Synopsis of Some Recent Tactical Application of Bioisosteres in Drug Design

Drug design and development are crucial areas of study for chemists and pharmaceutical companies. Nevertheless, the significant expenses, lengthy process, inaccurate delivery, and limited effectiveness present obstacles and barriers that affect the development and exploration of new drugs. Moreover, big and complex datasets from clinical trials, genomics, proteomics, and microarray data also disrupt the drug discovery approach. The integration of Artificial Intelligence (AI) into drug design is both timely and crucial due to several pressing challenges in the pharmaceutical industry, including the escalating costs of drug development, high failure rates in clinical trials, and the increasing complexity of disease biology. AI offers innovative solutions to address these challenges, promising to improve the efficiency, precision, and success rates of drug discovery and development. Artificial intelligence (AI) and machine learning (ML) technology are crucial tools in the field of drug discovery and development. More precisely, the field has been revolutionized by the utilization of deep learning (DL) techniques and artificial neural networks (ANNs). DL algorithms & ML have been employed in drug design using various approaches such as physiochemical activity, polypharmacology, drug repositioning, quantitative structure-activity relationship, pharmacophore modeling, drug monitoring and release, toxicity prediction, ligand-based virtual screening, structure-based virtual screening, and peptide synthesis. The use of DL and AI in this field is supported by historical evidence. Furthermore, management strategies, curation, and unconventional data mining aided assistance in modern modeling algorithms. In summary, the progress made in artificial intelligence and deep learning algorithms offers a promising opportunity for the development and discovery of effective drugs, ultimately leading to significant benefits for humanity. In this review, several tools and algorithmic programs have been discussed which are being used in drug design along with the descriptions of the patents that have been granted for the use of AI in this field, which constitutes the main focus of this review and differentiates it fromalready published materials.

Drug discovery and development is a complex and lengthy enterprise that suffers from high rates of candidate attrition at all stages of the process. The physical, biological, and toxicological properties of a drug candidate are inextricably linked to its structure, and once a molecule has been synthesized, all subsequent studies along the development path are focused only on assessing and understanding its properties in greater detail. Unfortunately, a full prediction of the biological properties of a molecule from an analysis of its 2- or 3-dimensional structure is currently beyond our expertise. This backdrop mandates that considerable care be taken at the design stage if a molecule is to be successful in testing a mechanistic concept underlying a disease process and to progress into late stage clinical trials and, ultimately, marketing approval. While there are multiple potential causes of candidate attrition, an introspective analysis of drug design practices over the past decade has focused attention on the perception that contemporary molecules are unnecessarily obese, burdened by high molecular weight and excessive lipophilicity. This practice is believed to have its roots in the singular pursuit of enhancing potency during lead optimization rather than adopting a more holistic approach to drug design that gives broader consideration to how structural features affect developability properties. In an effort to provide the medicinal chemistry community with practical guideposts to enhancing compound quality in the drug design phase and which can readily be applied, a series of efficiency indices have been proposed that attempt to define aspects of compound quality in the context of a series of physicochemical parameters. Of these metrics, lipophilic ligand efficiency (LLE or LipE), which provides an index of the dependence of the potency of a molecule on its intrinsic lipophilicity, has been characterized as the most robust metric that has potential for broad-based application. In this review, after describing the background literature behind the derivation of efficiency metrics and approaches to assessing compound aesthetics, synopses of some recent practical application in lead optimization campaigns are presented. However, molecules that fall into space beyond that associated with traditional drug-like properties are an important part of the current and future landscape, exemplified by the summary of direct acting hepatitis C virus NS3 and NS5A inhibitors that have transformed clinical therapy for this chronic disease. While drug development in nontraditional drug-like space is more challenging and the rules for compound quality will be different with much still to be understood, careful and disciplined drug design practices will be an essential element of success.

