determination of molecular graphs, the construction of MESP-based atomic basin is a new and exclusive feature introduced in DAMQT-2.1.0. The GUI in DAMQT provides a user-friendly interface to run the code and visualize the final outputs. MPI libraries have been implemented for all the tasks to develop the parallel version of the software. Almost linear scaling of computational time is achieved with the increasing number of processors while performing various aspects of topography. A brief discussion of molecular graph and atomic basin is provided in the current article highlighting their chemical importance. Appropriate example sets have been presented for demonstrating the functions and efficiency of the code.

The Drude polarizable force field (FF) captures electronic polarization effects via auxiliary Drude particles that are attached to non-hydrogen atoms, distinguishing it from commonly used additive FFs that rely on fixed charges. The Drude FF currently includes parameters for biomolecules such as proteins, nucleic acids, lipids, and carbohydrates and small-molecule representative of those classes of molecules as well as a range of atomic ions. Extension of the Drude FF to novel small druglike molecules is challenging as it requires the assignment of partial charges, atomic polarizabilities, and Thole scaling factors. In the present article, deep neural network (DNN) models are trained on quantum mechanical (QM)-based partial charges and atomic polarizabilities along with Thole scale factors trained to target QM molecular dipole moments and polarizabilities. Training of the DNN model used a collection of 39 421 molecules with molecular weights up to 200 Da and containing H, C, N, O, P, S, F, Cl, Br, or I atoms. The DNN model utilizes bond connectivity, including 1,2, 1,3, 1,4, and 1,5 terms and distances of Drude FF atom types as the feature vector to build the model, allowing it to capture both local and nonlocal effects in the molecules. Novel methods have been developed to determine restrained electrostatic potential (RESP) charges on atoms and external points representing lone pairs and to determine Thole scale factors, which have no QM analogue. A penalty scheme is devised as a performance predictor of the trained model. Validation studies show that these DNN models can precisely predict molecular dipole and polarizabilities of Food and Drug Administration (FDA)-approved drugs compared to reference MP2 calculations. The availability of the DNN model allowing for the rapid estimation of the Drude electrostatic parameters will facilitate its applicability to a wider range of molecular species.

The nature of electron localization in electrides is explored by examining their electrostatic features. Ab initio investigations of three experimentally synthesized and two theoretically modeled organic electrides are performed in order to unveil the characteristics of the trapped electron and to understand the reason for their low thermal stability. A single molecular unit of the electride extracted from the crystal structure shows an unusually deep minimum in its electrostatic potential, located far away from its van der Waals surface. A comparison of electrostatic features of the usual electron localization such as lone pairs has been drawn against those of the trapped electron in the crystal voids of electrides. Further characterization of the MESP minimum brings out the isotropic behavior of the trapped electrons as compared to the lone-pair minimum which is strongly directional. The analysis of single molecular behavior of an electride has been extended to the set of molecules in the unit cell of the crystal lattice. The present study also suggests the criteria for ligands to achieve thermally stable organic electrides.

The outcomes of computational chemistry and biology research, including drug design, are significantly influenced by the underlying force field (FF) used in molecular simulations. While improved FF accuracy may be achieved via inclusion of explicit treatment of electronic polarization, such an extension must be accompanied by optimization of van der Waals (vdW) interactions, in the context of the Lennard-Jones (LJ) formalism in the present study. This is particularly challenging due to the extensive nature of chemical space combined with the correlated nature of LJ parameters. To address this challenge, a deep learning (DL)-based parametrization framework is developed, allowing for sampling of wide ranges of LJ parameters targeting experimental condensed phase thermodynamic properties. The present work utilizes this framework to develop the LJ parameters for atoms associated with four distinct groups covering 10 different atom types. Final parameter selection was facilitated by quantum mechanical data on rare-gas interactions with the training set molecules. The chosen parameters were then validated through experimental hydration free energies and condensed phase thermodynamic properties of validation set molecules to confirm transferability. The ultimate outcome of utilizing this framework is a set of LJ parameters in the context of the polarizable Drude FF, which demonstrated improvement in the reproduction of both experimental pure solvent and crystal properties and hydration free energies of the molecules compared to the additive CHARMM General FF (CGenFF) including the ability of the Drude FF to accurately reproduce both experimental pure solvent properties and hydration free energies. The study also shows how correlations between difference in the reproduction of condensed phase data between model compounds may be used to direct the selection of new atom types and training set molecules during FF development.