A Study on Cheminformatics and its Applications on Modern Drug Discovery

Several distance and degree-based molecular structural attributes of cove-edged graphene nanoribbons

A carbon-based material with a broad scope of favourable developments is called graphene. Recently, a graphene nanoribbon with cove-edged was integrated by utilizing a bottom-up liquid-phase procedure, and it can be geometrically viewed as a hybrid of the armchair and the zigzag edges. It is indeed a type of nanoribbon containing asymmetric edges made up of sequential hexagons with impressive mechanical and electrical characteristics. Topological indices are numerical values associated with the structure of a chemical graph and are used to predict various physical, chemical, and biological properties of molecules. They are derived from the graph representation of molecules, where atoms are represented as vertices and bonds as edges. In this article, we derived the exact topological expressions of cove-edged graphene nanoribbons based on the graph-theoretical structural measures that help reduce the number of repetitive laboratory tasks necessary for studying the physicochemical characteristics of graphene nanoribbons with curved edges.

Alanazi M, El Yazidi M. Efficient Synthesis, Structural Characterization, Antibacterial Assessment, ADME-Tox Analysis, Molecular Docking and Molecular Dynamics Simulations of New Functionalized Isoxazoles

This work describes the synthesis, characterization, and in vitro and in silico evaluation of the biological activity of new functionalized isoxazole derivatives. The structures of all new compounds were analyzed by IR and NMR spectroscopy. The structures of 4c and 4f were further confirmed by single crystal X-ray and their compositions unambiguously determined by mass spectrometry (MS). The antibacterial effect of the isoxazoles was assessed in vitro against Escherichia coli, Bacillus subtilis, and Staphylococcusaureus bacterial strains. Isoxazole 4a showed significant activity against E. coli and B. subtilis compared to the reference antibiotic drugs while 4d and 4f also exhibited some antibacterial effects. The molecular docking results indicate that the synthesized compounds exhibit strong interactions with the target proteins. Specifically, 4a displayed a better affinity for E. coli, S. aureus, and B. subtilis in comparison to the reference drugs. The molecular dynamics simulations performed on 4a strongly support the stability of the ligand-receptor complex when interacting with the active sites of proteins from E. coli, S. aureus, and B. subtilis. Lastly, the results of the Absorption, Distribution, Metabolism, Excretion and Toxicity Analysis (ADME-Tox) reveal that the molecules have promising pharmacokinetic properties, suggesting favorable druglike properties and potential therapeutic agents.

Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design

Artificial intelligence (AI) has emerged as a powerful tool that harnesses anthropomorphic knowledge and provides expedited solutions to complex challenges. Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes. Machine learning algorithms assist in experimental design and can predict the pharmacokinetics and toxicity of drug candidates. This capability enables the prioritization and optimization of lead compounds, reducing the need for extensive and costly animal testing. Personalized medicine approaches can be facilitated through AI algorithms that analyze real-world patient data, leading to more effective treatment outcomes and improved patient adherence. This comprehensive review explores the wide-ranging applications of AI in drug discovery, drug delivery dosage form designs, process optimization, testing, and pharmacokinetics/pharmacodynamics (PK/PD) studies. This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care.

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.

Chemo-structural diversity of anti-obesity compound database

Nature plays a major role in the development of new drugs which helps in preventing and treating human diseases. Anti-obesity compound database (AOCD) contains comprehensive information on all published small molecules from natural sources with anti-obesity potential targeting pancreatic lipase (PL), appetite suppressant (AS) and adipogenesis (AD). Presently the database contains 349 compounds isolated from 307 plants, 26 marine and 16 microbial sources. Users can query the AOCD database (https://aocd.swmd.co.in/) in several ways. The database was divided into three datasets (PL, AS and AD) to perform chemoinformatic analysis using Platform for Unified Molecular Analysis (PUMA), which were analyzed based on molecular descriptors, scaffold diversity and structural fingerprint diversity. Chemoinformatics study inferred the PL dataset has the highest diversity of compounds based on the Euclidean distance on molecular properties, scaffold diversity and pairwise similarity on fingerprint diversity. This study would hasten the process of anti-obesity drug discovery.

