Computational Methods in Drug Discovery and Development

Over the last two decades, computational technologies have played a crucial role in antiviral drug development. Whenever a virus spreads and becomes a threat to global health, it brings along the challenge of developing new therapeutics and prophylactics. Computational drug and vaccine discovery has evolved quickly over the years. Some interesting examples of computational drug discovery are anti-AIDS drugs, where HIV protease and reverse transcriptase have been targeted by agents developed using computational methods. Various computational methods that have been applied to anti-viral research include ligand-based methods that rely on known active compounds, i.e., pharmacophore modeling, machine learning or classical QSAR; structure-based methods that rely on an experimentally determined 3D structure of the targets, i.e., molecular docking and molecular dynamics and methods for the development of vaccines such as reverse vaccinology; structural vaccinology and vaccine epitope prediction. This review summarizes these approaches to battle viral diseases and underscores their importance for anti-viral research. We discuss the role of computational methods in developing small molecules and vaccines against human immunodeficiency virus, yellow fever, human papilloma virus, SARS-CoV-2, and other viruses. Various computational tools available for the abovementioned purposes have been listed and described. A discussion on applying artificial intelligence-based methods for antiviral drug discovery has also been included.

The present and following issues of ADMET and DMPK are dedicated to the 3rd World Conference on Physico-Chemical Methods in Drug Discovery and Development (PCMDDD-3) held in Dubrovnik, Croatia, 23-26 September 2013. PCMDDD-3 is organized as a biannual event and is intended to provide a place and common ground for the scientific community whose work is closely related to the application of physical chemistry in ADME and DMPK research to get together in an open and relaxing atmosphere to exchange ideas and discuss challenges. The topics of the last conference included: physico-chemical methods and instrumentation in the physico-chemical profiling of drug substances, ADME and DMPK, determination and characterization of different solid forms, hydrates and polymorphs, separation and analytical techniques of importance in medicinal chemistry and pharmaceutical research, computational methods and modelling. More details of the PCMDDD-3 can be found on the conference webpage: http://www.iapchem.org/page.php?page_id=34.

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

Allostery is a universal, biological phenomenon in which orthosteric sites are fine‐tuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post‐translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large‐scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in‐depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure‐based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure‐based computational methods.This article is categorized under:Structure and Mechanism > Computational Biochemistry and Biophysics

The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein–ligand docking, pharmacophore modeling and QSAR techniques are reviewed.

Polypharmacology: The Rise of Multitarget Drugs over Combination Therapies

Artificial intelligence and machine learning in drug discovery and development

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a “gold standard” platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.


 
 
 SARS-CoV2 has affected millions of people around the globe with hundreds of mortalities. The emergence of SARS-COV2 is very recent, and there is no potential drug or vaccine available. In this review, we have compiled the most frequently used computational methods in drug discovery, target proteins of SARS-CoV2 as well as implementation of computational methods. Most recent literature on SARS-CoV2 has been compiled from various journal search engines including Google Scholar, Academia, PubMed, Scopus, Research Gate, and the Web of Science. The keywords chosen for the searches were COVID-19, Corona Virus, SARS-CoV2, drug development and future directions. This review has far reaching implications to both the public health and pharmaceutical industries for potential novel drug development against SARS-CoV2.
 
 

Over the past few years, small molecule screening has undergone a dramatic shift from simple ‘cheap and fast’ high-throughput assay systems to more complex screening setups where one aims to use more physiologically relevant systems. The new edition of ‘Methods in Molecular Biology: Drug Design and Discovery’ aims to cover aspects of this new approach with a comprehensive volume about various strategies in drug discovery. This volume is an easily accessible collection of protocols covering a broad range of different screening methodologies. The book is divided into 16 chapters that cover topics such as the design of large therapeutic molecules (DNA or peptides), revised protocols for the use of conventional assays in primary screening, computational methods and animal models for pre-clinical evaluation of drug candidates. One particular emphasis is the use of computational methods in drug discovery, which is covered in the first four chapters of the book. Chapter 4 (‘Methods for evaluation of structural and biological properties of anti-invasive natural products’) deserves particular mention in this regard, and the authors provide protocols for isolation and purification of compounds, the use of 3D model systems in cell culture and 3D QSAR modelling applications. Another major emphasis of this book is the use of animal models in primary screening (such as chapter 12, ‘Chemical screening with zebrafish embryos’ and chapter 10, ‘Imaging NFkB signaling in mice for screening anticancer drugs’) or in pre-clinical testing (chapter 15, ‘Evaluation of antibacterial activity of proteins and peptides using a specific animal model for wound healing’). Other noteworthy areas covered are the use of mass spectroscopy, in vivo imaging in drug discovery, and the use of in vitro phage display for selection of peptide ligands in human placental transcytosis systems (chapter 8; clearly a gem in this book). The strong academic background of the authors makes this book highly recommendable to everyone involved in drug screening and has a particular appeal to an academic audience. Written in the highly successful format of the Methods in Molecular Biology series, each chapter includes a background on the topic, a list of materials required, a detailed step-by-step description of the methods and a notes section that draws attention to potential pitfalls and highlights some of the key steps in the procedure. All protocols provide very detailed instructions, a characteristic of the Methods in Molecular Biology series. The only minor drawback of this volume is the slightly misleading title. This collection of protocols touches very briefly on drug design. The book rather focuses on novel approaches to drug screening and physiological validation. It is highly recommended for all those who are performing drug screening or who aim to be doing so in the near future.

