Aid Drug Discovery Research Articles

Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker.

Small molecules are usually compared by their chemical structure, but there is no unified analytic framework for representing and comparing their biological activity. We present the Chemical Checker (CC), which provides processed, harmonized and integrated bioactivity data on ~800,000 small molecules. The CC divides data into five levels of increasing complexity, from the chemical properties of compounds to their clinical outcomes. In between, it includes targets, off-targets, networks and cell-level information, such as omics data, growth inhibition and morphology. Bioactivity data are expressed in a vector format, extending the concept of chemical similarity to similarity between bioactivity signatures. We show how CC signatures can aid drug discovery tasks, including target identification and library characterization. We also demonstrate the discovery of compounds that reverse and mimic biological signatures of disease models and genetic perturbations in cases that could not be addressed using chemical information alone. Overall, the CC signatures facilitate the conversion of bioactivity data to a format that is readily amenable to machine learning methods.

A comprehensive automated computer-aided discovery pipeline from genomes to hit molecules

Big data generation through sequencing of genomes and proteomes has led to over thousands of whole genomes and millions of protein sequences. However, utilization of this data to generate drug-like molecules for curing diseases remains a challenge. We propose here, Dhanvantari, a comprehensive software suite which automates the journey from genomes to hit molecules via its various modules such as i) gene finding, ii) computational structural study of target proteins and iii) virtual screening/identification of hit molecules for computer aided drug discovery. The pipeline has five possible entry points. Validation of the protocol is performed on 10 major life-threatening diseases reported by WHO covering 33 different protein targets and 111 FDA approved drugs. Three complete case studies from genomes to hits are also performed using the pipeline where the reported FDA drugs against the pathogen have been successfully recovered. The proposed software suite promises to deliver some new opportunities and insights into “genome-based” drug discovery along with the classical structure-based approaches to discover novel potential drug-like molecules. The entire protocol requires ~6–12 h. Individual steps, however, get completed within a few minutes. The pipeline can be freely accessed at http://www.scfbio-iitd.res.in/software/dhanvantari_new/Home.html.

Exploring the computational methods for protein-ligand binding site prediction

Proteins participate in various essential processes in vivo via interactions with other molecules. Identifying the residues participating in these interactions not only provides biological insights for protein function studies but also has great significance for drug discoveries. Therefore, predicting protein–ligand binding sites has long been under intense research in the fields of bioinformatics and computer aided drug discovery. In this review, we first introduce the research background of predicting protein–ligand binding sites and then classify the methods into four categories, namely, 3D structure-based, template similarity-based, traditional machine learning-based and deep learning-based methods. We describe representative algorithms in each category and elaborate on machine learning and deep learning-based prediction methods in more detail. Finally, we discuss the trends and challenges of the current research such as molecular dynamics simulation based cryptic binding sites prediction, and highlight prospective directions for the near future.

Current Computational Approaches for the Development of Anti-HIV Inhibitors: An Overview.

Today, HIV-1 infection has become an extensive problem to public health and a greater challenge to all working researchers throughout the world. Since the beginning of HIV-1 virus, several antiviral therapeutic agents have been developed at various stages to combat HIV-1 infection. But, many of antiviral drugs are on the platform of drug resistance and toxicology issues, needs an urgent constructive investigation for the development of productive and protective therapeutics to make an improvement of individual life suffering with viral infection. As developing a novel agent is very costly, challenging and time taking route in the recent times. The review summarized about the modern approaches of computational aided drug discovery to developing a novel inhibitor within a short period of time and less cost. The outcome suggests on the premise of reported information that the computational drug discovery is a powerful technology to design a defensive and fruitful therapeutic agents to combat HIV-1 infection and recover the lifespan of suffering one. Based on survey of the reported information, we concluded that the current computational approaches is highly supportive in the progress of drug discovery and controlling the viral infection.

