The FAIR Cookbook - the essential resource for and by FAIR doers.

The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst.

Linked Registries: Connecting Rare Diseases Patient Registries through a Semantic Web Layer.

Patient registries are an essential tool to increase current knowledge regarding rare diseases. Understanding these data is a vital step to improve patient treatments and to create the most adequate tools for personalized medicine. However, the growing number of disease-specific patient registries brings also new technical challenges.

The Implicitome: A Resource for Rationalizing Gene-Disease Associations.

High-throughput experimental methods such as medical sequencing and genome-wide association studies (GWAS) identify increasingly large numbers of potential relations between genetic variants and diseases. Both biological complexity (millions of potential gene-disease associations) and the accelerating rate of data production necessitate computational approaches to prioritize and rationalize potential gene-disease relations. Here, we use concept profile technology to expose from the biomedical literature both explicitly stated gene-disease relations (the explicitome) and a much larger set of implied gene-disease associations (the implicitome).

From Peer-Reviewed to Peer-Reproduced in Scholarly Publishing: The Complementary Roles of Data Models and Workflows in Bioinformatics.

Motivation: Reproducing the results from a scientific paper can be challenging due to the absence of data and the computational tools required for their analysis. In addition, details relating to the procedures used to obtain the published results can be difficult to discern due to the use of natural language when reporting how experiments have been performed. The Investigation/Study/Assay (ISA), Nanopublications (NP), and Research Objects (RO) models are conceptual data modelling frameworks that can structure such information from scientific papers.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

Background: The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task).

Multi-objective evolutionary design of adenosine receptor ligands.

A novel multiobjective evolutionary algorithm (MOEA) for de novo design was developed and applied to the discovery of new adenosine receptor antagonists. This method consists of several iterative cycles of structure generation, evaluation, and selection. We applied an evolutionary algorithm (the so-called Molecule Commander) to generate candidate A1 adenosine receptor antagonists, which were evaluated against multiple criteria and objectives consisting of high (predicted) affinity and selectivity for the receptor, together with good ADMET properties.

A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design.

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models.

Substructure-based virtual screening for adenosine A2A receptor ligands.

A virtual ligand-based screening approach was designed and evaluated for the discovery of new A(2A) adenosine receptor (AR) ligands. For comparison and evaluation, the procedures from a recently published virtual screening study that used the A(2A) AR X-ray crystal structure for the target-based discovery of new A(2A) ligands were largely followed. Several screening models were constructed by deriving the distinguishing structural features from selected sets of A(2A) AR antagonists, so-called frequent substructure mining.

Chemogenomics approaches for receptor deorphanization and extensions of the chemogenomics concept to phenotypic space.

Chemogenomic approaches, which link ligand chemistry to bioactivity against targets (and, by extension, to phenotypes) are becoming more and more important due to the increasing number of bioactivity data available both in proprietary databases as well as in the public domain. In this article we review chemogenomics approaches applied in four different domains: Firstly, due to the relationship between protein targets from which an approximate relation between their respective bioactive ligands can be inferred, we investigate the extent to which chemogenomics approaches can be applied to receptor deorphanization. In this case it was found that by using knowledge about active compounds of related proteins, in 93% of all cases enrichment better than random could be obtained.

A novel chemogenomics analysis of G protein-coupled receptors (GPCRs) and their ligands: a potential strategy for receptor de-orphanization.

Background: G protein-coupled receptors (GPCRs) represent a family of well-characterized drug targets with significant therapeutic value. Phylogenetic classifications may help to understand the characteristics of individual GPCRs and their subtypes. Previous phylogenetic classifications were all based on the sequences of receptors, adding only minor information about the ligand binding properties of the receptors.

Substructure mining of GPCR ligands reveals activity-class specific functional groups in an unbiased manner.

In this study, we conducted frequent substructure mining to identify structural features that discriminate between ligands that do bind to G protein-coupled receptors (GPCRs) and those that do not. In most cases, particular chemical representations resulted in the most significant substructures. Substructures found to be characteristic for the background control set reflected reactions that may have been used to construct this library, e.