3-D pharmacophores in drug discovery

In the quest for histone deacetylase inhibitors: current trends in the application of multilayered computational methods

Histone deacetylase (HDAC) inhibitors have gained attention over the past three decades because of their potential in the treatment of different diseases including various forms of cancers, neurodegenerative disorders, autoimmune, inflammatory diseases, and other metabolic disorders. To date, 5 HDAC inhibitor drugs are marketed for the treatment of hematological malignancies and several drug-candidate HDAC inhibitors are at different stages of clinical trials. However, due to the toxic side effects of these drugs resulting from the lack of target selectivity, active studies are ongoing to design and develop either class-selective or isoform-selective inhibitors. Computational methods have aided the discovery of HDAC inhibitors with the desired potency and/or selectivity. These methods include ligand-based approaches such as scaffold hopping, pharmacophore modeling, three-dimensional quantitative structure-activity relationships (3D-QSAR); and structure-based virtual screening (molecular docking). The current trends involve the application of the combination of these methods and incorporating molecular dynamics simulations coupled with Poisson-Boltzmann/molecular mechanics generalized Born surface area (MM-PBSA/MM-GBSA) to improve the prediction of ligand binding affinity. This review aimed at understanding the current trends in applying these multilayered strategies and their contribution to the design/identification of HDAC inhibitors.

CSC01 shows promise as a potential inhibitor of the oncogenic G13D mutant of KRAS: an in silico approach

About 30% of malignant tumors include KRAS mutations, which are frequently required for the development and maintenance of malignancies. KRAS is now a top-priority cancer target as a result. After years of research, it is now understood that the oncogenic KRAS-G12C can be targeted. However, many other forms, such as the G13D mutant, are yet to be addressed. Here, we used a receptor-based pharmacophore modeling technique to generate potential inhibitors of the KRAS-G13D oncogenic mutant. Using a comprehensive virtual screening workflow model, top hits were selected, out of which CSC01 was identified as a promising inhibitor of the oncogenic KRAS mutant (G13D). The stability of CSC01 upon binding the switch II pocket was evaluated through an exhaustive molecular dynamics simulation study. The several post-simulation analyses conducted suggest that CSC01 formed a stable complex with KRAS-G13D. CSC01, through a dynamic protein-ligand interaction profiling analysis, was also shown to maintain strong interactions with the mutated aspartic acid residue throughout the simulation. Although binding free energy analysis through the umbrella sampling approach suggested that the affinity of CSC01 with the switch II pocket of KRAS-G13D is moderate, our DFT analysis showed that the stable interaction of the compound might be facilitated by the existence of favorable molecular electrostatic potentials. Furthermore, based on ADMET predictions, CSC01 demonstrated a satisfactory drug likeness and toxicity profile, making it an exemplary candidate for consideration as a potential KRAS-G13D inhibitor.

Identification of potential drug candidates as TGR5 agonist to combat type II diabetes using in silico docking and molecular dynamics simulation studies

A cell surface bile acid receptor TGR5 being considered as a novel target for Type II diabetes found to be expressed in various tissues. A major role for TGR5 is to maintain blood sugar levels and increase in energy expenditure. These benefits make it a potential candidate for the treatment of type 2 diabetes, obesity and other metabolic disorder. To date, many novel TGR5 agonists have been synthesized and evaluated in the literature, but very few in silico computational studies have been reported. The discovery of a high-resolution crystal structure of TGR5 in 2020 provides an excellent opportunity for computational screening of potential agonists. In this study, we, therefore, aim to search novel, less toxic TGR5 agonists by iteratively analyzing molecular docking against TGR5 (PDB ID: 7CFN) by means of structure-based virtual screening. The docking score of the designed coumarin derivatives that have been docked successfully varies between -9.4 and -9.0 kcal/mol. The molecular docking and ADMET profile examinations of compounds D1, D5 and D15 revealed that these have a strong affinity for the active site residues of TGR5. In addition, molecular dynamics simulation (MDS) studies have shown the stability of compounds that bind to TGR5. It can be summarized that designed coumarin derivatives seem to have promising activity as TGR5 agonists.Communicated by Ramaswamy H. Sarma.

