Computer-aided Drug Design Methods Research Articles

Discovery of drug lead compounds for Anti-Alzheimer's disease on the basis of synaptic plasticity

Alzheimer's disease (AD) is a progressive neurodegenerative disease commonly seen in the middle-aged and the elder. Its clinical presentations are mainly memory impairment and cognitive impairment. Its cardinal pathological features are the deposition of extracellular Amyloid-β (Aβ), intracellular neurofibrillary tangles and synaptic dysfunction. The etiology of AD is complex and the pathogenesis remains unclear. Having AD would lead to awful living experience of it's patients, which may be a burden to the patient even to the public health care system. However, there are no certain cure for AD. Thus it's significant for both medical value and social meaning to find the way to cure or prevent AD and to research on the pathogenesis of AD. In this work, the molecular docking technology, pharmacokinetic analysis and pharmacological experiments were employed to analyse the natural active compounds and the mechanisms against AD based on the synaptic plasticity. A total of seven target proteins related to the synaptic plasticity and 44 natural active compounds with potential to enhance the synaptic plasticity were obtained through a literature review and network pharmacological analysis. Computer-Aided Drug Design (CADD) method was used to dock the anti-AD key target proteins with the 44 compounds. The compounds with good binding effect were screened. Three anti-AD active compounds based on the synaptic plasticity were obtained, including Curcumin, Withaferin A and Withanolide A. In addition, pharmacological experiments were carried out on Withaferin A and Withanolide A based on its good docking results. The experimental results showed that Withaferin A has good anti-AD potential and great potential to enhance synaptic plasticity. The anti-AD effect can be achieved through a multi-target synergistic mechanism.

Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.

The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.

Evaluation of Receptor Relationships of Some Drugs Used in the Treatment of COVID-19 by Modeling Studies

Objective: It is important to investigate the interactions of drugs used in the treatment process of COVID-19 with cellular mechanisms. In this study, the aim was to investigate the interactions of Dexamethasone, Favipiravir, and Hydroxychloroquine drugs used in the treatment of COVID-19 with the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor-2 (HER2). Materials and Methods: Within the scope of the study, firstly, 3-dimensional structures of receptors and drug molecules were formed. Then the interactions of each of the receptor and drug molecules at the binding site were examined by molecular docking studies, which is a computer-aided drug design method, and their binding affinities were evaluated. Results: As a result of the analyses, it was determined that the drug named Hydroxychloroquine has the highest and the drug called Dexamethasone has the lowest binding affinity for all three receptors. In addition, it has been determined that Dexamethasone develops inappropriate interactions with ER and HER2 receptor active site amino acids. Conclusions: In this study, preliminary data on how receptor interactions can occur when normal individuals and breast cancer patients use Dexamethasone, Favipiravir, and Hydroxychloroquine are presented.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development.

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Rational design of stapled antimicrobial peptides.

The global increase in antimicrobial drug resistance has dramatically reduced the effectiveness of traditional antibiotics. Structurally diverse antibiotics are urgently needed to combat multiple-resistant bacterial infections. As part of innate immunity, antimicrobial peptides have been recognized as the most promising candidates because they comprise diverse sequences and mechanisms of action and have a relatively low induction rate of resistance. However, because of their low chemical stability, susceptibility to proteases, and high hemolytic effect, their usage is subject to many restrictions. Chemical modifications such as D-amino acid substitution, cyclization, and unnatural amino acid modification have been used to improve the stability of antimicrobial peptides for decades. Among them, a side-chain covalent bridge modification, the so-called stapled peptide, has attracted much attention. The stapled side-chain bridge stabilizes the secondary structure, induces protease resistance, and increases cell penetration and biological activity. Recent progress in computer-aided drug design and artificial intelligence methods has also been used in the design of stapled antimicrobial peptides and has led to the successful discovery of many prospective peptides. This article reviews the possible structure-activity relationships of stapled antimicrobial peptides, the physicochemical properties that influence their activity (such as net charge, hydrophobicity, helicity, and dipole moment), and computer-aided methods of stapled peptide design. Antimicrobial peptides under clinical trial: Pexiganan (NCT01594762, 2012-05-07). Omiganan (NCT02576847, 2015-10-13).

