Three-Dimensional Pharmacophoric Pattern Searching

Identification of Druggable Binding Sites and Small Molecules as Modulators of TMC1

Our ability to hear and maintain balance relies on the proper functioning of inner ear sensory hair cells, which translate mechanical stimuli into electrical signals via mechano-electrical transducer (MET) channels, composed of TMC1/2 proteins. However, the therapeutic use of ototoxic drugs, such as aminoglycosides and cisplatin, which can enter hair cells through MET channels, often leads to profound auditory and vestibular dysfunction. Despite extensive research on otoprotective compounds targeting MET channels, our understanding of how small molecule modulators interact with these channels remains limited, hampering the discovery of novel compounds. Here, we propose a structure-based screening approach, integrating 3D-pharmacophore modeling, molecular simulations, and experimental validation. Our pipeline successfully identified several novel compounds and FDA-approved drugs that reduced dye uptake in cultured cochlear explants, indicating MET modulation activity. Molecular docking and free-energy estimations for binding allowed us to identify three potential drug binding sites within the channel pore, phospholipids, and key amino acids involved in modulator interactions. We also identified shared ligand-binding features between TMC and structurally related TMEM16 protein families, providing novel insights into their distinct inhibition, while potentially guiding the rational design of MET-channel-specific modulators. Our pipeline offers a broad application to discover small molecule modulators for a wide spectrum of mechanosensitive ion channels.

DrugGen: a database of de novo-generated molecular binders for specified target proteins

De novo molecular generation is a promising approach to drug discovery, building novel molecules from the scratch that can bind the target proteins specifically. With the increasing availability of machine learning algorithms and computational power, artificial intelligence (AI) has emerged as a valuable tool for this purpose. Here, we have developed a database of 3D ligands that collects six AI models for de novo molecular generation based on target proteins, including 20 disease-associated targets. Our database currently includes 1767 protein targets and up to 164 107 de novo-designed molecules. The primary goal is to provide an easily accessible and user-friendly molecular database for professionals in the fields of bioinformatics, pharmacology and related areas, enabling them to efficiently screen for potential lead compounds with biological activity. Additionally, our database provides a comprehensive resource for computational scientists to explore and compare different AI models in terms of their performance in generating novel molecules with desirable properties. All the resources and services are publicly accessible at https://cmach.sjtu.edu.cn/drug/. Database URL: https://cmach.sjtu.edu.cn/drug/.

ELIXIR-A: An Interactive Visualization Tool for Multi-Target Pharmacophore Refinement

Pharmacophore modeling is an important step in computer-aided drug design for identifying interaction points between the receptor and ligand complex. Pharmacophore-based models can be used for de novo drug design, lead identification, and optimization in virtual screening as well as for multi-target drug design. There is a need to develop a user-friendly interface to filter the pharmacophore points resulting from multiple ligand conformations. Here, we present ELIXIR-A, a Python-based pharmacophore refinement tool, to help refine the pharmacophores between multiple ligands from multiple receptors. Furthermore, the output can be easily used in virtual pharmacophore-based screening platforms, thereby contributing to the development of drug discovery.

Repurposing of renin inhibitors as SARS-COV-2 main protease inhibitors: A computational study

The COVID-19 pandemic has urged for the repurposing of existing drugs for rapid management and treatment. Renin inhibitors down regulation of ACE2, which is an essential receptor for SARS-CoV-2 infection that is responsible for COVID-19, in addition to their ability to act as protease inhibitors were encouraging aspects for their investigation as possible inhibitors of main protease of SARS-CoV-2 via computational studies. A Pharmacophore model was generated using the newly released SARS-COV-2 main protease inhibitors. Virtual screening was performed on renin inhibitors, and Drug likeness filter identified remikiren and 0IU as hits. Molecular docking for both compounds showed that the orally active renin inhibitor remikiren (Ro 42–5892) of Hoffmann–La Roche exhibited good molecular interaction with Cys145 and His41 in the catalytic site of SARS-CoV-2 main protease. Molecular dynamics simulation suggested that the drug is stable in the active site of the enzyme.