Galileo: Three-dimensional searching in large combinatorial fragment spaces on the example of pharmacophores

Fragment spaces are an efficient way to model large chemical spaces using a handful of small fragments and a few connection rules. The development of Enamine's REAL Space has shown that large spaces of readily available compounds may be created this way. These are several orders of magnitude larger than previous libraries. So far, searching and navigating these spaces is mostly limited to topological approaches. A way to overcome this limitation is optimization via metaheuristics which can be combined with arbitrary scoring functions. Here we present Galileo, a novel Genetic Algorithm to sample fragment spaces. We showcase Galileo in combination with a novel pharmacophore mapping approach, called Phariety, enabling 3D searches in fragment spaces. We estimate the effectiveness of the approach with a small fragment space. Furthermore, we apply Galileo to two pharmacophore searches in the REAL Space, detecting hundreds of compounds fulfilling a HSP90 and a FXIa pharmacophore.

Ligand-Based Drug Design of Novel Antimicrobials against Staphylococcus aureus by Targeting Bacterial Transcription

Staphylococcus aureus is a common human commensal pathogen that causes a wide range of infectious diseases. Due to the generation of antimicrobial resistance, the pathogen becomes resistant to more and more antibiotics, resulting in methicillin-resistant S. aureus (MRSA) and even multidrug-resistant S. aureus (MDRSA), namely 'superbugs'. This situation highlights the urgent need for novel antimicrobials. Bacterial transcription, which is responsible for bacterial RNA synthesis, is a valid but underutilized target for developing antimicrobials. Previously, we reported a novel class of antimicrobials, coined nusbiarylins, that inhibited bacterial transcription by interrupting the protein-protein interaction (PPI) between two transcription factors NusB and NusE. In this work, we developed a ligand-based workflow based on the chemical structures of nusbiarylins and their activity against S. aureus. The ligand-based models-including the pharmacophore model, 3D QSAR, AutoQSAR, and ADME/T calculation-were integrated and used in the following virtual screening of the ChemDiv PPI database. As a result, four compounds, including J098-0498, 1067-0401, M013-0558, and F186-026, were identified as potential antimicrobials against S. aureus, with predicted pMIC values ranging from 3.8 to 4.2. The docking study showed that these molecules bound to NusB tightly with the binding free energy ranging from -58 to -66 kcal/mol.

Applications of the Novel Quantitative Pharmacophore Activity Relationship Method QPhAR in Virtual Screening and Lead-Optimisation

Pharmacophores are an established concept for the modelling of ligand–receptor interactions based on the abstract representations of stereoelectronic molecular features. They became widely popular as filters for the fast virtual screening of large compound libraries. A lot of effort has been put into the development of sophisticated algorithms and strategies to increase the computational efficiency of the screening process. However, hardly any focus has been put on the development of automated procedures that optimise pharmacophores towards higher discriminatory power, which still has to be done manually by a human expert. In the age of machine learning, the researcher has become the decision-maker at the top level, outsourcing analysis tasks and recurrent work to advanced algorithms and automation workflows. Here, we propose an algorithm for the automated selection of features driving pharmacophore model quality using SAR information extracted from validated QPhAR models. By integrating the developed method into an end-to-end workflow, we present a fully automated method that is able to derive best-quality pharmacophores from a given input dataset. Finally, we show how the QPhAR-generated models can be used to guide the researcher with insights regarding (un-)favourable interactions for compounds of interest.

