Applied Computer-Aided Drug Design: Models and Methods

The drug discovery and development process are challenging and have undergone many changes over the last few years. Academic researchers and pharmaceutical companies invest thousands of dollars a year to search for drugs capable of improving and increasing people's life quality. This is an expensive, time-consuming, and multifaceted process requiring the integration of several fields of knowledge. For many years, the search for new drugs was focused on Target-Based Drug Design methods, identifying natural compounds or through empirical synthesis. However, with the improvement of molecular modeling techniques and the growth of computer science, Computer-Aided Drug Design (CADD) emerges as a promising alternative. Since the 1970s, its main approaches, Structure-Based Drug Design (SBDD) and Ligand-Based Drug Design (LBDD), have been responsible for discovering and designing several revolutionary drugs and promising lead and hit compounds. Based on this information, it is clear that these methods are essential in drug design campaigns. Finally, this chapter will explore approaches used in drug design, from the past to the present, from classical methods such as bioisosterism, molecular simplification, and hybridization, to computational methods such as docking, molecular dynamics (MD) simulations, and virtual screenings, and how these methods have been vital to the identification and design of promising drugs or compounds. Finally, we hope that this chapter guides researchers worldwide in rational drug design methods in which readers will learn about approaches and choose the one that best fits their research.

Recently, many new methods have been used in the research and development of a new drug. In this article, QSAR, which is one of the usable areas of artificial intelligence during molecule research, and the analysis and formulation studies related to the suitability of this area are discussed. It is explained how a model to be created is prepared and calculation formulas for how to verify this model are shown. Examples of the most recent 4D-QSAR calculations are given.

Computer-Aided Drug Design (CADD) has become an integral part of drug discovery and development efforts in the pharmaceutical and biotechnology industry. Since the 1980s, structure-based design technology has evolved, and today, these techniques are being widely employed and credited for the discovery and design of most of the recent drug products in the market. Pharmacophore-based drug design provides fundamental approach strategies for both structure-based and ligand-based pharmacophore approaches. The different programs and methodologies enable the implementation of more accurate and sophisticated pharmacophore model generation and application in drug discovery. Commonly used programmes are GALAHAD, GASP, PHASE, HYPOGEN, ligand scout etc. In modern computational chemistry, pharmacophores are used to define the essential features of one or more molecules with the same biological activity. A database of diverse chemical compounds can then be searched for more molecules which share the same features located at a similar distance apart from each other. Pharmacophore requires knowledge of either active ligands and/or the active site of the target receptor. There are a number of ways to build a pharmacophore. It can be done by common feature analysis to find the chemical features shared by a set of active compounds that seem commonly important for receptor interaction. Alternately, diverse chemical structures for certain numbers of training set molecules, along with the corresponding IC50 or Ki values, can be used to correlate the three-dimensional arrangement of their chemical features with the biological activities of training set molecules. There are many advantages in pharmacophore based virtual screening as well as pharmacophore based QSAR, which exemplify the detailed application workflow. Pharmacophore based drug design process includes pharmacophore modelling and validation, pharmacophore based virtual screening, virtual hits profiling, and lead identification. The current chapter on pharmacophores also describes case studies and applications of pharmacophore mapping in finding new drug molecules of specific targets. 

Homology modeling is used to predict protein 3D structure from its amino acid sequence. It is the most accurate computational approach to estimate 3D structures. It has straightforward steps that save time and labor. There are several homology modeling tools under use. There is no sole tool that is superior in every aspect. Hence, the user should select the most appropriate one carefully. It is also a common practice to use two or more tools at a time and choose the best model among the resulting models. Homology modeling has various applications in the drug design and development process. Such applications need high-quality 3D structures. It is widely used in combination with other computational methods including molecular docking and molecular dynamics simulation. Like the other computational methods, it has been influenced by the involvement of artificial intelligence. In this regard, homology modeling tools, like AlphaFold, have been introduced. This type of method is expected to contribute to filling the gap between protein sequence release and 3D structure determination. This chapter sheds light on the history, relatively popular tools and steps of homology modeling. A detailed explanation of MODELLER is also given as a case study protocol. Furthermore, homology modeling’s application in drug discovery is explained by exemplifying its role in the fight against the novel Coronavirus. Considering the new advances in the area, better tools and thus high-quality models are expected. These, in turn, pave the way for more applications of it. 

Molecular docking involves the interaction of a molecule with another place, usually in the protein structure, and simulating the placement of the molecule in the protein structure with certain score algorithms, taking into account many quantities, such as the electro-negativity of atoms, their positions to each other, and the conformation of the molecule to be inserted into the protein structure. Finally, the activity of the molecule with the highest percentage by mass against various cancer proteins was investigated according to the GC-MS results made on some medicinal and aromatic plants in order to set an example of molecular docking calculations.