Tree-Based QSAR Model for Drug Repurposing in the Discovery of New Antibacterial Compounds Against Escherichia coli

Drug repurposing appears as an increasing popular tool in the search of new treatment options against bacteria. In this paper, a tree-based classification method using Linear Discriminant Analysis (LDA) and discrete indexes was used to create a QSAR (Quantitative Structure-Activity Relationship) model to predict antibacterial activity against Escherichia coli. The model consists on a hierarchical decision tree in which a discrete index is used to divide compounds into groups according to their values for said index in order to construct probability spaces. The second step consists in the calculation of a discriminant function which determines the prediction of the model. The model was used to screen the DrugBank database, identifying 134 drugs as possible antibacterial candidates. Out of these 134 drugs, 8 were antibacterial drugs, 67 were drugs approved for different pathologies and 55 were drugs in experimental stages. This methodology has proven to be a viable alternative to the traditional methods used to obtain prediction models based on LDA and its application provides interesting new drug candidates to be studied as repurposed antibacterial treatments. Furthermore, the topological indexes Nclass and Numhba have proven to have the ability to group active compounds effectively, which suggests a close relationship between them and the antibacterial activity of compounds against E. coli.

A Review on Applications of Computational Methods in Drug Screening and Design

Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure- and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.

Receptor-based pharmacophore modeling, virtual screening, and molecular docking studies for the discovery of novel GSK-3β inhibitors

Considering the emerging importance of glycogen synthase kinase 3 beta (GSK-3β) inhibitors in treatment of Alzheimer's disease, multi-protein structure receptor-based pharmacophore modeling was adopted to generate a 3D pharmacophore model for (GSK-3β) inhibitors. The generated 3D pharmacophore was then validated using a test set of 1235 compounds. The ZINCPharmer web tool was used to virtually screen the public ZINC database using the generated 3D pharmacophore. A set of 12,251 hits was produced and then filtered according to their lead-like properties, predicted central nervous system (CNS) activity, and Pan-assay interference compounds (PAINS) fragments to 630 compounds. Scaffold Hunter was then used to cluster the filtered compounds according to their chemical structure framework. From the different clusters, 123 compounds were selected to cover the whole chemical space of the obtained hits. The SwissADME online tool was then used to filter out the compounds with undesirable pharmacokinetic properties giving a set of 91 compounds with promising predicted pharmacodynamic and pharmacokinetic properties. To confirm their binding capability to the GSK-3β binding site, molecular docking simulations were performed for the final 91 compounds in the GSK-3β binding site. Twenty-five compounds showed acceptable binding poses that bind to the key amino acids in the binding site Asp133 and Val135 with good binding scores. The quinolin-2-one derivative ZINC67773573 was found to be a promising lead for designing new GSK-3β inhibitors for Alzheimer's disease treatment. Graphical abstract A 3D pharmacophore model for the discovery of novel (GSK-3β) inhibitors.

Truly Target-Focused Pharmacophore Modeling: A Novel Tool for Mapping Intermolecular Surfaces

Pharmacophore models are an accurate and minimal tridimensional abstraction of intermolecular interactions between chemical structures, usually derived from a group of molecules or from a ligand-target complex. Only a limited amount of solutions exists to model comprehensive pharmacophores using the information of a particular target structure without knowledge of any binding ligand. In this work, an automated and customable tool for truly target-focused (T²F) pharmacophore modeling is introduced. Key molecular interaction fields of a macromolecular structure are calculated using the AutoGRID energy functions. The most relevant points are selected by a newly developed filtering cascade and clustered to pharmacophore features with a density-based algorithm. Using five different protein classes, the ability of this method to identify essential pharmacophore features was compared to structure-based pharmacophores derived from ligand-target interactions. This method represents an extremely valuable instrument for drug design in a situation of scarce ligand information available, but also in the case of underexplored therapeutic targets, as well as to investigate protein allosteric pockets and protein-protein interactions.