Network pharmacology-based study on the mechanism of scutellarin against zearalenone-induced ovarian granulosa cell injury

Zearalenone(ZEA) is a kind of mycotoxin widely existing in nature, its toxic effects can lead to the reproductive disorders in humans and animals. The aim of this study was to investigate the mechanism of scutellarin against ovarian granulosa cell(GCs) injury induced by ZEA based on network pharmacology, molecular docking method. The results show that 293 drug targets of scutellarin were found from PhamMapper database, and 583 disease targets were selected from Genecards database. Finally, 57 scutellarin targets were obtained for the repair of GCs injury with gene intersection. The protein-protein interaction(PPI), gene ontology(GO) and kyoto encyclopedia of genes and genomes(KEGG) analysis indicated that MAPK signaling pathway was most likely activated by scutellarin. Scutellarin with JNK or Caspase-3 had minimal and negative free binding energy in molecular docking analysis, indicating that they might be the acting targets of scutellarin. Cell viability was significantly decreased in ZEA treated cells. However, GCs viability, the level of estradiol(E₂) and progesterone(P₄) were significantly increased with addition of scutellarin to ZEA treated cells. Western blot analysis showed that scutellarin significantly reduced the expression of JNK, c-jun and Cleaved-caspasee-3 in GCs compared with ZEA treatment. In conclusion, scutellarin could alleviate the ovarian GCs injury by down-regulating the expression of JNK, c-jun and Cleaved-caspase-3 through the activation of MAPK/JNK signaling pathway. Our results will provide a theoretical foundation for the treatment of reproductive disorders with scutellarin.

Advancements in Assay Technologies and Strategies to Enable Drug Discovery

Assays drive drug discovery from the exploratory phases to the clinical testing of drug candidates. As such, numerous assay technologies and methodologies have arisen to support drug discovery efforts. Robust identification and characterization of tractable chemical matter requires biochemical, biophysical, and cellular approaches and often benefits from high-throughput methods. To increase throughput, efforts have been made to provide assays in miniaturized volumes which can be arrayed in microtiter plates to support the testing of as many as 100,000 samples/day. Alongside these efforts has been the growth of microtiter plate-free formats with encoded libraries that can support the screening of billions of compounds, a hunt for new drug modalities, as well as emphasis on more disease relevant formats using complex cell models of disease states. This review will focus on recent developments in high-throughput assay technologies applied to identify starting points for drug discovery. We also provide recommendations on strategies for implementing various assay types to select high quality leads for drug development.

Antidepressant Drug Design on TCAs and Phenoxyphenylpropylamines utilizing QSAR and Pharmacophore Modeling

BACKGROUND

Depression is a mental illness caused by the imbalance of important neurotransmitters such as serotonin (5-HT) and norepinephrine (NE). It is a serious neurological disorder that could be treated by antidepressant drugs.

OBJECTIVE

There are two major classes such as TCAs and phenoxyphenylpropylamines which have been proven to be broad-spectrum antidepressant compounds. Several attempts were made to design, synthesize and discover potent antidepressant compounds having the least toxicity and most selectivity towards serotonin and norepinephrine transporters. But there is hardly any drug design based on quantitative structure-activity relationship (QSAR) and pharmacophore modeling attempted yet.

METHOD

In the present study, many TCAs (dibenzoazepine) and phenoxyphenylpropylamine derivatives are taken into consideration for pharmacophore feature generation followed by pharmacophoric distant related descriptors based QSAR modeling. Further, several five new congeners have been designed which are subjected to the prediction of biological activities in terms of serotonin receptor affinity utilizing validated QSAR models developed by us.

RESULTS

An important pharmacophoric feature point C followed by the generation of a topography of the TCAs and phenoxyphenylpropylamine has been predicted. The developed pharmacophoric feature-based QSAR can explain 64.2% of the variances of 5-HT receptor antagonism. The best training model has been statistically validated by the prediction of test set compounds. This training model has been used for the prediction of some newly designed congeneric compounds which are comparable with the existed drugs.

CONCLUSION

The newly designed compounds may be proposed for further synthesis and biological screening as antidepressant agents.

