Frontiers in Computational Chemistry

In molecular design techniques, thermodynamic properties are predicted through computational tools. Besides, the simple prediction methods explain the space of molecular design while quantum mechanics can accurately predict the properties without any kind of experimental data; however, it is a bit challenging. Therefore, in this chapter, the significant advancement, demurrers in progression, and the future perspective in designing the chemical compounds via using “computer-aided molecular design” (CAMD) tools will be elucidated. Since the interest in designing novel and advanced compounds is increasing with time, traditional methods are not efficient now. This is the key factor in the advancement of CAMD tools. The work advancement different classes of methods that predict the properties will be explained in the chapter. Applications of CAMD in the single component product designs, mixture designs, and also in integrated product designs will be evaluated. All the difficulties while operating the designs and also in obtaining the results and future perspectives will be reviewed. COSMO-CAMD successfully designs novel promising solvents in the liquid-liquid extraction of phenol from water; therefore, it will be explained thoroughly. Some would debate that theoretical tools in computational chemistry can now come up with eager understandings of any chemical process. Yet, the goblet of effective and reliable prediction of compound reactivity has remained fugitive. Favorably, recent developments in the electronic structure theory, which is based on both concepts, element, and rank-scanty, along with the appearance of the highly sophisticated computer architecture, prominently increased the time and length scales that can be simulated using molecular dynamics. This opens the door for the newly proposed ab initio nanoreactor method. Therefore, ab initio methods will be studied completely because we argue that due to this development in molecular designs, the holy grail of computational discovery for complex chemical reactivity is entirely within our reach.

Protein interactions with various other macromolecules is a key biological phenomenon for the molecular recognition process leading to various physiological functions. Throughout decades, researchers have proposed various methods for the investigation of such binding mechanism, starting from static, rigid docking to flexible docking approaches. Rational drug designing approaches were improvised by introducing semi- to full-flexibility in the protein-ligand molecular recognition process, conformational dynamics, and binding kinetics and thermodynamics of conserved waters in the binding site. A better understanding of ligand-binding is quintessential to gain more quantitative and accurate information about molecular recognition for drug and therapeutic interventions. To address these issues, Ensemble docking approaches were introduced, which include protein flexibility through a different set of protein conformations either experimentally or with computational simulations i.e., molecular dynamics simulations. MD simulations enable ensemble construction which generates an array of binding site conformations for multiple docking trials of the same protein, though sometimes poorly sampled. To overcome the same, enhanced sampling was introduced. In this chapter, the theoretical background of molecular docking, classical MD simulations, MD-based enhanced sampling methods and hybrid docking-MD based methods are highlighted, demonstrating how protein flexibility has been introduced to optimize and enhance accurate protein-ligand binding predictions. Overall, the evolution of various computational strategies is discussed, from molecular docking to molecular dynamics simulations, to improve the overall drug discovery and development process.

In recent years, the world has faced several outbreaks caused by viral diseases, resulting in deaths and comorbidities, harming the health of the population. Due to the “constant” discovery of new antivirals, vaccines, hygiene habits, and basic sanitation, society had the false impression of being free from these diseases. However, since the 1980s, various outbreaks have occurred, such as HIV (Human immunodeficiency virus) and recently, ZIKV (Zika virus), CHIKV (Chikungunya virus), and EBOV (Ebola virus) have increased the concern about such pathogens, resulting in advances in drug discovery. In addition, the SARS-CoV-2 outbreak responsible for 27,417,497 cases, and 894,241 deaths (to date, September 9th, 2020), showed how scientists should advance to end this disease so damaging to the global health and economy. In this context, researches focused on drug development have been improved in recent years. Thus, it is essential to use computational approaches to accelerate drug discovery in laboratories. Based on this, structure-based drug design (SBDD) techniques constitute the most used computer-aided approaches for discovering and developing new drugs. Among these techniques, molecular dynamics (MD) simulations have been essential steps and their use in virtual screening studies is considered indispensable. The MD considers the macromolecule flexibility using Newtonian principles applied to proteins, enzymes, membranes, nucleic acids, and other systems. Thus, it is possible to analyze protein-ligand interactions, and also the affinity energy that a determined ligand exhibits towards its target. Such information is indispensable for designing and optimizing new active agents. This chapter will be  addressed to concepts and applications of MD simulations, as well as their applications in the discovery of drugs against Coronaviruses (SARS-, MERS-CoV, and SARSCoV- 2); Influenza (INFV); Chikungunya (CHIKV); Zika (ZIKV); Dengue (DENV); Ebola (EBOV); and human immunodeficiency virus (HIV), constituting a great source of helpful information that could be utilized for designing new compounds against these diseases.

Malaria remains to be a life-threatening disease in the developing world. Recent reports show that the worldwide progress in reducing malaria has slowed. It accounts for causing more than 2.2 million cases and 405,000 deaths in 2018. Therefore, the situation demands the need for the development of new techniques or drugs against malaria. Several antimalarials have shown improvement in the treatment of malaria, but the emergence of drug resistance has intensified the need for the development of novel drugs. Drug discovery is an expensive, laborious, and timetaking process. Alternative to traditional drug design, computer-aided drug design plays a significant role. In this respect, a class of computational techniques known as pharmacophore modeling is considered beneficial for discovering novel lead compounds. Pharmacophore modeling with the virtual screening method has become a popular method for the screening of hit molecules. Pharmacophore modeling techniques are often implemented with molecular docking to improve the outcome of the virtual screening. The current study focuses on the pharmacophore modeling methods used to discover various novel antimalarials. According to the literature, this method is valuable in processes like virtual screening, design of effective hit molecules, and optimization of lead towards clinical trials. The reader will gain insight into the successful applications of the pharmacophore-based virtual screening to discover antimalarials.

Traditional Chinese Medicine (TCM) is a complementary and alternative medicine but possesses remarkable clinical efficacy in China and surrounding countries. Hence, systematic analysis and elucidation of the complex chemical basis and action mechanisms of TCM will be highly beneficial. Nowadays, the widespread application of network pharmacology has unveiled the mystery of TCM to some extent by constructing the relationship of “herb-compound-target-disease”. Moreover, it can promote the development of drug discovery, medical guidance, and the dissection of the syndrome in TCM. With the integration of computational techniques into network pharmacology, the efficiency of data mining and the accuracy of active compounds identification and target fishing have been improved, and the “herb-compound-targt- disease” network has been more systematically and comprehensively explained to reflect the holistic mechanisms of TCM. Therefore, a comprehensive overview of each aspect of the use of computational techniques in TCM network pharmacology is urgent. This chapter systematically dissects the core contents involved in TCM computational network pharmacology and highlights its application on TCM against COVID-19, and severs the cutting-edge study examples to compare and analyze the advantages and limitations of different computational techniques.

A review of ab initio computational chemistry methods that can be used for accurate studies of molecules and molecular design and simulation of chemical phenomena with applications that are relevant in exploring biological activity is presented. The review includes a discussion of recent computational approaches that account for the effects of electron correlation to a high degree and computational methods that seek to describe large molecular systems with reduced computational cost yet achieving good quality results. Comparison with available experimental data demonstrates the effectiveness of these computational methods in estimating accuracy, reliability, and scalability of the computational approaches discussed in this review. In recent years, the understanding of biological systems using electronic structure theorybased computational methods with applications to biology and medicine has gained increased interest. We draw special attention to the computational methods capable of describing phenomena relevant to biological activity and drug discovery and development, as well as the design of new materials relevant to understanding complex biological systems. As an application of these electronic structure methods, we include the case study of perboranation in aza-derivatives of aromatic five and six-membered rings.