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

HDX-MS guided ensemble reweighting approach reveals cryptic drug binding sites in the cytoplasmic heme binding protein, PhuS.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) is a valuable approach to probe protein structure and dynamics. However, interpretation of HDX-MS data is often qualitative and typically limited to peptide-level resolution. Ongoing efforts aim to integrate experimental HDX-MS data with molecular dynamics (MD) approaches, to provide a high-resolution interpretation of the HDX-MS data. One such approach is an HDX-MS based maximum-entropy reweighting approach (HDXer) to reweight computationally generated ensembles to HDX-MS data. Here, HDXer is used in conjunction with enhanced MD (eMD) to characterize the structural dynamics of PhuS. PhuS is a cytoplasmic heme binding protein from P. aeruginosa which shuttles exogenous heme to Heme Oxygenase (HemO) for degradation and is essential for virulence. Through the implementation of a workflow that integrates eMD, HDXer, and dimensional reduction, we had previously modeled the native state ensemble of apo-PhuS revealing a locally diverse apo-PhuS ensemble characterized by large rearrangements of helices surrounding the heme binding. Here, the representative structures from the modeled native ensemble were used as starting points for computer aided drug design (CADD). Implementing the Site Identification by Ligand Competitive Saturation (SILCS) CADD method approximately 1.3 million compounds were screened for each representative structure as well as the published apo PhuS crystal structure, yielding a library of 50 chemically representative candidate compounds. The candidate compounds were then screened using a fluorescence quenching assay. From these assays, several hits were confirmed to bind apo PhuS at nanomolar binding affinities. Most significantly, none of the compounds were derived from CADD using the published apo PhuS crystal structure, demonstrating the utility of HDX-MS guiding ensemble reweighting in revealing cryptic drug binding site in CADD approaches.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

Review of Modern Computer-aided Drug Design Methods

Computer technology has developed rapidly in recent decades, and it is also widely used in the field of drug research and development. Computer-aided drug design (CADD) has appeared in the form of assistance to drug discovery process in this background. Computer-aided drug design can save time which is spent in the experimental process in the real world. Since appearance of computer-based drug design strategies, the concepts of HTS, structure-based and ligand-based drug design (SBDD and LBDD), and virtual screening (VS) have been proposed. These technologies have their own advantages and disadvantages, and have different scope of application. This review provides an introduction of modern drug design strategies which are based on computer technology, classifies different methods and finds out the basic working principle of each one, the applicability and limitations of these methods are discussed and recommendations are provided in the application of each method.

Computer-Aided Drug Design: An Update.

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

Abstract 13724: Development of VS-041 as a Precision Medicine Targeting Cardiac-Relevant Matrix Metalloproteases (MMPs) in Heart Failure With Preserved Ejection Fraction (HFpEF)

Introduction. HFpEF is a heterogeneous syndrome, with several underlying etiologic and pathophysiologic factors. As “one size fits all” therapies are not equally efficacious across various HFpEF comorbidities, a precision medicine approach can offer a disease modifying treatment for HFpEF subpopulation stratified for specific molecular targets. Expression of MMP2, 9, and 13 in heart is elevated in HFpEF patients with hypertension, diabetes or coronary disease, and their levels in plasma correlate with survival, suggesting utility as predictive biomarkers. Genetic ablation of MMP2, 9, and 13 leads to a robust antifibrotic effect in the myocardium. Inhibition of MMP2, 9, and 13 directly regulates of Ca 2+ transients and protects sarcomere function. Hypothesis. We present preclinical profile of VS-041, a novel and narrow-spectrum MMP inhibitor, as a drug candidate for HFpEF patients with elevated levels of MMP2, 9, and 13. Methods and Results. VS-041, optimized with computer-aided drug design methods, shows single-digit IC 50 against MMP2 (1nM), MMP9 (2nM) and MMP13 (1nM), and maintains >300-fold selectivity against MMP1 and TACE. In isolated cardiomyocytes VS-041 reduces contractility deficit due to fast pacing. With high predicted metabolic bioavailability (MF%=90), >80% target occupancy by VS-041 is estimated after 50 mg/kg PO dose in rats. VS-041 demonstrated robust efficacy in Dahl Salt Sensitive rat model of hypertension and HFpEF after dosing at 25 or 50 mg/kg PO for 5 wks in a therapeutic mode (after 1-wk of high salt lead-in). VS-041 had no effect on blood pressure or ejection fraction confirming a direct effect on diastolic function. In Doppler mitral inflow, analysis of the isovolumic relaxation time and the E/A ratio of peak velocity of filling waves revealed 40% improvement in diastolic function. LV fibrosis was dose-proportionally decreased based on PSR morphometry. No adverse effects were observed after 5 weeks of dosing. Conclusions. Preclinical pharmacology and safety profile of VS-041 support its nomination as clinical candidate and initiation of IND-enabling studies. VS-041 has potential to be first-in-class precision medicine treatment for HFPEF patients with elevated levels of cardiac-relevant MMP2, 9 and 13.