Vitamin C Status and Cognitive Function: A Systematic Review

Vitamin C plays a role in neuronal differentiation, maturation, myelin formation and modulation of the cholinergic, catecholinergic, and glutaminergic systems. This review evaluates the link between vitamin C status and cognitive performance, in both cognitively intact and impaired individuals. We searched the PUBMED, SCOPUS, SciSearch and the Cochrane Library from 1980 to January 2017, finding 50 studies, with randomised controlled trials (RCTs, n = 5), prospective (n = 24), cross-sectional (n = 17) and case-control (n = 4) studies. Of these, 36 studies were conducted in healthy participants and 14 on cognitively impaired individuals (including Alzheimer’s and dementia). Vitamin C status was measured using food frequency questionnaires or plasma vitamin C. Cognition was assessed using a variety of tests, mostly the Mini-Mental-State-Examination (MMSE). In summary, studies demonstrated higher mean vitamin C concentrations in the cognitively intact groups of participants compared to cognitively impaired groups. No correlation between vitamin C concentrations and MMSE cognitive function was apparent in the cognitively impaired individuals. The MMSE was not suitable to detect a variance in cognition in the healthy group. Analysis of the studies that used a variety of cognitive assessments in the cognitively intact was beyond the scope of this review; however, qualitative assessment revealed a potential association between plasma vitamin C concentrations and cognition. Due to a number of limitations in these studies, further research is needed, utilizing plasma vitamin C concentrations and sensitive cognitive assessments that are suitable for cognitively intact adults.

Molecular Similarity Concepts for Informatics Applications

The assessment of small molecule similarity is a central task in chemoinformatics and medicinal chemistry. A variety of molecular representations and metrics are applied to computationally evaluate and quantify molecular similarity. A critically important aspect of molecular similarity analysis in chemoinformatics and pharmaceutical research is that one is typically not interested in quantifying the degree of structural or chemical similarity between compounds per se, but rather in extrapolating from molecular similarity to property similarity. In other words, one assumes that there is a correlation between calculated similarity and specific properties of small molecules including, first and foremost, biological activities. Although similarity is a priori a subjective concept, and difficult to quantify, it must computationally be assessed in a formally consistent manner. Otherwise, there is little utility of similarity calculations. Consistent treatment requires approximations to be made and the consideration of alternative computational similarity concepts, as discussed herein.

Computational Approaches for the Discovery of Human Proteasome Inhibitors: An Overview

Proteasome emerged as an important target in recent pharmacological research due to its pivotal role in degrading proteins in the cytoplasm and nucleus of eukaryotic cells, regulating a wide variety of cellular pathways, including cell growth and proliferation, apoptosis, DNA repair, transcription, immune response, and signaling processes. The last two decades witnessed intensive efforts to discover 20S proteasome inhibitors with significant chemical diversity and efficacy. To date, the US FDA approved to market three proteasome inhibitors: bortezomib, carfilzomib, and ixazomib. However new, safer and more efficient drugs are still required. Computer-aided drug discovery has long being used in drug discovery campaigns targeting the human proteasome. The aim of this review is to illustrate selected in silico methods like homology modeling, molecular docking, pharmacophore modeling, virtual screening, and combined methods that have been used in proteasome inhibitors discovery. Applications of these methods to proteasome inhibitors discovery will also be presented and discussed to raise improvements in this particular field.

Chemoinformatics Research at the University of Sheffield: A History and Citation Analysis

This paper reviews the work of the Chemoinformatics Research Group in the Department of Information Studies at the University of Sheffield, focusing particularly on the work carried out in the period 1985-2002. Four major research areas are discussed, these involving the development of methods for: substructure searching in databases of three-dimensional structures, including both rigid and flexible molecules; the representation and searching of the Markush structures that occur in chemical patents; similarity searching in databases of both two-dimensional and three-dimensional structures; and compound selection and the design of combinatorial libraries. An analysis of citations to 321 publications from the Group shows that it attracted a total of 3725 residual citations during the period 1980-2002. These citations appeared in 411 different journals, and involved 910 different citing organizations from 54 different countries, thus demonstrating the widespread impact of the Group's work.