NyctiDB: A non-relational bioprocesses modeling database supported by an ontology

Strategies to exploit and enable the digitalization of industrial processes are on course to become game-changers in optimizing (bio)chemical facilities. To achieve this, these industries face an increasing need for process models and, as importantly, an efficient way to store the models and data/information. Therefore, this work proposes developing an online information storage system that can facilitate the reuse and expansion of process models and make them available to the digitalization cycle. This system is named NyctiDB, and it is a novel non-relational database coupled with a bioprocess ontology. The ontology supports the selection and classification of bioprocess models focused information, while the database is in charge of the online storage of said information. Through a series of online collections, NyctiDB contains essential knowledge for the design, monitoring, control, and optimization of a bioprocess based on its mathematical model. Once NyctiDB has been implemented, its applicability and usefulness are demonstrated through two applications. Application A shows how NyctiDB is integrated inside the software architecture of an online educational bioprocess simulator. This implies that NyctiDB provides the information for the visualization of different bioprocess behaviours and the modifications of the models in the software. Moreover, the information related to the parameters and conditions of each model is used to support the users’ understanding of the process. Additionally, application B illustrates that NyctiDB can be used as AI enabler to further the research in this field through open-source and reliable data. This can, in fact, be used as the information source for the AI frameworks when developing, for example, hybrid models or smart expert systems for bioprocesses. Henceforth, this work aims to provide a blueprint on how to collect bioprocess modeling information and connect it to facilitate and empower the Internet-of-Things paradigm and the digitalization of the biomanufacturing industries.

Machine learning approaches and their applications in drug discovery and design

This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

Computational Chemistry Study of Natural Apocarotenoids and Their Synthetic Glycopeptide Conjugates as Therapeutic Drugs

The objective of the research to be presented in the chapter is the determination of the chemical reactivity properties of some natural apocarotenoids and their synthetic glycopeptide conjugates that could have the ability to inhibit SARS-CoV-2 replication. The study will be based on the consideration of the Conceptual DFT branch of Density Functional Theory (DFT) through the consideration of particular successful model chemistry which has been demonstrated as satisfying the Janak and Ionization Energy theorems within Generalized Gradient Approximation (GGA) theory. The research will be complemented by a report of the ADMET and pharmacokinetic properties hoping that this information could be of help in the development of new pharmaceutical drugs for fighting COVID-19.

Conceptual DFT-Based Computational Peptidology, Pharmacokinetics Study and ADMET Report of the Veraguamides A–G Family of Marine Natural Drugs

As a continuation of our research on the chemical reactivity, pharmacokinetics and ADMET properties of cyclopeptides of marine origin with potential therapeutic abilities, in this work our already presented integrated molecular modeling protocol has been used for the study of the chemical reactivity and bioactivity properties of the Veraguamides A–G family of marine natural drugs. This protocol results from the estimation of the conceptual density functional theory (CDFT) chemical reactivity descriptors together with several chemoinformatics tools commonly considered within the process of development of new therapeutic drugs. CP-CDFT is a branch of computational chemistry and molecular modeling dedicated to the study of peptides, and it is a protocol that allows the estimation with great accuracy of the CDFT-based reactivity descriptors and the associated physical and chemical properties, which can aid in determining the ability of the studied peptides to behave as potential useful drugs. Moreover, the superiority of the MN12SX density functional over other long-range corrected density functionals for the prediction of chemical and physical properties in the presence of water as the solvent is clearly demonstrated. The research was supplemented with an investigation of the bioactivity of the molecular systems and their ADMET (absorption, distribution, metabolism, excretion, and toxicity) parameters, as is customary in medicinal chemistry. Some instances of the CDFT-based chemical reactivity descriptors’ capacity to predict the pKas of peptides as well as their potential as AGE inhibitors are also shown.