Identification of Potential Inhibitors of Cathepsin-B using Shape & Pharmacophore-based Virtual Screening, Molecular Docking and Explicit Water Thermodynamics.

Lysosome has been long understood as a vital digestive organelle. Increasing reports indicate that the lysosome also plays a crucial role in the pathogenesis of a variety of neurodegenerative diseases, including Huntington's disease, Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. Abnormal protein degradation and deposition stimulated by lysosomal dysfunction may cause age-related neurodegeneration. Enormous efforts have been devoted to the development of effective therapeutics against Alzheimer's disease, the most debilitating neurodegenerative disease. Endopeptidase activity of the Cathepsin-B is associated with the pathological processes. Work presented here focuses on identification of new inhibitors against Cathepsin-B protein using diverse computational approaches together. The inhibitors identified were further tested for in-vitro activity using enzyme based assay method. The identified inhibitors provided interesting understanding on how the water thermodynamic properties along with hydrophobic, steric, electronic, and structural requirements contribute to cathepsin-B inhibitory activity. These water thermodynamic studies, may further be used in computer aided drug discovery pipeline to design and predict more potent derivatives of various scaffolds as cathepsin-B inhibitors.

Predicting mutations deleterious to function in beta-lactamase TEM1 using MM-GBSA.

Missense mutations can have disastrous effects on the function of a protein. And as a result, they have been implicated in numerous diseases. However, the majority of missense variants only have a nominal impact on protein function. Thus, the ability to distinguish these two classes of missense mutations would greatly aid drug discovery efforts in target identification and validation as well as medical diagnosis. Monitoring the co-occurrence of a given missense mutation and a disease phenotype provides a pathway for classifying functionally disrupting missense mutations. But, the occurrence of a specific missense variant is often extremely rare making statistical links challenging to infer. In this study, we benchmark a physics-based approach for predicting changes in stability, MM-GBSA, and apply it to classifying mutations as functionally disrupting. A large and diverse dataset of 990 residue mutations in beta-lactamase TEM1 is used to assess performance as it is rich in both functionally disrupting mutations and functionally neutral/beneficial mutations. On this dataset, we compare the performance of MM-GBSA to alternative strategies for predicting functionally disrupting mutations. We observe that the MM-GBSA method obtains an area under the curve (AUC) of 0.75 on the entire dataset, outperforming all other predictors tested. More importantly, MM-GBSA’s performance is robust to various divisions of the dataset, speaking to the generality of the approach. Though there is one notable exception: Mutations on the surface of the protein are the mutations that are the most difficult to classify as functionally disrupting for all methods tested. This is likely due to the many mechanisms available to surface mutations to disrupt function, and thus provides a direction of focus for future studies.

Structural Genomics in Drug Discovery: An Overview

Structural genomics, technology and methodology driven field has emerged in the past 20–25 years and mainly aims at coming up with as many three-dimensional structures of proteins as possible by combining experimental (X-ray crystallography and nuclear magnetic resonance) and comparative modeling methods at a minimal cost in comparison to the traditional approaches. Structure aided drug discovery which is used since long mainly focuses on a single target protein whereas structural genomics targets and investigates multiple proteins simultaneously, thus cutting down time and cost. These activities are done at specialized structural genomic centers available worldwide that help to interpret the biology of humans and pathogenic organisms in a better way leading to an increased productivity and saving time. Virtual screening methods like docking have also brought a great impact to quicken the drug discovery process. In diseases of public health concern (like tuberculosis), structural genomics helped greatly by giving important insights toward a better comprehension of the pathways associated in its pathogenesis and in guiding drug discovery. Several newer targets such as Scaff10-8, MS2734 (6), and GSK-J4 were developed showcasing their beneficial effects in a wide variety of disorders including diabetes insipidus, Parkinson's disease, and autoimmune diseases, respectively. Furthermore, these programs guide to understand the root cause and the mechanism associated with various human diseases and their pathogens. This review tries to look into the high throughput methods of structure determination, the newer targets developed in the past few years and the benefits offered that can pave a way for faster and efficient drug discovery.