Setting the record straight: the origin of the pharmacophore concept

For over a century since the early 1900s, Paul Ehrlich was credited with originating the concept of pharmacophores. This was challenged by John Van Drie in 2007 due to the fact that Ehrlich did not use the word "pharmacophore" in his writings. Van Drie claimed that the attribution of the pharmacophore concept to Ehrlich was due to an erroneous citation made by Ariëns in a 1966 paper, and instead he claimed, Lemont B. Kier developed the pharmacophore concept (in the modern sense, as defined by the IUPAC) during 1967-1971. There are two separate issues that may have triggered this conflict. The first one is the shift in the meaning of pharmacophore from "chemical groups" to patterns of "abstract features" of a molecule that are responsible for a biological effect. Indeed, the original use of the term is different than the current definition proposed by the IUPAC. The term was redefined in 1960 by Schueler, and this modification formed the basis of IUPAC's modern definition. The second issue is the origin of the "concept" of pharmacophore. While Ehrlich's contemporaries have consistently attributed the origin of the concept to him, the issue is further complicated by the fact that Ehrlich did not use the term pharmacophore in his papers. He, instead, referred to the features of a molecule that are responsible for biological effects as toxophores, while his contemporaries were using the term pharmacophore for the same features. In this paper, we resolve any doubts about the origins of the pharmacophore concept. Our research points to Paul Ehrlich's 1898 paper for originating the concept, which identifies peripheral chemical groups in molecules responsible for binding that leads to the subsequent biological effect, and to Schueler's 1960 book that extends the concept to the modern definition where spatial patterns of abstract features of a molecule define the pharmacophore and are ultimately responsible for the biological effect.

Insights into cardiovascular effects of proline-rich oligopeptide (Bj-PRO-10c) revealed by structure-activity analyses: dissociation of antihypertensive and bradycardic effects

We have previously reported that the proline-rich decapeptide from Bothrops jararaca (Bj-PRO-10c) causes potent and sustained antihypertensive and bradycardic effects in SHR. These activities are independent of ACE inhibition. In the present study, we used the Ala-scan approach to evaluate the importance of each amino acid within the sequence of Bj-PRO-10c (Pyr(1)-Asn(2)-Trp(3)-Pro(4)-His(5)-Pro(6)-Gln(7)-Ile(8)-Pro(9)-Pro(10)). The antihypertensive and bradycardic effects of the analogues Bj-PRO-10c Ala(3), Bj-PRO-10c Ala(7), Bj-PRO-10c Ala(8) were similar to those of Bj-PRO-10c, whereas the analogues Bj-PRO-10c Ala(2), Bj-PRO-10c Ala(4), Bj-PRO-10c Ala(5), Bj-PRO-10c Ala(9), and Bj-PRO-10c Ala(10) kept the antihypertensive activity and lost bradycardic activity considerably. In contrast, Bj-PRO-10c Ala(1) and Bj-PRO-10c Ala(6) were unable to provoke any cardiovascular activity. In summary, we demonstrated that (1) the Pyr(1) and Pro(6) residues are essential for both, the antihypertensive and bradycardic effects of Bj-PRO-10c; (2) Ala-scan approach allowed dissociating blood pressure reduction and bradycardic effects. Conformational properties of the peptides were examined by means of circular dichroism (CD) spectroscopy. The different Ala-scan analogues caused either an increase or decrease in the type II polyproline helix content compared to Bj-PRO-10c. The complete loss of activity of the Pro(6) → Ala(6) mutant is probably due to the fact that in the parent peptide the His(5)-Pro(6) bond can exist in the cis configuration, which could correspond to the conformation of this bond in the bound state. Current data support the Bj-PRO-10c as a promising leader prototype to develop new agents to treat cardiovascular diseases and its co-morbidities.

