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Can traditional computational analysis and machine learning help compensate for inadequate peer review of drug-repurposing papers in the context of an infodemic?

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Bioactive molecules such as drugs, pesticides and food additives are produced in large numbers by many commercial and academic groups around the world. Enormous quantities of data are generated on the biological properties and quality of these molecules. Access to such data - both on licensed and commercially available compounds, and also on those that fail during development - is crucial for understanding how improved molecules could be developed. For example, computational analysis of aggregated data on molecules that are investigated in drug discovery programmes has led to a greater understanding of the properties of successful drugs. However, the information required to perform these analyses is rarely published, and when it is made available it is often missing crucial data or is in a format that is inappropriate for efficient data-mining. Here, we propose a solution: the definition of reporting guidelines for bioactive entities - the Minimum Information About a Bioactive Entity (MIABE) - which has been developed by representatives of pharmaceutical companies, data resource providers and academic groups.

Nature Reviews Drug Discovery (2018); 10.1038/nrd.2018.14 In the version of this article that was originally published online, an older version of the data set categorizing proteins into target development levels was used to create Figure 1 than the version used to create Table 1, and data from Figure 1 were referred to at several points in the text of the article.

The increasing availability of genomic data for pathogens that cause tropical diseases has created new opportunities for drug discovery and development. However, if the potential of such data is to be fully exploited, the data must be effectively integrated and be easy to interrogate. Here, we discuss the development of the TDRtargets.org database (http://tdrtargets.org), which encompasses extensive genetic, biochemical and pharmacological data related to tropical disease pathogens, as well as computationally predicted druggability for potential targets and compound desirability information. By allowing the integration and weighting of this information, this database aims to facilitate the identification and prioritisation of candidate drug targets for pathogens.

ChEMBL is a large-scale drug discovery database containing bioactivity information primarily extracted from scientific literature. Due to the medicinal chemistry focus of the journals from which data are extracted, the data are currently of most direct value in the field of human health research. However, many of the scientific use-cases for the current data set are equally applicable in other fields, such as crop protection research: for example, identification of chemical scaffolds active against a particular target or endpoint, the de-convolution of the potential targets of a phenotypic assay, or the potential targets/pathways for safety liabilities. In order to broaden the applicability of the ChEMBL database and allow more widespread use in crop protection research, an extensive data set of bioactivity data of insecticidal, fungicidal and herbicidal compounds and assays was collated and added to the database.

BackgroundLarge-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins.ResultsIn this implementation of our heuristic, ligand binding to protein targets from the ChEMBL database was mapped to structural domains as defined by profiles contained within the Pfam-A database. Our mapping suggests that the majority of assay targets within the current version of the ChEMBL database bind ligands through a small number of highly prevalent domains, and conversely the majority of Pfam domains sampled by our data play no currently established role in ligand binding. Validation studies, carried out firstly against Uniprot entries with expert binding-site annotation and secondly against entries in the wwPDB repository of crystallographic protein structures, demonstrate that our simple heuristic maps ligand binding to the correct domain in about 90 percent of all assessed cases. Using the mappings obtained with our heuristic, we have assembled ligand sets associated with each Pfam domain.ConclusionsSmall molecule binding has been mapped to Pfam-A domains of protein targets in the ChEMBL bioactivity database. The result of this mapping is an enriched annotation of small molecule bioactivity data and a grouping of activity classes following the Pfam-A specifications of protein domains. This is valuable for data-focused approaches in drug discovery, for example when extrapolating potential targets of a small molecule with known activity against one or few targets, or in the assessment of a potential target for drug discovery or screening studies.

Nat. Chem. Biol. 11, 536–541 (2015); published online 21 July 2015; corrected after print 16 September 2015. In the version of this Commentary initially published, there were several errors in the author list and author affiliations. Bryan Roth was incorrectly listed as “Brian Roth” and his correct affiliations are: The National Institute of Mental Health Psychoactive Active Drug Screening Program (NIMH PDSP), Department of Pharmacology, The University of North Carolina Chapel Hill School of Medicine, Chapel Hill, North Carolina, USA and Division of Chemical Biology and Medicinal Chemistry, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.

Classification of proteins by ligand binding similarity offers an alternative approach to evolutionary methods for organizing and understanding biology, allowing new insights into protein function and physiological signal transduction.

Knowledge of the tertiary structure of the proteinase from human immunodeficiency virus HIV-1 is important to the design of inhibitors that might possess antiviral activity and thus be useful in the treatment of AIDS. The conserved Asp-Thr/Ser-Gly sequence in retroviral proteinases suggests that they exist as dimers similar to the ancestor proposed for the pepsins. Although this has been confirmed by X-ray analyses of Rous sarcoma virus and HIV-1 proteinases, these structures have overall folds that are similar to each other only where they are also similar to the pepsins. We now report a further X-ray analysis of a recombinant HIV-1 proteinase at 2.7 A resolution. The polypeptide chain adopts a fold in which the N- and C-terminal strands are organized together in a four-stranded beta-sheet. A helix precedes the single C-terminal strand, as in the Rous sarcoma virus proteinase and also in a synthetic HIV-1 proteinase, in which the cysteines have been replaced by alpha-aminobuytric acid. The structure reported here provides an explanation for the amino acid invariance amongst retroviral proteinases, but differs from that reported earlier in some residues that are candidates for substrate interactions at P3, and in the mode of intramolecular cleavage during processing of the polyprotein.