Toward a global picture of bacterial secondary metabolism.

Bacterial metabolism is comprised of primary metabolites, the intracellular molecules of life that enable growth and proliferation, and secondary metabolites, predominantly extracellular molecules that facilitate a microbe's interaction with its environment. While our knowledge of primary metabolism and its web of interconnected intermediates is quantitative and holistic, significant knowledge gaps remain in our understanding of the secondary metabolomes of bacteria. In this Perspective, I discuss the main challenges involved in obtaining a global, comprehensive picture of bacterial secondary metabolomes, specifically in biosynthetically "gifted" microbes. Recent methodological advances that can meet these challenges will be reviewed. Applications of these methods combined with ongoing innovations will enable a detailed picture of global secondary metabolomes, which will in turn shed light onto the biology, chemistry, and enzymology underlying natural products and simultaneously aid drug discovery.

A Comparative Analysis of the Molecular Interaction Techniques for In Silico Drug Design

Computer Aided Drug Discovery (CADD) has become a vital tool for the rapid identification of new candidate molecules prior to their chemical synthesis and screening. For precarious lifestyle diseases such as various types of carcinoma, CADD offers a fast and cost-effective approach for development of therapeutic drugs. Advancement in the molecular modelling systems enabled easier identification of the target, along with visualisation and interpretation of target-ligand interactions. This review highlights in silico drug design and development strategies with detailed description of the various approaches like classical variable selection and artificial intelligence which are used to select molecular descriptors. This study also critically evaluates the different molecular docking approaches like SLIDE, GLIDE, FlexX-Pharm, GOLD, AutoDock, FRED and the more frequently used ones like HADDOCK, AutoDock Vina and UCSF DOCK for anti-cancer drug development. Since cancer is a heterogeneous disease drug discovery becomes excessively complex. Bioinformatics therefore play a major role in a step wise drug discovery process in a lesser time and a cost efficient manner.

Aiding the discovery of new treatments for dementia by uncovering unknown benefits of existing medications

IntroductionThere is a significant need for disease-modifying therapies to treat and prevent dementia, including Alzheimer's disease. Availability of real-world observational information and new analytic techniques to analyze large volumes of data can provide a path to aid drug discovery. MethodsUsing a self-controlled study design, we examined the association between 2181 medications and incidence of dementia across four US insurance claims databases. Medications associated with ≥50% reduction in risk of dementia in ≥2 databases were examined. ResultsA total of 117,015,066 individuals were included in the analysis. Seventeen medications met our threshold criteria for a potential protective effect on dementia and fell into five classes: catecholamine modulators, anticonvulsants, antibiotics/antivirals, anticoagulants, and a miscellaneous group. DiscussionThe biological pathways of the medications identified in this analysis may be targets for further research and may aid in discovering novel therapeutic approaches to treat dementia. These data show association not causality.

Parametric Models for Understanding Atomic Trajectories in Different Domains of Lung Cancer Causing Protein