Deep learning approaches for <italic>de novo</italic> drug design: an overview

<p indent="0mm">The design and discovery of lead compounds is the most challenging and creative stage in drug development, and multiple attributes of candidate molecules need to be optimized in this process, such as structural novelty, biological activity, target selectivity, synthesability, druggability, and safety. Although the development and application of computer-aided drug design methods have substantially reduced the time and cost in the lead compound discovery stage, it has not yet been able to reverse the current situation of low success rate of drug development. In recent years, with the continuous development of deep learning (DL) technology, DL-based <italic>de novo</italic> drug design methods have brought new opportunities for the discovery of lead compounds. The DL frameworks used by these new drug design models include encoder-decoder, recurrent neural network, generative adversarial networks, and reinforcement learning. In this paper, the basic theory, the input molecular representation and the evaluation metrics of their methods are reviewed, and the application prospect of DL-based <italic>de novo</italic> drug design methods is also discussed.

In Silico Strategies for Designing of Peptide Inhibitors of Oncogenic K-Ras G12V Mutant: Inhibiting Cancer Growth and Proliferation.

Simple SummaryThe most well-known oncogene and one with the highest rates of mutation across all cancers is K-Ras, also known as the Kirsten rat sarcoma viral oncogene homologue. It is also linked to certain cancers with a high mortality rate, including pancreatic ductal adenocarcinoma (PDAC), non-small-cell lung cancer (NSCLC), and colorectal cancer (CRC). The aim of this study was to make peptides that inhibit K-Ras G12V by computer-aided drug design methods. Our results showed that the top four selected peptides interact with K-Ras more strongly than the reference-peptide and can stop K-Ras from activating. Our binding affinity analyses demonstrated that the developed peptides can inhibit K-Ras and slow cancer growth.Ras plays a pivotal function in cell proliferation and is an important protein in signal transduction pathways. Mutations in genes encoding the Ras protein drive the signaling cascades essential for malignant transformation, tumour angiogenesis, and metastasis and are responsible for above 30% of all human cancers. There is evidence that N-Ras, K-Ras, and H-Ras play significant roles in human cancer. The mutated K-Ras protein is typically observed in malignant growths. Mutant K-Ras is the most common in lung, colon, and pancreatic cancers. The purpose of this research was to create peptides that inhibit K-Ras G12V. The crystal structure of the mutant K-Ras G12V-H-REV107 complex was obtained from a protein data bank. Further, we used a residue scan approach to create unique peptides from the reference peptide (H-REV107). AMBER molecular dynamics simulations were used to test the stability of the top four proposed peptides (based on binding free energies). Our findings showed that the top four selected peptides had stronger interactions with K-Ras than the reference peptide and have the ability to block the activation function of K-Ras. Our extensive analyses of binding affinities showed that our designed peptide possesses the potential to inhibit K-Ras and to reduce the progression of cancer.

Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations.

