Frontiers in Computational Chemistry

The growing number of contagious viral diseases among different geographic regions has become a threat to human health and the economy on a global scale. Various viral epidemics in the past have caused huge casualties due to lack of effective vaccine, the recent outbreak of COVID-19 is a good example of it. Drug designing and development is a lengthy, tedious and expensive process that is always associated with a high level of uncertainty as the success rate of their approval as a drug is very low. Computer-aided drug designing by utilizing in silico methods has shown prominent ways to develop novel drugs in a cost-efficient manner and has evolved as a rescue in the past few years. Interestingly, the highest FDA approval reached a maximum (59 drugs) in 2018 for which a lot of credit goes to the successful development of computational chemistry tools for drug designing in the last two decades. These methods provide better chances of getting hit compounds in a far more accurate and faster way. Drug designing is a cyclic optimization process that involves various steps like creating a molecule, selecting the target for this molecule, analysing the binding pattern and estimating the pharmacokinetics of the molecule. The final development of a drug candidate is cumulative of positive results obtained in each aforementioned step. Various computational techniques/approaches such as molecular dynamic studies, homology modelling, ligand docking, pharmacophore modelling and QSAR can be utilized in each phase of the drug discovery cycle. In this chapter, we aim to highlight the recent advances that have taken place in developing tools and methodologies that lead to in silico preparation of novel drugs against various viral infections like Ebola, Zika, Hepatitis C and Coronavirus.

Cysteine proteases play numerous and extremely important roles in the life cycle of parasitic organisms with medicinal importance. From general catabolic functions and protein processing, cysteine proteases may be key to parasite immunoevasion, excystment/encystment, and cell and tissue invasion. Parasite cysteine proteases are unusually immunogenic and have been exploited as serodiagnostic markers and vaccine targets. The research focused on the development of new drugs actives toward this macromolecular target is an important task, where the rational design is considered as a critical step on it. The discovery of new drugs is a complex and multidisciplinary process, which includes an in-depth knowledge of organic chemistry, pharmacology, biochemistry, computer sciences, and others. This process involves high costs and several scientific fields, leading to the necessity to develop new processes that involve optimization of molecular modeling applied to the identification of bioactive molecules. These techniques could increase the probability of obtaining a rational-designed compound, with high activity and safety, which could be considered as a potential drug in the future. Thus, the use of computational techniques has become increasingly common in medical chemistry laboratories due to their low costs and high correlation with experimental results from assays. A broadly used technique in the rational design of active compounds is molecular docking of small ligand at the active site from the biological targets. In this chapter, we will demonstrate in detail different molecular modeling techniques applied to the development of new inhibitors against cruzain (Trypanosoma cruzi); falcipain (Plasmodium falciparum); SmHDAC8 (Schistosoma mansoni); nsP2 (Chikungunya virus) enzymes; and others, such as cathepsin family; caspase family, 3Cpro (Enterovirus 71) and 3CLpro (Coronavirus). Finally, studies have revealed that the application of molecular modeling is a powerful tool for predicting new active and productive molecules against infectious diseases.

A signalling pathway is a cascade of reactions carried out together by a group of molecules in a system to bring out the metabolic functions starting with cell division to cell death. When signalling pathways interact with one another, they form networks, which allow cellular responses to be coordinated, often by combinatorial signalling events. Any abnormal change affecting the activation or deactivation of such signalling events leads to the abnormal physiological cellular functions. The understanding of the molecular mechanism of signalling pathways is beneficial to understand the pathological scenery and treatment. Recent advancements in computational methods have given a new insight to understand the molecular mechanism involved in signalling pathways. The use of systemic and computational tools is crucial in systems biology as the complexity of the biological system is more, a vast amount of data is being generated and the scattered pieces of information has to be integrated into a meaningful order. The present chapter deals with the utilization of systems biology tools, and data mining techniques to understand the molecular mechanism of ‘Wnt signalling pathway; an intercellular pathway which regulates critical aspects of cell fate determination, cell migration, cell polarity, neural patterning and organogenesis during embryonic development’, with the integrated software platforms, which could help to address the future research problems in biology and medicine.