From the protein's perspective: the benefits and challenges of protein structure-based pharmacophore modeling

Protein structure-based pharmacophore (SBP) models derive the molecular features a ligand must contain to be biologically active by conversion of protein properties to reciprocal ligand space. SBPs improve molecular understanding of ligand–protein interactions and can be used as valuable tools for hit and lead optimization, compound library design, and target hopping.

Rational ligand-based virtual screening and structure-activity relationship studies in the ligand-binding domain of the glucocorticoid receptor-α

The interest in developing synthetic glucocorticoids (GCs) arises from the utility of endogenous steroids as potent anti-inflammatory and immunosuppressant agents. The first GCs to be discovered, such as cortisol or dexamethasone, still represent the main treatment for conditions of the inflammatory process, despite the fact that they carry a significant risk of side effects. Hence, there is a continuing need to find drugs that preserve the immune effects of GCs without the side effects, such as those on metabolism (diabetes), bone tissue (osteoporosis), muscles (myopathy), eyes and skin. In this review, we focus on the recent use of ligand-based computational approaches in glucocorticoid receptor (GR) drug-design efforts for the determination of novel GR ligands. We examine a number of ligand-based (similarity searches, pharmacophore screens and quantitative structure-activity relationships) approaches that have been implemented in recent years. A recent virtual high-throughput screening similarity search was successful in developing a novel series of nonsteroidal GR antagonists. Additionally, there has been considerable success in ligand-based structure-analysis relationship generation and lead optimization studies for the GR. Future trends toward integrated GR ligand design incorporating ligand- and structure-based methodologies are inevitable.

Mangiferin, an anti-HIV-1 agent targeting protease and effective against resistant strains

The anti-HIV-1 activity of mangiferin was evaluated. Mangiferin can inhibit HIV-1_ⅢB induced syncytium formation at non-cytotoxic concentrations, with a 50% effective concentration (EC₅₀) at 16.90 μM and a therapeutic index (TI) above 140. Mangiferin also showed good activities in other laboratory-derived strains, clinically isolated strains and resistant HIV-1 strains. Mechanism studies revealed that mangiferin might inhibit the HIV-1 protease, but is still effective against HIV peptidic protease inhibitor resistant strains. A combination of docking and pharmacophore methods clarified possible binding modes of mangiferin in the HIV-1 protease. The pharmacophore model of mangiferin consists of two hydrogen bond donors and two hydrogen bond acceptors. Compared to pharmacophore features found in commercially available drugs, three pharmacophoric elements matched well and one novel pharmacophore element was observed. Moreover, molecular docking analysis demonstrated that the pharmacophoric elements play important roles in binding HIV-1 protease. Mangiferin is a novel nonpeptidic protease inhibitor with an original structure that represents an effective drug development strategy for combating drug resistance.

Elucidation of common pharmacophores from analysis of targeted metabolites transported by the multispecific drug transporter-Organic anion transporter1 (Oat1)

Organic anion transporter 1 (Oat1), first identified as NKT, is a multispecific transporter responsible for the handling of drugs and toxins in the kidney and choroid plexus, but its normal physiological role appears to be in small molecule metabolite regulation. Metabolites transported by Oat1 and which are altered in the blood and urine of the murine Oat1 knockout, may serve as templates for further drug design. This may lead to better tissue targeting of drugs or design of Oat1 inhibitors that prolong the half-life of current drugs. Due to the multispecificity of the transporter, 19 of known targeted metabolites have different chemical structures and properties that make constructing a common pharmacophore model difficult. Here we propose an approach that clustered the metabolites into four distinct groups which allowed for the construction of a consensus pharmacophore for each cluster. The screening of commercial molecular databases determined the top candidates whose interaction with Oat1 was confirmed in an experimental model of organic anion transport. Thus, these candidate selections represent potential molecules for further drug design.