The present issue of ADMET and DMPK is dedicated to the IAPC-10 Meeting, which was organized as a joint event consisting of 10th World Conference on Physico-Chemical Methods in Drug Discovery and Develop¬ment and 6th World Conference on ADMET and DMPK. The meeting took place in the University of Belgrade Rectorate building, Belgrade, Republic of Serbia, September 4-6, 2023. IAPC meetings are organized as annual events in alternating European and East Asia locations. The topics covered the most advanced directions and new achievements in physico-chemical methods, which underlie almost all instrumental techniques used in the research processes in drug discovery and pharmaceutical development. Experimental determination of ADMET properties through the in vitro and in vivo assays was discussed as well as modern theoretical methods for computer-aided drug design. Ten sessions were organized, among which two special sessions: 1) Special Session on Solubility of Multi-Component Solids (Salts and Cocrystals), organized and co-chaired by Kiyohiko Sugano and Alex Avdeef, and 2) Special Session on the ADME properties and toxicity prediction by HPLC, organized and chaired by Klara Valko. Editorial

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

The methods utilized in the drug discovery pipeline routinely combine machine learning and deep learning algorithms to enhance the outputs. The generation of a drug target, through virtual screening and computational analysis of databases used for target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. Recent technological advances in human immunology have provided improved tools that allow a better understanding of the biological and molecular mechanisms leading to the protective human immune response to pathogens, inspiring new strategies for vaccine design. Immunoinformatics approaches are more beneficial, and thus there is a demand for modern technologies such as reverse vaccinology, structural vaccinology, and system approaches in developing potential vaccine candidates. System theory, defined as a set of machine learning, control theory, and optimization-based methods applied to networked systems, provides a unifying framework for modeling and analyzing biological complexity. In this review, we explore the application of such computational methods at every stage of the therapeutic pipeline, including lead discovery, optimization, and dosing, as well as vaccine target prediction and immunogen design. Here, we summarize the system theoretic methods which provide insights into developed approaches and their applications in rational drug discovery and vaccine formulations. The approaches ranged in the review yield accurate predictions and insights. This review is intended to serve as a resource for researchers seeking to understand, adopt, or build upon system theoretic techniques in drug and vaccine development, offering both conceptual foundations and practical directions.

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

Computer-aided drug design and discovery methods have been essential in developing small molecules with therapeutic properties over the last decades. Application of computational resources includes drug target identification, hit discovery, and lead optimization. Accordingly, with tremendous research efforts and the availability of financial support from government agencies across the world, and multinational drug companies, the overall research level in this area will continue to advance. The methodology used in this review paper entailed a thorough examination of research studies on relevant literature on drug design and development using computational resources. Extensive searches using Scopus, International Pharmaceutical s (OvidSp, WHO Global Health Library, Cochrane, Google Scholar, Web of Science, Science Direct, ProQuest dissertation & theses, Worldwide Political Science s (CSA), and PubMed was carried out. A standardized template was used to ensure that the selected papers met the inclusion criteria, and relevant to the review. Ultimately, there are robust technologies developed to enhance the drug discovery process. Therefore, this review provides insights into computational resources in Silico and ab initio methods and algorithms, not restricted to drug metabolism predictions for drug design, and the practical applications of artificial intelligence (AI) in drug discovery. Computational tools and methods for drug design and development such as molecular dynamics (MD), molecular docking, quantum mechanics (QM), hybrid quantum mechanics/molecular mechanics (QM/MM), and Density functional theory (DFT) have been reviewed. Accordingly, the emerging technique of synergistically employing these techniques influences the fundamental challenges of conventional medicines for complex diseases. Herein, we discuss ligand-based and structure-based drug discoveries, force field models in MD simulations, docking algorithms, subtractive and additive QM/MM coupling. Nonetheless, as computer-aided drug (CADD) approaches continue to evolve with significant improvements, the focus areas will be on docking and virtual screening, scoring functions, optimization of hits, and assessment of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) properties. With the current success, the present computational resources will aid in the future discovery of novel compounds with high therapeutic performance. The ongoing oncology research efforts will also significantly contribute to UN sustainable development goals – good health and well-being, sustainable innovation and industrialization.