Non-small cell lung cancer (NSCLC) is a major cause of death worldwide. About 80% to 85% of lung cancer cases are NSCLC. It is well known that mutation of the epidermal growth factor (EGFR) may lead to the NSCLC. The first generation drugs are effective initially, but almost all patients develop drug resistance after about a year due to a secondary mutation. The computational methods are an efficient tool for investigating drug resistance, design, and discovery. Moreover, molecular dynamics (MD) simulation enables us to study and analyze the behavior of proteins and molecules at the atomic level. MD simulations offer extraordinary insight about biomolecules and are a valuable tool for computer aided drug discovery. Earlier studies on EGFR only focused on the kinase domain. Because EGFR is a multi-domain protein, mutations in the kinase domain may affect the function in other domains. Therefore, it is important to investigate the complete structure of the EGFR and its mutants. In this paper, we first generate the complete structure of the EGFR and perform MD simulation for the wildtype EGFR, EGFR with L858R mutation and EGFR with L858R and T790M mutation. We divide the complete structure of the EGFR and its mutants into 8 domains according to the reference crystal structure. We then consider atom trajectories as time series signals and estimate the power spectral densities using the auto-regressive integrated (ARI) model, which shows interesting insight. Dynamic time warping is used to analyze the similarity between each domain of the structures. Interesting patterns are observed which may be useful for investigating drug resistance and design. Furthermore, Pearson correlation coefficient, peaks, and widths of the power spectral density are calculated for each domain. The simulation results provide useful insight about conformation dynamics of the EGFR, such as atom motion and protein stability. The domains are less correlated in L858R type and even weaker when the second mutation occurs. The warping patterns are changed due to mutation and the movement of atoms is distorted. Hence, it is difficult for a drug to bind to the protein. These findings will be useful in understanding the characteristics of the EGFR and for computer aided drug design process for the NSCLC patients.

Enhancing Molecular Promiscuity Evaluation Through Assay Profiles.

The growing amount of heterogeneous bioactivity data requires effective strategies to assess the promiscuity/selectivity of small-molecules and aid drug discovery. In the current study, we aim to evaluate the potential of assay profiles (APs, i.e., unique combinations of assay-related features describing how activity determinations were performed and reported) in molecular promiscuity analysis. Using PubChem bioactivity data, we computed for all Molecular Libraries Small Molecule Repository (MLSMR library) compounds the frequency of hits score (FoH, i.e., the ratio between the number of times the compound was found active and the number of times it was tested), which were subsequently fit into 32 theoretical APs. The promiscuity of drugs and non-drugs was compared at different levels of test results. We found 8 dominant APs, indicating that compounds tested in more than ten assays (or against ten targets) and found active at least once tend to reach near to maximum hit rates in scientific literature and confirmatory assays (e.g., 95% of the drugs show FoH scores >0.93). Primary and high-throughput screening testing results in very low hit rates (e.g., 95% of the compounds show FoH scores <0.11), promoting a different perspective of promiscuity. In general, drugs exert higher promiscuity compared to non-drugs. Targets and classes of drugs are also discussed within the main APs. APs contain relevant features and are suited for big data promiscuity analysis. The activity data of the main APs are freely available on www.chembioinf.ro .

Human iPS Cell-Derived Patient Tissues and 3D Cell Culture Part 1: Target Identification and Lead Optimization.

Human-induced pluripotent stem cells (HiPSCs), and new technologies to culture them into functional cell types and tissues, are now aiding drug discovery. Patient-derived HiPSCs can provide disease models that are more clinically relevant and so more predictive than the currently available animal-derived or tumor cell-derived cells. These cells, consequently, exhibit disease phenotypes close to the human pathology, particularly when cultured under conditions that allow them to recapitulate the tissue architecture in three-dimensional (3D) systems. A key feature of HiPSCs is that they can be cultured under conditions that favor formation of multicellular spheroids or organoids. By culturing and differentiating in systems mimicking the human tissue in vivo, the HiPSC microenvironment further reflects patient in vivo physiology, pathophysiology, and ultimately pharmacological responsiveness. We assess the rationale for using HiPSCs in several phases of preclinical drug discovery, specifically in disease modeling, target identification, and lead optimization. We also discuss the growing use of HiPSCs in compound lead optimization, particularly in profiling compounds for their potential metabolic liability and off-target toxicities. Collectively, we contend that both approaches, HiPSCs and 3D cell culture, when used in concert, have exciting potential for the development of novel medicines.