Historical overview of chemical library design

High-throughput chemistry (HTC) is approaching its 20-year anniversary. Since 1992, some 5,000 chemical libraries, prepared for the purpose of biological investigation and drug discovery, have been published in the scientific literature. This review highlights the key events in the history of HTC with emphasis on library design. A historical perspective on the design of screening, targeted, and optimization libraries and their application is presented. Design strategies pioneered in the 1990s remain viable in the twenty-first century.

Large-scale systematic analysis of 2D fingerprint methods and parameters to improve virtual screening enrichments

A systematic virtual screening study on 11 pharmaceutically relevant targets has been conducted to investigate the interrelation between 8 two-dimensional (2D) fingerprinting methods, 13 atom-typing schemes, 13 bit scaling rules, and 12 similarity metrics using the new cheminformatics package Canvas. In total, 157 872 virtual screens were performed to assess the ability of each combination of parameters to identify actives in a database screen. In general, fingerprint methods, such as MOLPRINT2D, Radial, and Dendritic that encode information about local environment beyond simple linear paths outperformed other fingerprint methods. Atom-typing schemes with more specific information, such as Daylight, Mol2, and Carhart were generally superior to more generic atom-typing schemes. Enrichment factors across all targets were improved considerably with the best settings, although no single set of parameters performed optimally on all targets. The size of the addressable bit space for the fingerprints was also explored, and it was found to have a substantial impact on enrichments. Small bit spaces, such as 1024, resulted in many collisions and in a significant degradation in enrichments compared to larger bit spaces that avoid collisions.

Quantitative structure-activity relationships for cycloguanil analogs as PfDHFR inhibitors using mathematical molecular descriptors

Computed molecular descriptors were used to develop quantitative structure-activity relationships (QSARs) for binding affinities (K(i)) for a set of 58 cycloguanil (2,4-diamino-1,6-dihydro-1,3,5-triazine) analogues for dihydrofolate reductase (DHFR) enzyme extracted from wild and A16V+S108T mutant type (a double mutation) malaria parasite Plasmodium falciparum (Pf). High-quality models were obtained in both cases. The results of statistical analyses show that ridge regression (RR) outperformed the two other modelling methods, principal component regression (PCR) and partial least squares (PLS). For both enzymes, recognition of the inhibitors was based on four broad categories of descriptors encoding information on: (1) the electronic character of the various atoms in the molecule, (2) the size and shape of the structure, (3) the degree of branching in the molecular skeleton, and (4) two to five atom molecular fragments with aliphatic carbon at one end and aliphatic or aromatic carbon or nitrogen at the other end. The subsets of influential descriptors underlying the QSARs for the wild versus the mutant DHFR are quite non-overlapping. This indicates that the two enzymes recognize the inhibitor molecules on the basis of mutually distinct structural attributes. Such differential QSARs can be useful in the design of novel drugs active against malaria parasites which are growing in resistant to existing chemotherapeutic agents.

On the electronic structure of cocaine and its metabolites

This work aims at describing the electronic features of cocaine and how they are modified by the different substituents present in its metabolites. The QTAIM analysis of B3LYP and MP2 electron densities obtained with the 6-311++G** 6d basis set for cocaine and its principal metabolites indicates: (i) its positive charge is shared among the amino hydrogen, those of the methylamino group, and all of the hydrogens attached to the bicycle structure; (ii) the zwitterionic structure of benzoylecgonine can be described as two partial charges of 0.63 au, the negative one shared by the oxygens of the carboxylate group, whereas the positive charge is distributed among all the hydrogens that bear the positive charge in cocaine; (iii) its hydrogen bond is strengthened in the derivatives without benzoyloxy group and is also slightly strengthened as the size of the alkyl ester group at position 2 increases.