Pharmacophore features for machine learning in pharmaceutical virtual screening

Methods of three-dimensional molecular alignment generally treat all pharmacophore features equally when superimposing. However, some pharmacophore features can be more important in a specific system. In this work, we derived the overlap volume of pharmacophore features from a molecular alignment approach as new features of molecules to build machine learning models. Features can be assigned weights to indicate their importance. With validation on DUD-E collection, models based on pharmacophore features represented by the overlap volume yielded significant performances with median AUC of approximately 0.98 and recall rate of almost 0.8.

Drug Discovery

Modern chemistry foundations were made in between the 18th and 19th centuries and have been extended in 20th century. R&D towards synthetic chemistry was introduced during the 1960s. Development of new molecular drugs from the herbal plants to synthetic chemistry is the fundamental scientific improvement. About 10-14 years are needed to develop a new molecule with an average cost of more than $800 million. Pharmaceutical industries spend the highest percentage of revenues, but the achievement of desired molecular entities into the market is not increasing proportionately. As a result, an approximate of 0.01% of new molecular entities are approved by the FDA. The highest failure rate is due to inadequate efficacy exhibited in Phase II of the drug discovery and development stage. Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery. These technologies can accelerate the success rates in introducing new molecular entities into the market.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, ¹H-NMR, ¹³C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Combining Similarity Searching and Network Analysis for the Identification of Active Compounds

A variety of computational screening methods generate similarity-based compound rankings for hit identification. However, these rankings are difficult to interpret. It is essentially impossible to determine where novel active compounds might be found in database rankings. Thus, compound selection largely depends on intuition and guesswork. Herein, we show that molecular networks can substantially aid in the analysis of similarity-based compound rankings. A series of networks generated for rankings provides visual access to search results and adds chemical neighborhood and context information for reference compounds that are not available in rankings. Network structure is shown to serve as a diagnostic criterion for the likelihood to successfully select active compounds from rankings. In addition, comparison of different networks makes it possible to prioritize alternative similarity measures for search calculations and optimize the enrichment of active compounds in rankings.

Evaluation of 6-chloro-N-[3,4-disubstituted-1,3-thiazol-2(3H)-ylidene]-1,3-benzothiazol-2-amine Using Drug Design Concept for Their Targeted Activity Against Colon Cancer Cell Lines HCT-116, HCT15, and HT29

BACKGROUND Colorectal adenocarcinoma is the second leading cause of cancer-related death in the world. The stage of the disease is related to the survival of the patient, and in early phases surgery is the main modality of treatment. The main aim of modern medicinal chemistry is to synthesize small molecules via drug designing, especially by targeting tumor cells. MATERIAL AND METHODS A new series of 19 compounds containing benzothiazole and thiazole were designed. Molecular docking studies were performed on the designed series of molecules. Compounds showing good binding affinity towards the EGFR receptor were selected for synthetic studies. Characterization of the synthesized compounds was done by FTIR, 1HNMR, Mass and C, H, N, analysis. RESULTS The anticancer evaluation of the synthesized compounds was done at NIC, USA at a single dose against colon cancer cell lines HCT 116, HCT15, and HC 29. The active compounds were further evaluated for the 5-dose testing. Compounds were designed by using docking analysis. To ascertain the interaction of EGFR tyrosine kinase binding, energy calculation was used. CONCLUSIONS The results of the present study indicate that the designed compounds show good activity against colon cancer cell lines, which may be further studied to design new potential molecules.

Computational methods in drug discovery

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

Combination therapeutics in complex diseases

The biological redundancies in molecular networks of complex diseases limit the efficacy of many single drug therapies. Combination therapeutics, as a common therapeutic method, involve pharmacological intervention using several drugs that interact with multiple targets in the molecular networks of diseases and may achieve better efficacy and/or less toxicity than monotherapy in practice. The development of combination therapeutics is complicated by several critical issues, including identifying multiple targets, targeting strategies and the drug combination. This review summarizes the current achievements in combination therapeutics, with a particular emphasis on the efforts to develop combination therapeutics for complex diseases.