COMPUTER AIDED DRUG DESIGN: AN OVERVIEW

Discovery and development of a new drug is generally known as a very complex process which takes a lot of time and resources. So now a day’s computer aided drug design approaches are used very widely to increase the efficiency of the drug discovery and development course. Various approaches of CADD are evaluated as promising techniques according to their need, in between all these structure-based drug design and ligand-based drug design approaches are known as very efficient and powerful techniques in drug discovery and development. These both methods can be applied with molecular docking to virtual screening for lead identification and optimization. In the recent times computational tools are widely used in pharmaceutical industries and research areas to improve effectiveness and efficacy of drug discovery and development pipeline. In this article we give an overview of computational approaches, which is inventive process of finding novel leads and aid in the process of drug discovery and development research.&#x0D; Keywords: computer aided drug discovery, structure-based drug design, ligand-based drug design, virtual screening and molecular docking

A confidence predictor for logD using conformal regression and a support-vector machine

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water–octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of hbox {Q}^{2}=0.973 and with the best performing nonconformity measure having median prediction interval of pm ~0.39 log units at 80% confidence and pm ~0.60 log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Rational Design, Synthesis and Biological Evaluation of Pyrimidine-4,6-diamine derivatives as Type-II inhibitors of FLT3 Selective Against c-KIT

FMS-like Tyrosine Kinase 3 (FLT3) is a clinically validated target for acute myeloid leukemia (AML). Inhibitors targeting FLT3 have been evaluated in clinical studies and have exhibited potential to treat FLT3-driven AML. A frequent, clinical limitation is FLT3 selectivity, as concomitant inhibition of FLT3 and c-KIT is thought to cause dose-limiting myelosuppression. Through a rational design approach, novel FLT3 inhibitors were synthesized employing a pyridine/pyrimidine warhead. The most potent compound identified from the studies is compound 13a, which exhibited an IC50 value of 13.9 ± 6.5 nM against the FLT3 kinase with high selectivity over c-KIT. Mechanism of action studies suggested that 13a is a Type-II kinase inhibitor, which was also supported through computer aided drug discovery (CADD) efforts. Cell-based assays identified that 13a was potent on a variety of FLT3-driven cell lines with clinical relevance. We report herein the discovery and therapeutic evaluation of 4,6-diamino pyrimidine-based Type-II FLT3 inhibitors, which can serve as a FLT3-selective scaffold for further clinical development.

Immobilized FhuD2 Siderophore-Binding Protein Enables Purification of Salmycin Sideromycins from Streptomyces violaceus DSM 8286

Siderophores are a structurally diverse class of natural products common to most bacteria and fungi as iron(III)-chelating ligands. Siderophores, including trihydroxamate ferrioxamines, are used clinically to treat iron overload diseases and show promising activity against many other iron-related human diseases. Here, we present a new method for the isolation of ferrioxamine siderophores from complex mixtures using affinity chromatography based on resin-immobilized FhuD2, a siderophore-binding protein (SBP) from Staphylococcus aureus. The SBP-resin enabled purification of charge positive, charge negative, and neutral ferrioxamine siderophores. Treatment of culture supernatants from Streptomyces violaceus DSM 8286 with SBP-resin provided an analytically pure sample of the salmycins, a mixture of structurally complex glycosylated sideromycins (siderophore-antibiotic conjugates) with potent antibacterial activity toward human pathogenic Staphylococcus aureus (minimum inhibitory concentration (MIC) = 7 nM). Siderophore affinity chromatography could enable the rapid discovery of new siderophore and sideromycin natural products from complex mixtures to aid drug discovery and metabolite identification efforts in a broad range of therapeutic areas.

