Molecular blueprints: Guiding drug discovery through protein structure analysis.

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two basic approaches of computer-aided drug design (CADD) used in modern drug discovery and development programme. Virtual screening (or in silico screening) has been used in drug discovery program as a complementary tool to high throughput screening (HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained considerable interest in the pharmaceutical research as a productive and cost-effective technology in search for novel molecules of medicinal interest. Docking is also used for virtual screening of new ligands on the basis of biological structures for identification of hits and generation of leads or optimization (potency/ property) of leads in drug discovery program. Hence, docking is approach of SBDD which plays an important role in rational designing of new drug molecules. Quantitative structure-activity relationship (QSAR) is an important chemometric tool in computational drug design. It is a common practice of LBDD. The study of QSAR gives information related to structural features and/or physicochemical properties of structurally similar molecules to their biological activity. In this paper, a comprehensive review on several computational tools of SBDD and LBDD such as virtual screening, molecular docking and QSAR methods of and their applications in the drug discovery and development programme have been summarized.
 Keywords: Virtual screening, Molecular docking, QSAR, Drug discovery, Lead molecule

Abstract: Drug design and discovery is a process that requires high financial costs and is timeconsuming. For many years, this process focused on empirical pharmacology. However, over the years, the target-based approach allowed a significant discovery in this field, initiating the rational design era. In view, to decrease the time and financial cost, rational drug design is benefited by increasing computer engineering and software development, and computer-aided drug design (CADD) emerges as a promising alternative. Since the 1970s, this approach has been able to identify many important and revolutionary compounds, like protease inhibitors, antibiotics, and others. Many anticancer compounds identified through this approach have shown their importance, being CADD essential in any drug discovery campaign. Thus, this perspective will present the prominent successful cases utilizing this approach and entering into the next stage of drug design. We believe that drug discovery will follow the progress in bioinformatics, using high-performance computing with molecular dynamics protocols faster and more effectively. In addition, artificial intelligence and machine learning will be the next process in the rational design of new drugs. Here, we hope that this paper generates new ideas and instigates research groups worldwide to use these methods and stimulate progress in drug design.

Chapter 11 - Fundamentals of drug design and discovery

Drug discovery is а time consuming рrосess of finding out a new drug molecule. The process takes many years to complete and needs human resource. These is difficulties have been overcome by introducing computer programmes in drug discovery (CADD) which includes target identification, hit identification, and molecular modification of а lead compound to optimize desired effects and minimize side effects, based on the knowledge of their biological targets. Molecular modelling is the process of designing a molecule with a computer-based collection of programmes (in-silico design) for deriving, representing, and manipulating the structures and reactions of molecules. Numerous Software tools, online data bases and computer programmes are used in the field of CADD in which some relevant, user friendly and precise ones are reviewed in this article. Software is available for personal use and for commercial purposes. All these tools are highly useful in the field of drug design and discovery. The article will be helpful for selecting a tool for computer aided drug design.

One of the key operations in the pharmaceutical sector is the creation of new drugs. The time and expense associated with drug discovery have been significantly lowered by various computer techniques. For more than three decades, the generation of therapeutically significant small molecules has been greatly facilitated by computer-aided drug discovery and design technologies. The use of contemporary molecular modelling tools to aid in the teaching of crucial principles of drug design to undergraduate students of chemistry and pharmacy is encouraged by the growing usage of information technology in the discovery of new molecular entities. In this chapter, we will discuss in detail about the study of the Computational Modelling and Methods in Drug Discovery such as “Structure-Based Drug Design” (SBDD) and “Ligand-Based Computer-Aided Drug Design,” ADMET prediction in Computer-Aided Drug Design, and Molecular Dynamic simulation in Drug Discovery.

1 Some Aspects of Computational Chemistry.- 2 Computer-Aided Drug Design in Industrial Research - A Management Perspective.- 3 Reflections on Collaborations of a Computational Chemist with Medicinal Chemists and Other Scientists.- 4 Structure-Based Ligand Design.- 5 Chances and Risks of Modeling in Industry.- 6 The Advantages of Using Rational Drug Design in Modern Drug Discovery: How to Integrate Computer-Aided Drug Design and Modern Biotechnology.- 7 Screening Three-Dimensional Databases for Lead Finding.- 8 Optimization of Peptide Leads and Molecular Modeling.- 9 Quantitative Structure-Activity Relationships and Crystallography in Industrial Drug Design.- 10 The Role of Structure-Based Ligand Design in Industrial Pharmaceutical Research.- 11 Drug Design Methods in Real-Life Situations: Recent Examples and Future Opportunities.- 12 Theoretical Chemistry as Part of the Interdisciplinary Approach to Rational Drug Design.- 13 Computer-Aided Drug Design in Industry: A Summary of Perspectives.- Previous Volumes Published in this Series.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