Fragment-based drug or lead discovery (FBDD or FBLD) refers to as one of the most significant approaches in the domain of current research in the pharmaceutical industry as well as academia. It offers a number of advantages compared to the conventional drug discovery approach, which include – 1) It needs the lesser size of chemical databases for the development of fragments, 2) A wide spectrum of biophysical methodologies can be utilized for the selection of the best fit fragments against a particular receptor, and 3) It is far more simpler, feasible, and scalable in terms of the application when compared to the classical high-throughput screening methods, making it more popular day by day. For a fragment to become a drug candidate, they are analyzed and evaluated on the basis of numerous strategies and criteria, which are thoroughly explained in this chapter. One important term in the field of FBDD is de novo drug design (DNDD), which means the design and development of new ligand molecules or drug candidates from scratch using a wide range of in silico approaches and algorithmic tools, among which AI-based platforms are gaining large attraction. A principle segment of AI includes DRL that finds numerous applicabilities in the DNDD sector, such as the discovery of novel inhibitors of BACE1 enzyme, identification and optimization of new antagonists of DDR1 kinase enzyme, and development and design of ligand molecules specific to target adenosine A2A, etc. In this book chapter, several aspects of both FBDD and DNDD are briefly discussed. 

Molecular interaction is the basis for protein and cellular function. Careful inhibition or modulation of these is the main goal of therapeutic compounds. In the pharmaceutical field, this process is referred to as pharmacodynamics. Over the years, there have been several hypotheses attempting to describe this complex phenomenon. From a purely biophysical point of view, molecular interactions may be attributed to pairwise contributions such as charge angles, torsions, and overall energy. Thus, the computation of binding affinity is possible, at least in principle. Over the last half of the past century, molecular simulation was developed using a combination of physics, mathematics, and thermodynamics. Currently, these methods are known as structure-based drug design (SBDD) and it has become a staple of computer-aided drug design (CADD). In this chapter, we present an overview of the theory, current advances, and limitations of molecular dynamics simulations. We put a special focus on their application to virtual screening and drug development. 

Drug design and development are expensive and time-consuming processes, which in many cases result in failures during the clinical investigation steps. In order to increase the chances to obtain potential drug candidates, several in silico approaches have emerged in the last years, most of them based on molecular or quantum mechanics theories. These computational strategies have been developed to treat a large dataset of chemical information associated with drug candidates. In this context, quantum chemistry is highlighted since it is based on the Schrödinger equation with mathematic solutions, especially the Born-Oppenheimer approximation. Among the Hartree-Fock-based methods, the Density Functional Theory (DFT) of HohenbergKohn represents an interesting and powerful tool to obtain accurate results for electronic properties of molecules or even solids, which in many cases are corroborated by experimental data. Additionally, DFT-related methods exhibit a moderate time-consuming cost when compared to other ab initio methods. In this chapter, we provide a deep overview focused on the formalism behind DFT, including historical aspects of its development and improvements. Moreover, different examples of the application of DFT in studies involving GABA inhibitors, or catalytic mechanisms of enzymes, such as RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2, and different proteases associated impacting diseases, such as malaria, Chagas disease, human African trypanosomiasis, and others. Moreover, the role of metal ions in catalytic enzymatic mechanisms is also covered, discussing iron-, copper-, and nickel-catalyzed processes. Finally, this chapter comprises several aspects associated with the elucidation of catalytic mechanisms of inhibition, which could be used to develop new potential pharmacological agents.

Drug development is a remarkably complex subject, with potency and specificity being the desired traits in the early stages of research. Yet, these need careful thought and rational design, which has led to the inclusion of multidisciplinary efforts and non-chemistry methods in the ever-changing landscape of medicinal chemistry. Computational approximation of protein-ligand interactions is the main goal of the so-called structure-based methods. Over the years, there has been a notable improvement in the predictive power of approaches like molecular force fields. Mainstream applications of these include molecular docking, a well-known method for high-throughput virtual screening. Still, even with notable success cases, the search for accurate and efficient methods for free energy estimation remains a major goal in the field. Recently, with the advent of technology, more exhaustive simulations are possible in a reasonable time. Herein, we discuss free energy predictions and applications of perturbation theory, with emphasis on their role in molecular design and drug discovery. Our aim is to provide a concise but comprehensive view of current trends, best practices, and overall perspectives in this maturing field of computational chemistry.