S-adenosyl-L-homocysteine hydrolase (SAHase) is an important regulator in the methylation reactions in many organisms and thus is crucial for numerous cellular functions. In recent years, SAHase has become one of the popular targets for drug design, and SAHase inhibitors have exhibited potent antiviral activity. In this study, we established the complex-based pharmacophore models based on the known crystal complex of SAHase (PDB ID: 1A7A) to screen the drug-likeness compounds of ChEMBL database. Then, three molecular docking programs were used to validate the reliability of compounds, involving Libdock, CDOCKER, and AutoDock Vina programs. The four promising hit compounds (CHEMBL420751, CHEMBL346387, CHEMBL1569958, and CHEMBL4206648) were performed molecular dynamics simulations and MM-PBSA calculations to evaluate their stability and binding-free energy in the binding site of SAHase. After screening and analyzing, the hit compounds CHEMBL420751 and CHEMBL346387 were suggested to further research to obtain novel potential SAHase inhibitors. A series of computer-aided drug design methods, including pharmacophore, molecular docking, molecular dynamics simulation and MM-PBSA calculations, were employed in this study to identity novel inhibitors of S-adenosyl-L-homocysteine hydrolase (SAHase). Some compounds from virtual screening could form various interactions with key residues of SAHase. Among them, compounds CHEMBL346387 and CHEMBL420751 exhibited potent binding affinity from molecular docking and MM-PBSA, and maintained good stability at the binding site during molecular dynamics simulations as well. All these results indicated that the selected compounds might have the potential to be novel SAHase inhibitors.

Discovery of Novel c-Jun N-Terminal Kinase 1 Inhibitors from Natural Products: Integrating Artificial Intelligence with Structure-Based Virtual Screening and Biological Evaluation.

c-Jun N-terminal kinase 1 (JNK1) is currently considered a critical therapeutic target for type-2 diabetes. In recent years, there has been a great interest in naturopathic molecules, and the discovery of active ingredients from natural products for specific targets has received increasing attention. Based on the above background, this research aims to combine emerging Artificial Intelligence technologies with traditional Computer-Aided Drug Design methods to find natural products with JNK1 inhibitory activity. First, we constructed three machine learning models (Support Vector Machine, Random Forest, and Artificial Neural Network) and performed model fusion based on Voting and Stacking strategies. The integrated models with better performance (AUC of 0.906 and 0.908, respectively) were then employed for the virtual screening of 4112 natural products in the ZINC database. After further drug-likeness filtering, we calculated the binding free energy of 22 screened compounds using molecular docking and performed a consensus analysis of the two methodologies. Subsequently, we identified the three most promising candidates (Lariciresinol, Tricin, and 4′-Demethylepipodophyllotoxin) according to the obtained probability values and relevant reports, while their binding characteristics were preliminarily explored by molecular dynamics simulations. Finally, we performed in vitro biological validation of these three compounds, and the results showed that Tricin exhibited an acceptable inhibitory activity against JNK1 (IC50 = 17.68 μM). This natural product can be used as a template molecule for the design of novel JNK1 inhibitors.

Computer-Aided Drug Design Boosts RAS Inhibitor Discovery.

The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.

SILCS-RNA: Toward a Structure-Based Drug Design Approach for Targeting RNAs with Small Molecules.

RNA molecules can act as potential drug targets in different diseases, as their dysregulated expression or misfolding can alter various cellular processes. Noncoding RNAs account for ∼70% of the human genome, and these molecules can have complex tertiary structures that present a great opportunity for targeting by small molecules. In the present study, the site identification by ligand competitive saturation (SILCS) computational approach is extended to target RNA, termed SILCS-RNA. Extensions to the method include an enhanced oscillating excess chemical potential protocol for the grand canonical Monte Carlo calculations and individual simulations of the neutral and charged solutes from which the SILCS functional group affinity maps (FragMaps) are calculated for subsequent binding site identification and docking calculations. The method is developed and evaluated against seven RNA targets and their reported small molecule ligands. SILCS-RNA provides a detailed characterization of the functional group affinity pattern in the small molecule binding sites, recapitulating the types of functional groups present in the ligands. The developed method is also shown to be useful for identification of new potential binding sites and identifying ligand moieties that contribute to binding, granular information that can facilitate ligand design. However, limitations in the method are evident including the ability to map the regions of binding sites occupied by ligand phosphate moieties and to fully account for the wide range of conformational heterogeneity in RNA associated with binding of different small molecules, emphasizing inherent challenges associated with applying computer-aided drug design methods to RNA. While limitations are present, the current study indicates how the SILCS-RNA approach may enhance drug discovery efforts targeting RNAs with small molecules.