Computational peptidology approach to the study of the chemical reactivity and bioactivity properties of Aspergillipeptide D, a cyclopentapeptide of marine origin

Aspergillipeptide D is a cyclic pentapeptide isolated from the marine gorgonian Melitodes squamata-derived fungus Aspergillus sp. SCSIO 41501 that it has been shown to present moderate activity against herpes virus simplex type 1 (HSV-1). Thus, this paper presents the results of a computational study of this cyclopentapeptide's chemical reactivity and bioactivity properties using a CDFT-based computational peptidology (CDFT-CP) methodology, which is derived from combining chemical reactivity descriptors derived from Conceptual Density Functional Theory (CDFT) and some Cheminformatics tools which may be used. This results in an improvement of the virtual screening procedure by a similarity search allowing the identification and validation of the known ability of the peptide to act as a possible useful drug. This was followed by an examination of the drug's bioactivity and pharmacokinetics indices in relation to the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics. The findings provide further evidence of the MN12SX density functional's superiority in proving the Janak and Ionization Energy theorems using the proposed KID approach. This has proven to be beneficial in accurately predicting CDFT reactivity characteristics, which aid in the understanding of chemical reactivity. The Computational Pharmacokinetics study revealed the potential ability of Aspergillipeptide D as a therapeutic drug through the interaction with different target receptors. The ADMET indices confirm this assertion through the absence of toxicity and good absorption and distribution properties.

Computational Pharmacokinetics Report, ADMET Study and Conceptual DFT-Based Estimation of the Chemical Reactivity Properties of Marine Cyclopeptides

Homophymines A-E and A1-E1 are bioactive natural cyclodepsipeptides with a complex molecular architecture. These molecules could have a potential use as antimicrobial, antiviral, and anticancer substances. We have carried out a computational study of the properties of this family of marine peptides using a CDFT-based Computational Peptidology (CDFT-CP) methodology that results from the combination of the chemical reactivity descriptors that arise from conceptual Density Functional Theory (CDFT) together with cheminformatics tools. The latter can be used to estimate the associated physicochemical parameters and to improve the process of virtual screening through a similarity search. Using this approach, the ability of the peptides to behave as a potentially useful drugs can be investigated. An analysis of their bioactivity and pharmacokinetics indices related to the ADMET (Absorption, Distribution, Metabolism, Excretion and Toxicity) features has also been carried out.

An integrated molecular modeling protocol for drug screening based on conceptual density functional theory and chemoinformatics for the study of marine cyclopeptides

An integrated molecular modeling protocol resulting from the combination of conceptual density functional theory (CDFT) chemical reactivity descriptors with several chemoinformatics tools has been used for the study of the chemical reactivity and bioactivity properties of a group of marine cyclic peptides. CP-CDFT is a branch of computational chemistry and molecular modeling dedicated to the study of peptides. The protocol allowed the estimation of the CDFT-based reactivity indices together with the associated physicochemical parameters that can help to identify the ability of the studied peptides to behave as potential useful drugs. This was complemented with an analysis of the bioactivity and pharmacokinetics parameters related to the ADMET (absorption, distribution, metabolism, excretion, and toxicity) features. Some examples related to the ability of the CDFT-based chemical reactivity descriptors for the prediction of the pKas of the peptides as well as their potential as AGE inhibitors are also presented.

A CDFT-Based Computational Peptidology (CDFT-CP) Study of the Chemical Reactivity and Bioactivity of the Marine-Derived Alternaramide Cyclopentadepsipeptide