In silico Screening and Evaluation of the Anticonvulsant Activity of Docosahexaenoic Acid-Like Molecules in Experimental Models of Seizures

Background:

Resistance to antiepileptic drugs and the intolerability in 20-30% of the patients raises demand for developing new drugs with improved efficacy and safety. Acceptable anticonvulsant activity, good tolerability, and inexpensiveness of docosahexaenoic acid (DHA) make it as a good candidate for designing and development of the new anticonvulsant medications.

Methods:

Ten DHA-based molecules were screened based on in silico screening of DHA-like molecules by root-mean-square deviation of atomic positions, the biological activity score of Professional Association for SQL Server, and structural requirements suggested by pharmacophore design. Anticonvulsant activity was tested against clonic seizures induced by pentylenetetrazole (PTZ, 60 mg/kg, i.p.) and tonic seizures induced by maximal electroshock (MES, 50 mA, 50 Hz, 1 ms duration) by intracerebroventricular (i.c.v.) injection of the screened compounds to mice.

Results:

Among screened compounds, 4-Phenylbutyric acid, 4-Biphenylacetic acid, phenylacetic acid, and 2-Phenylbutyric acid showed significant protective activity in pentylenetetrazole test with ED₅₀ values of 4, 5, 78, and 70 mM, respectively. In MES test, shikimic acid and 4-tert-Butylcyclo-hexanecarboxylic acid showed significant activity with ED₅₀ values 29 and 637 mM, respectively. Effective compounds had no mortality in mice up to the maximum i.c.v. injectable dose of 1 mM.

Conclusion:

Common electrochemical features and three-dimensional spatial structures of the effective compounds suggest the involvement of the anticonvulsant mechanisms similar to the parent compound DHA.

Identification of Structurally Diverse Antimicrobials Through Sequential Application of Pharmacophore Modeling, Virtual Screening, Molecular Docking and In Vitro Microbiological Assay

Dihydrofolate reductase enzyme has been an attractive biological target for the design and development of antimicrobials. Considering this, we have attempted to identify novel dihydrofolate reductase inhibitors through our well-defined in silico and in vitro work flow. An accurate and predictive pharmacophore model comprising of one hydrogen bond acceptor, two hydrophobic and one ring aromatic was developed and utilized as a query to search the National Cancer Institute and Maybridge database leading to retrieval of various compounds which were filtered on the basis of estimated activity, fit value and Lipinski's violation. Selected hits NSC3423, KM09759, NSC391, NSC2091 and HTS00630 were subjected to docking studies which resulted into visualization of potential interaction capabilities of hits in line to pharmacophoric features. The identified hits were evaluated for in vitro antimicrobial potential, and the results revealed that among all the five hits, NSC3423 is the most potent compound with activity against E. coli, P. aeruginosa, S. aureus, B. substilis, A. niger and F. oxysporum. On the other hand, KM09759, NSC391, NSC2091 and HTS00630 showed varying degree of activities against gram-positive, gram-negative and fungal strains.

In-silico combinatorial design and pharmacophore modeling of potent antimalarial 4-anilinoquinolines utilizing QSAR and computed descriptors

There are very few studies for combinatorial library design and high throughput screening of 4-anilinoquinoline antimalarial compounds having activities against parasitic strain of P. falciparum. Therefore, an attempt has been made in the present paper to design potent lead compounds in this congener utilizing quantitative structure activity relationship utilizing theoretical molecular descriptors. QSAR models for a series of 4-anilinoquinolines considering various theoretical molecular descriptors including topological, constitutional, geometrical, functional group and atom-centered fragments has been carried out by stepwise forward–backward variable selections assimilating multiple linear regression (MLR) methods showing the topological indices contribute maximum impact on parasitic P. falciparum strain. A combinatorial library of 2160 compounds has been generated and finally, 16 compounds were screened through high throughput screening as promising 4-anilinoquinoline antimalarial hits based on their predicted activities utilizing topological descriptor based validated QSAR model. Highly predicted active compounds were then undergone for pharmacophore modeling to predict mode of binding and to optimize leads having greater affinity towards malarial P. falciparum parasitic strain.