In-silico Homology Modeling and Inhibitors Identification of Breast Cancer

Abstract: Azadirachta Indica (Neem) is the first introduced medicinal plant in ancient medicine system, used for various disease treatments and household remedies. Lots of phytochemicals have been isolated from the various parts of the tree to study their biological activities. Gedunin has shown anticancer activity and act as Heat shock protein 90 (Hsp90) inhibitor. Breast cancer one of the most common cancer which mainly develops from lobules or ducts. Triple-negative Breast Cancer (TNBC) is an aggressive type of breast cancer in which cancer cells does not have hormone receptors. Due to lack of hormone receptors many targeted therapies were developed for advance stage cancer. The interaction between drugs and protein were examined using spectroscopic techniques, to determine the drug stability and binding site of drug in protein. Computer-aided drug design method were used to develop and analyse the drugs. Interaction between protein and drugs visualize by using molecular graphic viewer tools, to identify protein conformation and binding energy of ligand and protein.

Allostery Inhibition of BACE1 by Psychotic and Meroterpenoid Drugs in Alzheimer's Disease Therapy.

In over a century since its discovery, Alzheimer’s disease (AD) has continued to be a global health concern due to its incurable nature and overwhelming increase among older people. In this paper, we give an overview of the efforts of researchers towards identifying potent BACE1 exosite-binding antibodies and allosteric inhibitors. Herein, we apply computer-aided drug design (CADD) methods to unravel the interactions of some proposed psychotic and meroterpenoid BACE1 allosteric site inhibitors. This study is aimed at validating the allosteric potentials of these selected compounds targeted at BACE1 inhibition. Molecular docking, molecular dynamic (MD) simulations, and post-MD analyses are carried out on these selected compounds, which have been experimentally proven to exhibit allosteric inhibition on BACE1. The SwissDock software enabled us to identify more than five druggable pockets on the BACE1 structural surface using docking. Besides the active site region, a melatonin derivative (compound 1) previously proposed as a BACE1 allostery inhibitor showed appreciable stability at eight different subsites on BACE1. Refinement with molecular dynamic (MD) simulations shows that the identified non-catalytic sites are potential allostery sites for compound 1. The allostery and binding mechanism of the selected potent inhibitors show that the smaller the molecule, the easier the attachment to several enzyme regions. This finding hereby establishes that most of these selected compounds failed to exhibit strong allosteric binding with BACE1 except for compound 1. We hereby suggest that further studies and additional identification/validation of other BACE1 allosteric compounds be done. Furthermore, this additional allosteric site investigation will help in reducing the associated challenges with designing BACE1 inhibitors while exploring the opportunities in the design of allosteric BACE1 inhibitors.

A review of approaches in computer-aided drug design in drug discovery

The process of discovering and developing a new medication is often seen as a lengthy and expensive endeavors. As a result, computer-aided drug design methods are now frequently utilized to improve the efficiency of the drug discovery and development process. Various CADD approaches are regarded as potential techniques based on their needs; nevertheless, structure-based drug design and ligand-based drug design approaches are well-known as highly efficient and powerful strategies in drug discovery and development. Both of these approaches may be used in conjunction with molecular docking to conduct virtual screening for the purpose of identifying and optimizing leads. In recent years, computational tools have become increasingly popular in the pharmaceutical industry and academic fields as a means of improving the efficiency and effectiveness of the drug discovery and development pipeline. In this post, we'll go over computational methods, which are a creative way of discovering new leads and assisting in drug discovery and development research.