Alternaramide is a cyclic pentadepsipeptide isolated from marine sources that has been shown to present weak antibiotic activity against Bacillus subtilis and Staphylococcus aureus as well as inhibitory effects on inflammatory mediator expressions. Thus, this work reports the results of a computational study of the chemical reactivity and bioactivity properties of this cyclopentadepsipeptide considering a CDFT-based computational peptidology (CDFT-CP) methodology that results from the combination of the chemical reactivity descriptors that arise from conceptual density functional theory (CDFT) together with some cheminformatics tools that can be used to estimate the associated physicochemical parameters, to improve the process of virtual screening through a similarity search, and to identify the ability of the peptide to behave as a potential useful drug, complemented with an analysis of its bioactivity and pharmacokinetics indices related to the ADMET (absorption, distribution, metabolism, excretion, and toxicity) features. The results represent a new confirmation of the superiority of the MN12SX density functional in the fulfilment of the Janak and ionization energy theorems through the proposed KID procedure. This has been useful for the accurate prediction of the CDFT reactivity descriptors that help in understanding the chemical reactivity. The computational pharmacokinetics study revealed the potential ability of alternaramide as a therapeutic drug by interacting with GPCR ligands and protease inhibitors. The ADMET indices confirm this assertion through the absence of toxicity and good absorption and distribution properties.

In Silico Pharmacokinetics, ADMET Study and Conceptual DFT Analysis of Two Plant Cyclopeptides Isolated From Rosaceae as a Computational Peptidology Approach

This research presents the outcomes of a computational determination of the chemical reactivity and bioactivity properties of two plant cyclopeptides isolated from Rosaceae through the consideration of Computational Peptidology (CP), a protocol employed previously in the research of similar molecular systems. CP allows the prediction of the global and local descriptors that are the integral foundations of Conceptual Density Functional Theory (CDFT) and which could help in getting in the understanding of the chemical reactivity properties of the two plant cyclopeptides under study, hoping that they could be related to their bioactivity. The methodology based on the Koopmans in DFT (KID) approach and the MN12SX/Def2TZVP/H2O model chemistry has been successfully validated. Various Chemoinformatics tools have been used to improve the process of virtual screening, thus identifying some additional properties of these two plant cyclopeptides connected to their ability to behave as potentially useful drugs. With the further objective of analyzing their bioactivity, the CP protocol is complemented with the estimation of some useful parameters related to pharmacokinetics, their predicted biological targets, and the Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) parameters related to the bioavailability of the two plant cyclopeptides under study are also reported.

Emerging Need of Today: Significant Utilization of Various Databases and Softwares in Drug Design and Development

In drug discovery, in silico methods have become a very important part of the process. These approaches impact the entire development process by discovering and identifying new target proteins as well as designing potential ligands with a significant reduction of time and cost. Furthermore, in silico approaches are also preferred because of reduction in the experimental use of animals as; in vivo testing for safer drug design and repositioning of known drugs. Novel software-based discovery and development such as direct/indirect drug design, molecular modelling, docking, screening, drug-receptor interaction, and molecular simulation studies are very important tools for the predictions of ligand-target interaction pattern, pharmacodynamics as well as pharmacokinetic properties of ligands. On the other part, the computational approaches can be numerous, requiring interdisciplinary studies and the application of advanced computer technology to design effective and commercially feasible drugs. This review mainly focuses on the various databases and software used in drug design and development to speed up the process.

Chemical space and diversity of seaweed metabolite database (SWMD): A cheminformatics study

Seaweeds have attracted attention in the past decade as a biological source of highly diverse secondary metabolites with great potential in the industrial and pharmaceutical sciences. Herein, we represent a comprehensive cheminformatics study to compare the chemical diversity of seaweed metabolites based on their taxonomic source. Seaweed Metabolite Database (SWMD) was utilized in this study. The compounds were manually categorized into three datasets, namely red algae (Rhodophyta, n = 645), brown algae (Phaeophyta, n = 220), and green algae (Chlorophyta, n = 32). The compounds in each dataset were curated to generate six chemical descriptors of pharmaceutical interest for each molecule, which were later used to visualize the chemical space of these metabolites by principal component analysis. Scaffolds were generated by removing side chains and keeping the core part of each molecule. Scaffold diversity among the tested datasets was quantified using Cyclic System Retrieval Curves. Green algae metabolites in SWMD possessed the highest scaffold diversity followed by brown and red algae metabolites, respectively. Three structural binary fingerprints, including ECFP_4, MACCS keys, and PubChem were computed indicating that the red algae metabolites had the highest fingerprint diversity followed by the green and brown algae metabolites respectively. Finally, Consensus Diversity Plots were generated to assess the global diversity considering both scaffold and fingerprint diversity. It was concluded that green algae metabolites in the SWMD are the most diverse regarding chemical descriptors of pharmaceutical relevance and scaffolds. While red algae possess the highest fingerprint diversity.