Machine-learning approaches in drug discovery: methods and applications

During the past decade, virtual screening (VS) has evolved from traditional similarity searching, which utilizes single reference compounds, into an advanced application domain for data mining and machine-learning approaches, which require large and representative training-set compounds to learn robust decision rules. The explosive growth in the amount of public domain-available chemical and biological data has generated huge effort to design, analyze, and apply novel learning methodologies. Here, I focus on machine-learning techniques within the context of ligand-based VS (LBVS). In addition, I analyze several relevant VS studies from recent publications, providing a detailed view of the current state-of-the-art in this field and highlighting not only the problematic issues, but also the successes and opportunities for further advances.

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.

Synthesis and in vitro antitumor activity of new series of benzothiazole and pyrimido[2,1-b]benzothiazole derivatives

New series of benzothiazole and pyrimido[2,1-b]benzothiazole derivatives were synthesized and characterized by analytical and spectrometrical methods (IR, HRMS, (1)H and (13)C NMR). Nineteen of the synthesized compounds were selected by the National Cancer Institute (NCI), USA, to be screened for their antitumor activity at a single dose (10 μM) against a panel of 60 cancer cell lines. The most active compounds, 4, 6, 10, 14, 17 and 20 were selected for further evaluation at five dose level screening. Compounds 17 (GI50 = 0.44 μM, TGI = 1.2 μM and LC50 MG-MID = 6.6 μM) and 4 (GI50 = 0.77 μM, TGI = 2.08 μM and LC50 MG-MID = 11.74 μM) were proved to be the most active members in this study. 3D and 2D pharmacophoric maps for the structural features of both compounds were studied.

Composition and applications of focus libraries to phenotypic assays

The wealth of bioactivity information now available on low-molecular weight compounds has enabled a paradigm shift in chemical biology and early phase drug discovery efforts. Traditionally chemical libraries have been most commonly employed in screening approaches where a bioassay is used to characterize a chemical library in a random search for active samples. However, robust curating of bioassay data, establishment of ontologies enabling mining of large chemical biology datasets, and a wealth of public chemical biology information has made possible the establishment of highly annotated compound collections. Such annotated chemical libraries can now be used to build a pathway/target hypothesis and have led to a new view where chemical libraries are used to characterize a bioassay. In this article we discuss the types of compounds in these annotated libraries composed of tools, probes, and drugs. As well, we provide rationale and a few examples for how such libraries can enable phenotypic/forward chemical genomic approaches. As with any approach, there are several pitfalls that need to be considered and we also outline some strategies to avoid these.

Fast similarity searching and screening hit analysis

Similarity searching allows a fast identification of analogues to biologically active molecules. Depending on the applied similarity metrics, either structurally close analogues or more diverse compounds can be identified. This is of particular interest for the analysis of high-throughput screening (HTS) hits. A combination of similarity searching and data mining applied to HTS data derives early structure-activity relationships to guide a subsequent optimization of hits.:

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Macro and small over micro: macromolecules and small molecules that regulate microRNAs

Given the correlation between the deregulation of specific miRNAs and disease onset, it is critical to identify miRNA regulators that effectively control miRNAs involved in the pathogenesis of target diseases. This review provides the latest update on oligonucleotide- and small-molecule-based miRNA regulators, and discusses assays developed to screen for small-molecule regulators.

Molecular similarity in medicinal chemistry

Similarity is a subjective and multifaceted concept, regardless of whether compounds or any other objects are considered. Despite its intrinsically subjective nature, attempts to quantify the similarity of compounds have a long history in chemical informatics and drug discovery. Many computational methods employ similarity measures to identify new compounds for pharmaceutical research. However, chemoinformaticians and medicinal chemists typically perceive similarity in different ways. Similarity methods and numerical readouts of similarity calculations are probably among the most misunderstood computational approaches in medicinal chemistry. Herein, we evaluate different similarity concepts, highlight key aspects of molecular similarity analysis, and address some potential misunderstandings. In addition, a number of practical aspects concerning similarity calculations are discussed.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Visualization of Protein 3D Structures in ‘Double-Centroid’ Reduced Representation