Towards Dynamic Pharmacophore Models by Coarse Grained Molecular Dynamics

Pharmacophore models play a crucial role in computer aided drug discovery e.g. in virtual screening, de novo drug design, and lead optimization. Due to the increased numbers of protein structures elucidated, structure-based methods for developing pharmacophore models have started gaining in popularity and are becoming of particular importance. There has been a number of studies combining such methods with the use of MD simulations to model protein flexibility. In the MARTINI forcefield, four heavy atoms are represented with the use of a single interaction centre. Four types of interactions have been parametrised in the model, polar, charged, non-polar and apolar. These interactions coincide with some of the typical features found in a pharmacophore model, allowing the CG atoms to be used as pharmacophoric probes. These probes are then used in CG MD simulations in order to explore protein interaction propensities. This approach, in combination with cavity detection methods, allows for the identification of potential ligand binding sites and the detection of ‘hotspot’ interactions that enhance ligand binding. Using CDK2 as a test case, we demonstrate the ability of this method to recapitulate the positions of moieties that contribute to binding interactions commonly observed for ligands known to bind CDK2 and for which crystal structures are available. When examining the ATP binding site of CDK2, a comprehensive and accurate map of the interactions which play a role in ligand binding is generated. The interaction sites identified are then compared with holo crystal structures and are shown to correctly identify the moieties which contribute to the binding interactions. By calculating the ΔGInt values of the interaction maps, we are able to further focus on the areas of interest and identify which moieties are the driving force behind binding.

Recent Advances in Cancer Drug Development: Targeting Induced Myeloid Cell Leukemia-1 (Mcl-1) Differentiation Protein.

Anti-apoptotic members of the Bcl-2 family of proteins are upregulated in a majority of cancers and are potential therapeutic targets. Fragment-based design led to the development of clinical candidates that target Bcl-xL/Bcl-2. Although these BclxL/ Bcl-2 inhibitors showed promise in pre-clinical studies, resistance to several Bcl-xL inhibitors was observed, when used alone. This is attributed to the over-expression of Mcl-1, another member of the Bcl-2 family of proteins. Indeed, Mcl-1 is highly amplified in numerous cancers, suggesting that it may contribute to malignant cell growth and evasion of apoptosis. Therefore, significant efforts have been made toward the development of direct Mcl-1 inhibitors for cancer therapy. Following an extensive search of peer-reviewed articles on the development of Mcl-1-selective inhibitors, the literature retrieved is chronologically arranged and discussed in this review article. We have included 147 articles in this review; including articles that describe the development of stapled peptides with improved binding affinity as Mcl-1-selective BH3 mimetics, those describing fragment-based and structure-based design of small molecule Mcl-1 inhibitors by various research groups, and those detailing the use of natural products and their derivatives as potential Mcl-1 inhibitors. The therapeutic potential of targeting the Mcl-1 protein for cancer drug discovery is vast. Stapling BH3 peptides, as well as the development of small molecule inhibitors as BH3 mimetics, are viable strategies to develop selective Mcl-1 inhibitors. With no clinically approved candidate in hand, additional modes of perturbing the biological function of this protein will aid drug discovery efforts.

Whole body physiologically based modelling of β-blockers in the rat: events in tissues and plasma following an i.v. bolus dose.

Whole body physiologically based pharmacokinetic (PBPK) models have been increasingly applied in drug development to describe kinetic events of therapeutic agents in animals and humans. The advantage of such modelling is the ability to incorporate vast amounts of physiological information, such as organ blood flow and volume, to ensure that the model is as close to reality as possible. Previous PBPK model development of enantiomers of a series of seven racemic β-blockers, namely, acebutolol, betaxolol, bisoprolol, metoprolol, oxprenolol, pindolol and propranolol, together with S-timolol in rat was based on tissue and blood concentration data at steady state. Compounds were administered in several cassettes with the composition mix and blood and tissue sampling times determined using a D-optimal design. Closed-loop PBPK models were developed initially based on the application of open loop forcing function models to individual tissues and compounds. For the majority of compounds and tissues, distribution kinetics was adequately characterized by perfusion rate-limited models. For some compounds in the testes and gut, a permeability rate-limited distribution model was required to best fit the data. Parameter estimates of the tissue-to-blood partition coefficient through fitting of individual enantiomers and of racemic pair were generally in agreement and also concur with those from previous steady-state experiments. PBPK modelling is a very powerful tool to aid drug discovery and development of therapeutic agents in animals and humans. However, careful consideration of the assumptions made during the modelling exercise is essential.