Recent advances and application of generative adversarial networks in drug discovery, development, and targeting

Phage display is a molecular diversity technology that allows the presentation of large peptide and protein libraries on the surface of filamentous phage.Phage display libraries permit the selection of peptides and proteins,including antibodies,with high affinity and specificity for almost any target.In recent years,along with phage display technology maturated,it has been widely used in screening tumor antibody library,the preparation of monoclonal antibody,polypeptide,drug design,gene therapy and cell signal transduction.This review serves as an introduction to phage display in molecular nuclear medicine,and recent application in display technology. Key words: Radionuclide imaging; Radioactive tracers; Phage display

In this review we describe progress in docking and especially high-throughput docking (HTD) for applications in drug design and in silico screening. Computational methods that are used in HTD to assist drug design involve two steps: docking and scoring. Several current docking programs have the ability to generate protein-ligand configurations that are close to the correct structure, as revealed by X-ray crystallography, in many cases. Recent comparison studies of docking and scoring methods have shown that the choice of the best docking (and scoring) tool is to a large extent target-dependent. Most of the docking programs treat the ligand as flexible, but the protein conformation is kept rigid. We review algorithmic advances that allow for partial treatment of protein flexibility. The estimation of binding affinities (scoring) is, from a theoretical point of view, the most challenging part of ligand design. Despite significant progress, a fast and accurate computational prediction of binding affinities is still beyond the limits of current methods. We discuss multivariate statistical methods that have been proposed recently for improving HTD results and briefly outline simplified free energy calculations based on molecular dynamics simulations. Recent applications, in particular those based on end-point free energy models, are re-establishing the interest in this approach and we present an example from our research work. Finally, we discuss new application scenarios of HTD, including chemogenomics docking on entire protein families and docking of natural products. Keywords: Virtual screening, docking algorithms, scoring functions, protein flexibility, molecular dynamics, natural products

Implementation of novel and biocompatible polymers in drug design is an emerging and rapidly growing area of research. Even though we have a large number of polymer materials for various applications, the biocompatibility of these materials remains as a herculean task for researchers. Aptamers provide a vital and efficient solution to this problem. They are usually small (ranging from 20 to 60 nucleotides, single-stranded DNA or RNA oligonucleotides which are capable of binding to molecules possessing high affinity and other properties like specificity. This review focuses on different aspects of Aptamers in drug discovery, starting from its preparation methods and covering the recent scenario reported in the literature regarding their use in drug discovery. We address the limitations of Aptamers and provide valuable insights into their future potential in the areas regarding drug discovery research. Finally, we explained the major role of Aptamers like medical imaging techniques, application as synthetic antibodies, and the most recent application, which is in combination with nanomedicines.

Theoretical modeling has evolved into a very important tool in biochemistry and biophysics and ranges from elementary modeling of quantitative structure–activity relationships, electrostatic maps, or docking to classical molecular dynamics simulations or sophisticated quantum chemical treatments. Theoretical biochemists have over the years successfully addressed a wide variety of topics, such as enzymatic reactions, drug design, DNA structure and function, biomimetics, and of course the recent applications in the area of bioinformatics. The field, both in terms of areas of applications as well as new or combined methodologies, grows rapidly, and new developments are being made in a seemingly endless flow. In March 2003, a symposium was arranged in the field of Theoretical Biophysics, at the Donostia International Physics Center, Euskal Herriko Unibertsitatea, in the beautiful city of Donostia/San Sebastian, in Northern Spain. The aim of the symposium was not only to present explicit results from recent modeling studies but also to allow for presentations of novel methodologies and to do so in a setting that allowed for plentiful discussions and exchange of knowledge and ideas. To this end, it was decided to significantly limit the number of participants, to allow for a more intimate setting. The symposium thus brought together 45 participants, ranging from senior researchers to first-year PhD students, representing a total of 18 universities from seven countries. Several of the presentations in the more applied areas were devoted to enzyme catalysis, such as radical enzymes, peptide bond cleavage, biochemical Claisen rearrangements, and phosphate ester hydrolysis. We also saw several contributions on drug design. Alzheimer's disease, antimetastatic and anticancer drugs, antibiotics, and antioxidants were among the topics covered in the presentations. Other applications dealt with modeling of ion channels or reactions in interstellar clouds leading to organic and prebiotic molecules. In the more methodology-oriented parts, much emphasis was put on new quantum mechanics/molecular mechanics hybrid methods or quantum chemically based dynamics simulations. Also, other recent developments, such as time-dependent density functional theory for excited state properties and dynamics, new molecular dynamics simulations techniques, and new approaches in theoretical electrochemistry were covered. The meeting generated much discussion, as well as a splendid array of social activities: in all, a most successful event, providing valuable new insights in an informal setting. It is our hope that the articles presented in the symposium proceedings will give an indication of some of the excellent science being conducted in the area of theoretical biophysics.