This article is of two parts: (a) the development of a protein reduced representation and its implementation in a Web server; and (b) the use of the reduced protein representation in the modeling of the binding site of a given ligand and the screening for the model in other protein 3D structures. Current methods of reduced protein 3D structure representation such as the Ca trace method not only lack essential molecular detail, but also ignore the chemical properties of the component amino acid side chains. This chapter describes a reduced protein 3D structure representation called “double-centroid reduced representation” and presents a visualization tool called the “DCRR Web Server” that graphically displays a protein 3D structure in DCRR along with non-covalent intra- and intermolecular hydrogen bonding and van der Waals interactions. In the DCRR model, each amino acid residue is represented as two points: the centroid of the backbone atoms and that of the side chain atoms; in the visualization Web server, they and the non-bonded interactions are color-coded for easy identification. The visualization tool in this chapter is implemented in MATLAB and is the first for a reduced protein representation as well as one that simultaneously displays non-covalent interactions in the molecule. The DCRR model reduces the atomicity of the protein structure by ~75% while capturing the essential chemical properties of the component amino acids. The second half of this chapter describes the application of this reduced representation to the modeling and screening of ligand binding sites using a data model termed the “tetrahedral motif.” This type of ligand binding site modeling and screening presents a novel type of pharmacophore modeling and screening, one that depends on a reduced protein representation.

Biodiversity of small molecules--a new perspective in screening set selection

How is the 'diversity' of a compound set defined and how is the most appropriate compound subset identified for assay when screening the entire HTS deck is not an option? A common approach has so far been to cover as much of the chemical space as possible by screening a chemically diverse set of compounds. We show that, rather than chemical diversity, the biologic diversity of a compound library is an essential requirement for hit identification. We describe a simple and efficient approach for the design of a HTS library based on compound-target diversity. Biodiverse compound subsets outperform chemically diverse libraries regarding hit rate and the total number of unique chemical scaffolds present among hits. Specifically, by screening ~19% of a HTS collection, we expect to discover ~50-80% of all desired bioactive compounds.

Redefining Cheminformatics with Intuitive Collaborative Mobile Apps

The proliferation of mobile devices such as smartphones and tablet computers has recently been extended to include a growing ecosystem of increasingly sophisticated chemistry software packages, commonly known as apps. The capabilities that these apps can offer to the practicing chemist are approaching those of conventional desktop-based software, but apps tend to be focused on a relatively small range of tasks. To overcome this, chemistry apps must be able to seamlessly transfer data to other apps, and through the network to other devices, as well as to other platforms, such as desktops and servers, using documented file formats and protocols whenever possible. This article describes the development and state of the art with regard to chemistry-aware apps that make use of facile data interchange, and some of the scenarios in which these apps can be inserted into a chemical information workflow to increase productivity. A selection of contemporary apps is used to demonstrate their relevance to pharmaceutical research. Mobile apps represent a novel approach for delivery of cheminformatics tools to chemists and other scientists, and indications suggest that mobile devices represent a disruptive technology for drug discovery, as they have been to many other industries.

ZINCPharmer: pharmacophore search of the ZINC database

ZINCPharmer (http://zincpharmer.csb.pitt.edu) is an online interface for searching the purchasable compounds of the ZINC database using the Pharmer pharmacophore search technology. A pharmacophore describes the spatial arrangement of the essential features of an interaction. Compounds that match a well-defined pharmacophore serve as potential lead compounds for drug discovery. ZINCPharmer provides tools for constructing and refining pharmacophore hypotheses directly from molecular structure. A search of 176 million conformers of 18.3 million compounds typically takes less than a minute. The results can be immediately viewed, or the aligned structures may be downloaded for off-line analysis. ZINCPharmer enables the rapid and interactive search of purchasable chemical space.