A drug takes many years to develop and reach the market using the conventional drug discovery procedure. Computer-aided drug design (CADD) is an emerging technology that accelerates the process of drug discovery and minimizes the total expenditure associated with labour and resources. In the current scenario, the computational aided drug design (CADD) techniques play a significant role in the design and development of lead molecules for the treatment of various lethal pathological conditions. The prediction of the tertiary structure of a protein is a big concern in drug design and discovery. A typical drug discovery procedure starts with the tertiary structure of a protein. At present, a total of 184,407 protein structures are available in the protein data bank, which are determinedusing experimental methods. However, the procedures are difficult and time-consuming. A more advanced technique has been developed for the prediction of the 3D structure of a protein using a computational method. This technique has played a vital role in drug discovery. It has not only facilitated but also hastened the process of drug discovery. The method is named homology modeling since it involves the building of a protein model based on its homology to similar evolutionary proteins. The method is based on the fact that evolutionary related proteins have similar structures. In homology modelling, the 3D structure of a protein is derived from its primary sequence based on its similarity to the existing protein templates. There are many computational tools for homology modelling such as Modeller, Swiss model, Composer, 3D-JIGSAW, etc. The proposed book chapter will cover the introduction to homology modelling, step-by-step guide to building a protein model, various challenges and how to refine and validate the model, different algorithms related to sequence alignment, similarity search, and the applications of homology modelling in drug design and discovery. The chapter would be very fruitful to the readers to get insights into protein modelling, which will facilitate their research activities. It will be of great application in various disciplines,such as bioinformatics, physics, structural biology, and molecular biology. The content of the chapter will cover various research papers, review papers, and corresponding reference books.

Chapter 25 - Tools and software for computer-aided drug design and discovery

Computational Medicinal Chemistry: Part II

Drug discovery is one of the most complicated processes and establishment of a single drug may require multidisciplinary attempts to design efficient and commercially viable drugs. The main purpose of drug design is to identify a chemical compound or inhibitor that can bind to an active site of a specific cavity on a target protein. The traditional drug design methods involved various experimental based approaches including random screening of chemicals found in nature or can be synthesized directly in chemical laboratories. Except for the long cycle design and time, high cost is also the major issue of concern. Modernized computer-based algorithm including structure-based drug design has accelerated the drug design and discovery process adequately. Surprisingly from the past decade remarkable progress has been made concerned with all area of drug design and discovery. CADD (Computer Aided Drug Designing) based tools shorten the conventional cycle size and also generate chemically more stable and worthy compounds and hence reduce the drug discovery cost. This special edition of editorial comprises the combination of seven research and review articles set emphasis especially on the computational approaches along with the experimental approaches using a chemical synthesizing for the binding affinity in chemical biology and discovery as a salient used in de-novo drug designing. This set of articles exfoliates the role that systems biology and the evaluation of ligand affinity in drug design and discovery for the future.

Drug discovery and development is a very time- and resource-consuming process. Comprehensive knowledge of chemistry has been integrated with information technology to streamline drug discovery, design, development, and optimization. Computer-aided drug design is being utilized to expedite and facilitate hit identification, hit-to-lead selection, and optimize the absorption, distribution, metabolism, excretion, and toxicity profile. Regulatory organizations and the pharmaceutical industry are continuously involved in the development of computational techniques that will improve the effectiveness and efficiency of the drug discovery process while decreasing the use of animals, cost, and time and increasing predictability. The present chapter will provide an overview of computational tools, such as structure-based and receptor-based drug designing, and how the coupling of these tools with a rational drug design process has led to the discovery of small molecules as therapeutic agents for numerous human disease conditions duly approved by the Food and Drug Administration. It is expected that the power of CADD will grow as the technology continues to evolve.

Viral diseases have caused millions of deaths around the world. In the past, health organizations and pharmaceutical industries have neglected these diseases for years, mainly because they affected a small geographic population. In contrast, since 2016, several viral outbreaks have been reported worldwide, such as those caused by Ebola, Zika, and SARS-CoV2 (COVID-19). Thus, these have received more attention, leading to increased efforts to search for new antiviral drugs. The SARS-CoV-2 pandemic, already responsible for more than 1,254,567 deaths worldwide, is the greatest example of a virus that has always been present in our society, responsible for small outbreaks in Asian and Arabic countries in 2004 and 2012. But, investments in research to identify/discover new drugs and vaccines were only intensified in 2020, in which only the remdesivir (an FDA-approved drug) was developed to addressCOVID19 until today. Nonetheless, it has been used in hospitals in the United States and Japan, in emergency cases. Indeed, it justifies greater investments in discovering new alternatives that could save thousands of people. In this context, improving drug discovery techniques is fundamental in searching for new therapies that could be selective and effective to combat SARS-CoV-2. Drug discovery approaches are based on ligands (Ligand-Based Drug Design - LBDD) or structures (Structure-Based Drug Discovery - SBDD). Concerning SBDD, it is the main and most evolved technique used for discovering new drugs. The application of SBDD techniques has improved the pharmacological arsenal against diverse diseases, which allowed the discovery of innovative treatments, such as inhibitors of HIV-1 proteases. In this chapter, main SBDD techniques (i.e. homology modeling; molecular dynamics and docking; de novo drug discovery; pharmacophore modeling; fragment-based drug discovery; and virtual high-throughput screenings) applied to discover new hit compounds SARS-CoV-2 (COVID-19) will be discussed in details.<br>

: Computer-aided drug design has an important role in drug development and design. It has become a thriving area of research in the pharmaceutical industry to accelerate the drug discovery process. Deep learning, a subdivision of artificial intelligence, is widely applied to advance new drug development and design opportunities. This article reviews the recent technology that uses deep learning techniques to ameliorate the understanding of drug-target interactions in computer-aided drug discovery based on the prior knowledge acquired from various literature. In general, deep learning models can be trained to predict the binding affinity between the protein-ligand complexes and protein structures or generate protein-ligand complexes in structure-based drug discovery. In other words, artificial neural networks and deep learning algorithms, especially graph convolutional neural networks and generative adversarial networks, can be applied to drug discovery. Graph convolutional neural network effectively captures the interactions and structural information between atoms and molecules, which can be enforced to predict the binding affinity between protein and ligand. Also, the ligand molecules with the desired properties can be generated using generative adversarial networks.