Electrostatically embedded molecules-in-molecules approach and its application to molecular clusters

Intermolecular interaction energies with AROFRAG-A systematic approach for fragmentation of aromatic molecules

Intermolecular interactions with polycyclic aromatic hydrocarbons (PAHs) represent an important area of physisorption studies. These investigations are often hampered by a size of interacting PAHs, which makes the calculation prohibitively expensive. Therefore, methods designed to deal with large molecules could be helpful to reduce the computational costs of such studies. Recently we have introduced a new systematic approach for the molecular fragmentation of PAHs, denoted as AROFRAG, which decomposes a large PAH molecule into a set of predefined small PAHs with a benzene ring being the smallest unbreakable unit, and which in conjunction with the Molecules-in-Molecules (MIM) approach provides an accurate description of total molecular energies. In this contribution we propose an extension of the AROFRAG, which provides a description of intermolecular interactions for complexes composed of PAH molecules. The examination of interaction energy partitioning for various test cases shows that the AROFRAG3 model connected with the MIM approach accurately reproduces all important components of the interaction energy. An additional important finding in our study is that the computationally expensive long-range electron-correlation part of the interaction energy, that is, the dispersion component, is well described at lower AROFRAG levels even without MIM, which makes the latter models interesting alternatives to existing methods for an accurate description of the electron-correlated part of the interaction energy.

Landscape of In Silico Tools for Modeling Covalent Modification of Proteins: A Review on Computational Covalent Drug Discovery

Covalent drug discovery has been a challenging research area given the struggle of finding a sweet balance between selectivity and reactivity for these drugs, the lack of which often leads to off-target activities and hence undesirable side effects. However, there has been a resurgence in covalent drug design following the success of several covalent drugs such as boceprevir (2011), ibrutinib (2013), neratinib (2017), dacomitinib (2018), zanubrutinib (2019), and many others. Design of covalent drugs includes many crucial factors, where "evaluation of the binding affinity" and "a detailed mechanistic understanding on covalent inhibition" are at the top of the list. Well-defined experimental techniques are available to elucidate these factors; however, often they are expensive and/or time-consuming and hence not suitable for high throughput screens. Recent developments in in silico methods provide promise in this direction. In this report, we review a set of recent publications that focused on developing and/or implementing novel in silico techniques in "Computational Covalent Drug Discovery (CCDD)". We also discuss the advantages and disadvantages of these approaches along with what improvements are required to make it a great tool in medicinal chemistry in the near future.

Fragment-based models for dissociation of strong acids in water: Electrostatic embedding minimizes the dependence on the fragmentation schemes

Fragmentation methods such as MIM (Molecules-in-Molecules) provide a route to accurately model large systems and have been successful in predicting their structures, energies, and spectroscopic properties. However, their use is often limited to systems at equilibrium due to the inherent complications in the choice of fragments in systems away from equilibrium. Furthermore, the presence of charges resulting from any heterolytic bond breaking may increase the fragmentation error. We have previously suggested EE-MIM (Electrostatically Embedded Molecules-In-Molecules) as a method to mitigate the errors resulting from the missing long-range interactions in molecular clusters in equilibrium. Here, we show that the same method can be applied to improve the performance of MIM to solve the longstanding problem of dependency of the fragmentation energy error on the choice of the fragmentation scheme. We chose four widely used acid dissociation reactions (HCl, HClO4, HNO3, and H2SO4) as test cases due to their importance in chemical processes and complex reaction potential energy surfaces. Electrostatic embedding improves the performance at both one and two-layer MIM as shown by lower EE-MIM1 and EE-MIM2 errors. The EE-MIM errors are also demonstrated to be less dependent on the choice of the fragmentation scheme by analyzing the variation in fragmentation energy at the points with more than one possible fragmentation scheme (points where the fragmentation scheme changes). EE-MIM2 with M06-2X as the low-level resulted in a variation of less than 1 kcal/mol for all the cases and 1 kJ/mol for all but three cases, rendering our method fragmentation scheme-independent for acid dissociation processes.

EE-ONIOM-CT Method to Efficiently Account for the Missing Interactions in ONIOM: Energies and Analytic Gradients

Hybrid methods such as ONIOM (QM:QM) are widely used for the study of local processes in large systems. However, the intrinsic need for system partitioning often leads to a less-than-desirable performance for some important chemical processes. This is due to the missing interactions in the chemically important model region (i.e., active site) of the high-level theory. The missing interactions can be categorized into two classes, viz. charge transfer (i.e., charge redistribution) and long-range electrostatic interactions. Our group presented two entirely different methods to treat these deficiencies individually. ONIOM-CT and ONIOM-EE methods have been demonstrated to improve the performance of ONIOM by incorporating charge transfer and missing electrostatic interactions, respectively. In general, the inclusion of the missing interactions separately in two different calculations may not be sufficient to reach a high accuracy. Thus, it is highly desirable to develop a method to correct both deficiencies simultaneously. In this work, we aim to connect the methods ONIOM-CT and ONIOM-EE for a more comprehensive treatment. A "stepwise" model was found to be necessary for a robust performance. This model employs a stepwise procedure by first satisfying the ONIOM-CT condition for charge balance before accounting for the electrostatic interactions from the rest of the system perturbatively. This has the advantage of easy interpretation due to the clear separation of the two effects. We demonstrate the performance of our method using embedding charges determined from a Mulliken population analysis. An efficient analytic gradient expression for this method is derived and implemented by requiring three sets of z-vector self-consistent equations. The performance of our method is assessed against full system calculations in high-level theory for a set of three proton transfer reactions representing different degrees of electrostatic embedding.

Benchmarking various types of partial atomic charges for classical all-atom simulations of metal-organic frameworks

The density derived electrostatic and chemical (DDEC) approach for calculating the charges of atoms in a metal-organic framework (MOF) is considered to be the most accurate (yet computationally costly) one among many charge-assignment methods. Here, we conducted a comparative study on five different types of atomic partial charges (namely CM5, Mulliken, Qeq, EQeq and PACMOF) prepared for a subset of MOFs with affordable computational costs and benchmarked them with respect to the DDEC charges, which is particularly relevant because currently most databases lack MOFs with pre-calculated DDEC charges. To find a suitable charge type alternative to the DDEC approach, we statistically ranked the five charge types based on two metrics, the relative standard deviation of charges and relative dipole moment difference, based on which we provide general guidance as well as suggestions for specific MOFs according to bond polarity analyses. Finally, we recommend a possible and more accurate parametrization scheme for future studies.