In this fast-growing era, high throughput data is now being easily accessed by getting transformed into datasets which store the information. Such information is valuable to optimize the hypothesis and drug design via computer-aided drug design (CADD). Nowadays, we can explore the role of CADD in various disciplines like Nanotechnology, Biochemistry, Medical Sciences, Molecular Biology, etc. Methods: We searched the valuable literature using a pertinent database with given keywords like computer-aided drug design, anti-diabetic, drug design, etc. We retrieved all valuable articles which are recent and discussing the role of computation in the designing of anti-diabetic agents. To facilitate the drug discovery process, the computational approach has set landmarks in the whole pipeline for drug discovery from target identification and mechanism of action to the identification of leads and drug candidates. Along with this, there is a determined endeavor to describe the significance of in-silico studies in predicting the absorption, distribution, metabolism, excretion, and toxicity profile. Thus, globally, CADD is accepted with a variety of tools for studying QSAR, virtual screening, protein structure prediction, quantum chemistry, material design, physical and biological property prediction. Computer-assisted tools are used as the drug discovery tool in the area of different diseases, and here we reviewed the collaborative aspects of information technologies and chemoinformatic tools in the discovery of anti-diabetic agents, keeping in view the growing importance for treating diabetes.

Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?

Recent developments and applications in theoretical methods focusing on drug design and particularly on the solvent effect in molecular recognition based on the three-dimensional reference interaction site model (3D-RISM) theory are reviewed. Molecular recognition, a fundamental molecular process in living systems, is known to be the functional mechanism of most drugs. Solvents play an essential role in molecular recognition processes as well as in ligand-protein interactions. The 3D-RISM theory is derived from the fundamental statistical mechanics theory, which reproduces all solvation thermodynamics naturally and has some advantages over conventional solvation methods, such as molecular simulation and the continuum model. Here, we review the basics of the 3D-RISM theory and methods of molecular recognition in its applications toward drug design.

The adventures of a rogue cell.

We present a comprehensive but not exhaustive review of new trends in drug discovery and targets for cancer and AIDS which begins by highlighting the different historical stages of drug discovery from clinical diagnosis, natural compounds, serendipity and chemotherapy to the present age of modern experimental and computational techniques. The current state of the art of computer-aided drug design and selected recent applications to Cancer and AIDS are discussed. Novel targets and drugs in Cancer and AIDS are analyzed. In conclusion, future perspectives for drug discovery, design and therapeutics in cancer and AIDS are presented. Keywords: HIV-1, non-nucleoside reverse transcriptase inhibitors (NNRTIs), chemokine co-receptors, anti-VEGF antibody, molecular modeling (MM)

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

The benzene moiety is the most prevalent ring system in marketed drugs, underscoring its historic popularity in drug design either as a pharmacophore or as a scaffold that projects pharmacophoric elements. However, introspective analyses of medicinal chemistry practices at the beginning of the 21st century highlighted the indiscriminate deployment of phenyl rings as an important contributor to the poor physicochemical properties of advanced molecules, which limited their prospects of being developed into effective drugs. This Perspective deliberates on the design and applications of bioisosteric replacements for a phenyl ring that have provided practical solutions to a range of developability problems frequently encountered in lead optimization campaigns. While the effect of phenyl ring replacements on compound properties is contextual in nature, bioisosteric substitution can lead to enhanced potency, solubility, and metabolic stability while reducing lipophilicity, plasma protein binding, phospholipidosis potential, and inhibition of cytochrome P450 enzymes and the hERG channel.

COVID-19 is a rampant worldwide problem. It is caused by the SARS-CoV-2 virus and is manifest in different variants. The Delta variant compromised existing therapeutic and preventive options for this disease and is beginning to be replaced by the Omicron variant. Through pharmaceutical biotechnology, three different treatment approaches to COVID-19 have been developed: computer-aided drug design (CADD); rational drug design in the wet lab; and the advanced drug delivery system. These approaches are heavily influenced by advances in life sciences, such as the development of structural bioinformatics, the establishment of nanobiotechnology as a standard approach in drug design, and major advances in structural biology such as the development of the CryoEM method. This book will focus on providing possible solutions to the ongoing COVID-19 pandemic in light of these advances in life sciences.