The impact of chemoinformatics on drug discovery in the pharmaceutical industry

Introduction: Even though there have been substantial advances in our understanding of biological systems, research in drug discovery is only just now beginning to utilize this type of information. The single-target paradigm, which exemplifies the reductionist approach, remains a mainstay of drug research today. A deeper view of the complexity involved in drug discovery is necessary to advance on this field.Areas covered: This perspective provides a summary of research areas where cheminformatics has played a key role in drug discovery, including of the available resources as well as a personal perspective of the challenges still faced in the field.Expert opinion: Although great strides have been made in the handling and analysis of biological and pharmacological data, more must be done to link the data to biological pathways. This is crucial if one is to understand how drugs modify disease phenotypes, although this will involve a shift from the single drug/single target paradigm that remains a mainstay of drug research. Moreover, such a shift would require an increased awareness of the role of physiology in the mechanism of drug action, which will require the introduction of new mathematical, computer, and biological methods for chemoinformaticians to be trained in.

The rcdk and cluster R packages applied to drug candidate selection

The aim of this article is to show how thevpower of statistics and cheminformatics can be combined, in R, using two packages: rcdk and cluster.We describe the role of clustering methods for identifying similar structures in a group of 23 molecules according to their fingerprints. The most commonly used method is to group the molecules using a "score" obtained by measuring the average distance between them. This score reflects the similarity/non-similarity between compounds and helps us identify active or potentially toxic substances through predictive studies.Clustering is the process by which the common characteristics of a particular class of compounds are identified. For clustering applications, we are generally measure the molecular fingerprint similarity with the Tanimoto coefficient. Based on the molecular fingerprints, we calculated the molecular distances between the methotrexate molecule and the other 23 molecules in the group, and organized them into a matrix. According to the molecular distances and Ward 's method, the molecules were grouped into 3 clusters. We can presume structural similarity between the compounds and their locations in the cluster map. Because only 5 molecules were included in the methotrexate cluster, we considered that they might have similar properties and might be further tested as potential drug candidates.

Potential Therapeutic Approaches to Alzheimer's Disease By Bioinformatics, Cheminformatics And Predicted Adme-Tox Tools

BACKGROUND

Alzheimer's disease (AD) is considered a severe, irreversible and progressive neurodegenerative disorder. Currently, the pharmacological management of AD is based on a few clinically approved acethylcholinesterase (AChE) and N-methyl-D-aspartate (NMDA) receptor ligands, with unclear molecular mechanisms and severe side effects.

METHODS

Here, we reviewed the most recent bioinformatics, cheminformatics (SAR, drug design, molecular docking, friendly databases, ADME-Tox) and experimental data on relevant structurebiological activity relationships and molecular mechanisms of some natural and synthetic compounds with possible anti-AD effects (inhibitors of AChE, NMDA receptors, beta-secretase, amyloid beta (Aβ), redox metals) or acting on multiple AD targets at once. We considered: (i) in silico supported by experimental studies regarding the pharmacological potential of natural compounds as resveratrol, natural alkaloids, flavonoids isolated from various plants and donepezil, galantamine, rivastagmine and memantine derivatives, (ii) the most important pharmacokinetic descriptors of natural compounds in comparison with donepezil, memantine and galantamine.

RESULTS

In silico and experimental methods applied to synthetic compounds led to the identification of new AChE inhibitors, NMDA antagonists, multipotent hybrids targeting different AD processes and metal-organic compounds acting as Aβ inhibitors. Natural compounds appear as multipotent agents, acting on several AD pathways: cholinesterases, NMDA receptors, secretases or Aβ, but their efficiency in vivo and their correct dosage should be determined.