Computer simulated screening of dentin bonding primer monomers through analysis of their chemical functions and their spatial 3D alignment

Binding interactions between dentin bonding primer monomers and dentinal collagen were studied by an analysis of their chemical functions and their spatial 3D alignment. A trial set of 12 monomers used as primers in dentin adhesives was characterized to assess them for binding to a complementary target. HipHop utility in the Catalyst software from Accelrys was used for the study. Ten hypotheses were generated by HipHop procedures involving (a) conformational generation using a poling technique to promote conformational variation, (b) extraction of functions to remodel ligands as function-based structures, and (c) identification of common patterns of functional alignment displayed by low energy conformations. The hypotheses, designated as pharmacaphores, were also scored and ranked. Analysis of pharmacaphore models through mapping of ligands revealed important differences between ligands. Top-ranked poses from direct docking simulations using type 1 collagen target were mapped in a rigid manner to the highest ranked pharmacophore model. The visual match observed in mapping and associated fit values suggest a strong correspondence between direct and indirect docking simulations. The results elegantly demonstrate that an indirect approach used to identify pharmacaphore models from adhesive ligands without a target may be a simple and viable approach to assess their intermolecular interactions with an intended target. Inexpensive indirect/direct virtual screening of hydrophilic monomer candidates may be a practical way to assess their initial promise for dentin primer use well before additional experimental evaluation of their priming/bonding efficacy. This is also of value in the search/design of new compounds for priming dentin.

From chemical documentation to chemoinformatics: 50 years of chemical information science

This paper summarizes the historical development of the discipline that is now called `chemoinformatics'. It shows how this has evolved, principally as a result of technological developments in chemistry and biology during the past decade, from long-established techniques for the modelling and searching of chemical molecules. A total of 30 papers, the earliest dating back to 1957, are briefly summarized to highlight some of the key publications and to show the development of the discipline.

Is it possible to increase hit rates in structure-based virtual screening by pharmacophore filtering? An investigation of the advantages and pitfalls of post-filtering

We have investigated the influence of post-filtering virtual screening results, with pharmacophoric features generated from an X-ray structure, on enrichment rates. This was performed using three docking softwares, zdock+, Surflex and FRED, as virtual screening tools and pharmacophores generated in UNITY from co-crystallized complexes. Sets of known actives along with 9997 pharmaceutically relevant decoy compounds were docked against six chemically diverse protein targets namely CDK2, COX2, ERalpha, fXa, MMP3, and NA. To try to overcome the inherent limitations of the well-known docking problem, we generated multiple poses for each compound. The compounds were first ranked according to their scores alone and enrichment rates were calculated using only the top scoring pose of each compound. Subsequently, all poses for each compound were passed through the different pharmacophores generated from co-crystallized complexes and the enrichment factors were re-calculated based on the top-scoring passing pose of each compound. Post-filtering with a pharmacophore generated from only one X-ray complex was shown to increase enrichment rates in all investigated targets compared to docking alone. This indicates that this is a general method, which works for diverse targets and different docking softwares.

Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

Background

RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities.

Results

Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative.

Conclusion

The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at the following site: .

Pharmacophore-based discovery of ligands for drug transporters

The ability to identify ligands for drug transporters is an important step in drug discovery and development. It can both improve accurate profiling of lead pharmacokinetic properties and assist in the discovery of new chemical entities targeting transporters. In silico approaches, especially pharmacophore-based database screening methods have great potential in improving the throughput of current transporter ligand identification assays, leading to a higher hit rate by focusing in vitro testing to the most promising hits. In this review, the potential of different in silico methods in transporter ligand identification studies are compared and summarized with an emphasis on pharmacophore modeling. Various implementations of pharmacophore model generation, database compilation and flexible screening algorithms are also introduced. Recent successful utilization of database searching with pharmacophores to identify novel ligands for the pharmaceutically significant transporters hPepT1, P-gp, BCRP, MRP1 and DAT are reviewed and the challenges encountered with current approaches are discussed.