Biofilms are multicellular communities of microorganisms that generally attach to surfaces in a self-produced matrix. Unlike planktonic cells, biofilms can withstand conventional antibiotics, causing significant challenges in the healthcare system. Currently, new chemical entities are urgently needed to develop novel anti-biofilm agents. In this study, we designed and synthesized a set of 2,4,5,6-tetrasubstituted pyrimidines and assessed their antibacterial activity against planktonic cells and biofilms formed by Staphylococcus aureus. Compounds 9e, 10d, and 10e displayed potent activity for inhibiting the onset of biofilm formation as well as for killing pre-formed biofilms of S. aureus ATCC 25923 and Newman strains, with half-maximal inhibitory concentration (IC50) values ranging from 11.6 to 62.0 µM. These pyrimidines, at 100 µM, not only decreased the number of viable bacteria within the pre-formed biofilm by 2–3 log10 but also reduced the amount of total biomass by 30–50%. Furthermore, these compounds were effective against planktonic cells with minimum inhibitory concentration (MIC) values lower than 60 µM for both staphylococcal strains. Compound 10d inhibited the growth of S. aureus ATCC 25923 in a concentration-dependent manner and displayed a bactericidal anti-staphylococcal activity. Taken together, our study highlights the value of multisubstituted pyrimidines to develop novel anti-biofilm agents.

Background The disposition of a pharmaceutical compound within an organism, i.e. its Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) properties and adverse effects, critically affects late stage failure of drug candidates and has led to the withdrawal of approved drugs. Computational methods are effective approaches to reduce the number of safety issues by analyzing possible links between chemical structures and ADMET or adverse effects, but this is limited by the size, quality, and heterogeneity of the data available from individual sources. Thus, large, clean and integrated databases of approved drug data, associated with fast and efficient predictive tools are desirable early in the drug discovery process.Description We have built a relational database (IDAAPM) to integrate available approved drug data such as drug approval information, ADMET and adverse effects, chemical structures and molecular descriptors, targets, bioactivity and related references. The database has been coupled with a searchable web interface and modern data analytics platform (KNIME) to allow data access, data transformation, initial analysis and further predictive modeling. Data were extracted from FDA resources and supplemented from other publicly available databases. Currently, the database contains information regarding about 19,226 FDA approval applications for 31,815 products (small molecules and biologics) with their approval history, 2505 active ingredients, together with as many ADMET properties, 1629 molecular structures, 2.5 million adverse effects and 36,963 experimental drug-target bioactivity data.ConclusionIDAAPM is a unique resource that, in a single relational database, provides detailed information on FDA approved drugs including their ADMET properties and adverse effects, the corresponding targets with bioactivity data, coupled with a data analytics platform. It can be used to perform basic to complex drug-target ADMET or adverse effects analysis and predictive modeling. IDAAPM is freely accessible at http://idaapm.helsinki.fi and can be exploited through a KNIME workflow connected to the database.Graphical abstractFDA approved drug data integration for predictive modelingElectronic supplementary materialThe online version of this article (doi:10.1186/s13321-016-0141-7) contains supplementary material, which is available to authorized users.

Cooperative molecular contacts play an important role in protein structure and ligand binding. Here, we constructed a PostgreSQL database that stores structural information in the form of atomic environments and allows flexible mining of molecular contacts. Taking the Ser‐His‐Asp/Glu catalytic triad as a first test case, we demonstrate that the presence of a carboxylate oxygen atom in the vicinity of a His is associated with shorter Ser‐OH..N‐His bond in the PDB30 subset. We prospectively mine catalytic triads in unannotated proteins, suggesting catalytic functions for unannotated proteins. As a second test case, we demonstrate that this database system can include ligand atoms, represented by Sybyl atom types, by evaluating the proportion of counter‐ions for ligand carboxylate oxygens.