Comparison of topological descriptors for similarity-based virtual screening using multiple bioactive reference structures

This paper reports a detailed comparison of a range of different types of 2D fingerprints when used for similarity-based virtual screening with multiple reference structures. Experiments with the MDL Drug Data Report database demonstrate the effectiveness of fingerprints that encode circular substructure descriptors generated using the Morgan algorithm. These fingerprints are notably more effective than fingerprints based on a fragment dictionary, on hashing and on topological pharmacophores. The combination of these fingerprints with data fusion based on similarity scores provides both an effective and an efficient approach to virtual screening in lead-discovery programmes

Association of a de novo 16q copy number variant with a phenotype that overlaps with Lenz microphthalmia and Townes-Brocks syndromes

<p>Abstract</p> <p>Background</p> <p>Anophthalmia and microphthalmia are etiologically and clinically heterogeneous. Lenz microphthalmia is a syndromic form that is typically inherited in an X-linked pattern, though the causative gene mutation is unknown. Townes-Brocks syndrome manifests thumb anomalies, imperforate anus, and ear anomalies. We present a 13-year-old boy with a syndromic microphthalmia phenotype and a clinical diagnosis of Lenz microphthalmia syndrome.</p> <p>Case Presentation</p> <p>The patient was subjected to clinical and molecular evaluation, including array CGH analysis. The clinical features included left clinical anophthalmia, right microphthalmia, anteriorly placed anus with fistula, chordee, ventriculoseptal defect, patent ductus arteriosus, posteriorly rotated ears, hypotonia, growth retardation with delayed bone age, and mental retardation. The patient was found to have an approximately 5.6 Mb deletion of 16q11.2q12.1 by microarray based-comparative genomic hybridization, which includes the <it>SALL1 </it>gene, which causes Townes-Brocks syndrome.</p> <p>Conclusions</p> <p>Deletions of 16q11.2q12.2 have been reported in several individuals, although those prior reports did not note microphthalmia or anophthalmia. This region includes <it>SALL1</it>, which causes Townes-Brocks syndrome. In retrospect, this child has a number of features that can be explained by the <it>SALL1 </it>deletion, although it is not clear if the microphthalmia is a rare feature of Townes-Brocks syndrome or caused by other mechanisms. These data suggest that rare copy number changes may be a cause of syndromic microphthalmia allowing a personalized genomic medicine approach to the care of patients with these aberrations.</p

Shaping a screening file for maximal lead discovery efficiency and effectiveness: elimination of molecular redundancy

High Throughput Screening (HTS) is a successful strategy for finding hits and leads that have the opportunity to be converted into drugs. In this paper we highlight novel computational methods used to select compounds to build a new screening file at Pfizer and the analytical methods we used to assess their quality. We also introduce the novel concept of molecular redundancy to help decide on the density of compounds required in any region of chemical space in order to be confident of running successful HTS campaigns

Analysis of in vitro bioactivity data extracted from drug discovery literature and patents: Ranking 1654 human protein targets by assayed compounds and molecular scaffolds

<p>Abstract</p> <p>Background</p> <p>Since the classic Hopkins and Groom druggable genome review in 2002, there have been a number of publications updating both the hypothetical and successful human drug target statistics. However, listings of research targets that define the area between these two extremes are sparse because of the challenges of collating published information at the necessary scale. We have addressed this by interrogating databases, populated by expert curation, of bioactivity data extracted from patents and journal papers over the last 30 years.</p> <p>Results</p> <p>From a subset of just over 27,000 documents we have extracted a set of compound-to-target relationships for biochemical <it>in vitro </it>binding-type assay data for 1,736 human proteins and 1,654 gene identifiers. These are linked to 1,671,951 compound records derived from 823,179 unique chemical structures. The distribution showed a compounds-per-target average of 964 with a maximum of 42,869 (Factor Xa). The list includes non-targets, failed targets and cross-screening targets. The top-278 most actively pursued targets cover 90% of the compounds. We further investigated target ranking by determining the number of molecular frameworks and scaffolds. These were compared to the compound counts as alternative measures of chemical diversity on a per-target basis.</p> <p>Conclusions</p> <p>The compounds-per-protein listing generated in this work (provided as a supplementary file) represents the major proportion of the human drug target landscape defined by published data. We supplemented the simple ranking by the number of compounds assayed with additional rankings by molecular topology. These showed significant differences and provide complementary assessments of chemical tractability.</p

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism

Small molecule drugs target many core metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism. These maps predict the “effect space” comprising likely target enzymes for each of the 246 MDDR drug classes in humans. The online resource also provides species-specific interactive drug-metabolism maps for each of the 385 model organisms and pathogens in the BioCyc database collection. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. The metabolic maps enable interactive navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism

Structure-based classification and ontology in chemistry

<p>Abstract</p> <p>Background</p> <p>Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving <it>relevant </it>results from the available information, and <it>organising </it>those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies.</p> <p>Results</p> <p>We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches.</p> <p>Conclusion</p> <p>Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research.</p

ICF, An Immunodeficiency Syndrome: DNA Methyltransferase 3B Involvement, Chromosome Anomalies, and Gene Dysregulation

The immunodeficiency, centromeric region instability, and facial anomalies syndrome (ICF) is the only disease known to result from a mutated DNA methyltransferase gene, namely, DNMT3B. Characteristic of this recessive disease are decreases in serum immunoglobulins despite the presence of B cells and, in the juxtacentromeric heterochromatin of chromosomes 1 and 16, chromatin decondensation, distinctive rearrangements, and satellite DNA hypomethylation. Although DNMT3B is involved in specific associations with histone deacetylases, HP1, other DNMTs, chromatin remodelling proteins, condensin, and other nuclear proteins, it is probably the partial loss of catalytic activity that is responsible for the disease. In microarray experiments and real-time RT-PCR assays, we observed significant differences in RNA levels from ICF vs. control lymphoblasts for pro- and anti-apoptotic genes (BCL2L10, CASP1, and PTPN13); nitrous oxide, carbon monoxide, NF-κB, and TNFa signalling pathway genes (PRKCH, GUCY1A3, GUCY1B3, MAPK13; HMOX1, and MAP4K4); and transcription control genes (NR2F2 and SMARCA2). This gene dysregulation could contribute to the immunodeficiency and other symptoms of ICF and might result from the limited losses of DNA methylation although ICF-related promoter hypomethylation was not observed for six of the above examined genes. We propose that hypomethylation of satellite 2at1qh and 16qh might provoke this dysregulation gene expression by trans effects from altered sequestration of transcription factors, changes in nuclear architecture, or expression of noncoding RNAs

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