CONCLUSION

Bioinformatics, cheminformatics and ADME-Tox methods can be very helpful in the quest for an effective anti-AD treatment, allowing the identification of novel drugs, enhancing the druggability of molecular targets and providing a deeper understanding of AD pathological mechanisms.

The significance of artificial intelligence in drug delivery system design

Over the last decade, increasing interest has been attracted towards the application of artificial intelligence (AI) technology for analyzing and interpreting the biological or genetic information, accelerated drug discovery, and identification of the selective small-molecule modulators or rare molecules and prediction of their behavior. Application of the automated workflows and databases for rapid analysis of the huge amounts of data and artificial neural networks (ANNs) for development of the novel hypotheses and treatment strategies, prediction of disease progression, and evaluation of the pharmacological profiles of drug candidates may significantly improve treatment outcomes. Target fishing (TF) by rapid prediction or identification of the biological targets might be of great help for linking targets to the novel compounds. AI and TF methods in association with human expertise may indeed revolutionize the current theranostic strategies, meanwhile, validation approaches are necessary to overcome the potential challenges and ensure higher accuracy. In this review, the significance of AI and TF in the development of drugs and delivery systems and the potential challenging issues have been highlighted.

Using bioinformatics tools for the discovery of Dengue RNA-dependent RNA polymerase inhibitors

Background

Dengue fever has rapidly manifested into a serious global health concern. The emergence of various viral serotypes has prompted the urgent need for innovative drug design techniques. Of the viral non-structural enzymes, the NS5 RNA-dependent RNA polymerase has been established as a promising target due to its lack of an enzymatic counterpart in mammalian cells and its conserved structure amongst all serotypes. The onus is now on scientists to probe further into understanding this enzyme and its mechanism of action. The field of bioinformatics has evolved greatly over recent decades, with updated drug design tools now being publically available.

Methods

In this study, bioinformatics tools were used to provide a comprehensive sequence and structural analysis of the two most prominent serotypes of Dengue RNA-dependent RNA polymerase. A list of popular flavivirus inhibitors were also chosen to dock to the active site of the enzyme. The best docked compound was then used as a template to generate a pharmacophore model that may assist in the design of target-specific Dengue virus inhibitors.

Results

Comparative sequence alignment exhibited similarity between all three domains of serotype 2 and 3.Sequence analysis revealed highly conserved regions at residues Meth530, Thr543 Asp597, Glu616, Arg659 and Pro671. Mapping of the active site demonstrated two highly conserved residues: Ser710 and Arg729. Of the active site interacting residues, Ser796 was common amongst all ten docked compounds, indicating its importance in the drug design process. Of the ten docked flavivirus inhibitors, NITD-203 showed the best binding affinity to the active site. Further pharmacophore modeling of NITD-203 depicted significant pharmacophoric elements that are necessary for stable binding to the active site.

Discussion

This study utilized publically available bioinformatics tools to provide a comprehensive framework on Dengue RNA-dependent RNA polymerase. Based on docking studies, a pharmacophore model was also designed to unveil the crucial pharmacophoric elements that are required when constructing an efficacious DENV inhibitor. We believe that this study will be a cornerstone in paving the road toward the design of target-specific inhibitors against DENV RdRp.

Bioinformatics-based tools in drug discovery: the cartography from single gene to integrative biological networks

Originally developed for the analysis of biological sequences, bioinformatics has advanced into one of the most widely recognized domains in the scientific community. Despite this technological evolution, there is still an urgent need for nontoxic and efficient drugs. The onus now falls on the 'omics domain to meet this need by implementing bioinformatics techniques that will allow for the introduction of pioneering approaches in the rational drug design process. Here, we categorize an updated list of informatics tools and explore the capabilities of integrative bioinformatics in disease control. We believe that our review will serve as a comprehensive guide toward bioinformatics-oriented disease and drug discovery research.

Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success

Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.