The molecular electrostatic potential (MEP) is a useful tool to design and develop drugs. However, the evaluation of this property using quantum chemistry methods presents a challenge for molecules of medium or large size since this is computationally expensive. In this chapter, we showed two implementations of this property over graphics processing units (GPU). In the first instance, we discussed some details that must be considered when GPUs are involved in high-performance computing. After this step, the algorithms considered to evaluate MEP over GPUs are exposed to observe the main differences between a method with minimal approximations and another one where usual approximations are implemented in many quantum chemistry codes. The benefits provided by these graphics cards are evidenced when our implementations are applied over molecules of considerable size like those found in protein-ligand complexes, where usually the electrostatic potential is modeled by a set of point charges.

Organic Chemistry has evolved continuously as the backbone for the sustainability of different disciplines such as medicinal chemistry, chemical biology, biochemistry, biotechnology, material science, polymers, and nanotechnology. The beauty of organic reactions lies in their unique structural framework, reactivity, and selectivity, interesting stuff for molecular modelling research. However, theoretical interpretations of organic reactions have not been able to keep pace with the everincreasing efficiency of computational chemistry software along the last two decades. This is probably due to the popular use of the Frontier Molecular Orbital (FMO) theory to study the course of organic reactions during the last 40 years, in spite of its failure and criticism in several cases. In 2016, Domingo proposed a new theory, called “Molecular Electron Density Theory (MEDT)” to study molecular reactivity of organic reactions, which is backed up by the use of quantum chemical tools. This theory proposes the decisive role of electron density changes in the reactivity of organic molecules, being opposed to FMO concepts. MEDT has been successfully applied to rationalize the experimental outcome of several Diels-Alder reactions, sigmatropic rearrangements, electrocyclic reactions, and [3+2] cycloaddition reactions. MEDT covers the detailed analysis of Conceptual DFT (CDFT) indices, exploration of the Potential Energy Surface (PES), calculation of the global electron density transfer (GEDT), topological analysis of the Electron Localization Function (ELF) and Quantum Theory of Atoms in Molecules (QTAIM), and Non-Covalent Interactions (NCI) with the aid of molecular modelling software. MEDT correlates the changes in electron density along a reaction path with the activation energies and establishes the polar character associated with the reorganization of the molecular mechanism to reach a meaningful insight into the reactivity of organic molecules. MEDT can be applied to study the mechanism, reactivities and selectivities of organic reactions, particularly those showing chemo-, regio-, and stereoselectivities in the synthesis of biologically active products. This chapter aims to provide a detailed description of the basic theoretical concepts covered by MEDT to design a precise computational model of an organic reaction. Some applications of MEDT have also been illustrated in the concluding section for ready reference.

The frontier molecular orbital (FMO) theory has provided a powerful model for the qualitative understanding of reactivity and regioselectivity of the Cycloaddition reactions, based on the electronic properties of isolated reactants. The 1,3-dipolar cycloaddition reactions are explicable by the frontier molecular orbital approach, which is based on the assumption that bonds are formed by a flow of electrons from the highest occupied molecular orbital (HOMO) of one reactant to the lowest unoccupied molecular orbital (LUMO) of another. But the tricky part here was to decide which molecule supplied the HOMO and which supplied the LUMO. Furthermore, the computational chemistry techniques like HOMOdipole-LUMOdipolarophile/HOMOdipolarophile- LUMOdipole energy gaps, electronic chemical potentials (μ), electrophilicity indices (ω) and the charge capacities (ΔNmax) are useful in indicating whether the reactions are under normal or inverse electron demand conditions. Also, the relative kinetics of cycloaddition reactions can be rationalized by utilizing HOMOdipole-LUMOdipolarophile energy gaps and ΔNmax, from which it was found that increasing electron-withdrawing power of the dipolarophile substituents, the energy gap decreases and, thus, reactions with the same dipole became faster in Normal electron demand cycloadditions, while the reverse occurs in case of inverse electron demand conditions.