The Journal of Computer-Aided Molecular Design: a bibliometric note

Summarizes the articles in, and the citations to, volumes 2-24 of the Journal of Computer-Aided Molecular Design. The citations to the journal come from almost 2000 different sources that span a very wide range of academic subjects, with the most heavily cited articles being descriptions of software systems and of computational methods

Structuprint: a scalable and extensible tool for two-dimensional representation of protein surfaces

© 2016 Kontopoulos et al.Background: The term molecular cartography encompasses a family of computational methods for two-dimensional transformation of protein structures and analysis of their physicochemical properties. The underlying algorithms comprise multiple manual steps, whereas the few existing implementations typically restrict the user to a very limited set of molecular descriptors. Results: We present Structuprint, a free standalone software that fully automates the rendering of protein surface maps, given - at the very least - a directory with a PDB file and an amino acid property. The tool comes with a default database of 328 descriptors, which can be extended or substituted by user-provided ones. The core algorithm comprises the generation of a mould of the protein surface, which is subsequently converted to a sphere and mapped to two dimensions, using the Miller cylindrical projection. Structuprint is partly optimized for multicore computers, making the rendering of animations of entire molecular dynamics simulations feasible. Conclusions: Structuprint is an efficient application, implementing a molecular cartography algorithm for protein surfaces. According to the results of a benchmark, its memory requirements and execution time are reasonable, allowing it to run even on low-end personal computers. We believe that it will be of use - primarily but not exclusively - to structural biologists and computational biochemists

Visual and computational analysis of structure-activity relationships in high-throughput screening data

Novel analytic methods are required to assimilate the large volumes of structural and bioassay data generated by combinatorial chemistry and high-throughput screening programmes in the pharmaceutical and agrochemical industries. This paper reviews recent work in visualisation and data mining that can be used to develop structure-activity relationships from such chemical/biological datasets

MMsINC: a large-scale chemoinformatics database

MMsINC (http://mms.dsfarm.unipd.it/MMsINC/search) is a database of non-redundant, richly annotated and biomedically relevant chemical structures. A primary goal of MMsINC is to guarantee the highest quality and the uniqueness of each entry. MMsINC then adds value to these entries by including the analysis of crucial chemical properties, such as ionization and tautomerization processes, and the in silico prediction of 24 important molecular properties in the biochemical profile of each structure. MMsINC is consequently a natural input for different chemoinformatics and virtual screening applications. In addition, MMsINC supports various types of queries, including substructure queries and the novel ‘molecular scissoring’ query. MMsINC is interfaced with other primary data collectors, such as PubChem, Protein Data Bank (PDB), the Food and Drug Administration database of approved drugs and ZINC

SYNTHESIS AND SCREENING OF SOME AZOMETHINES BEARING METHYL SALICYLATE MOIETY FOR ANTIBACTERIAL ACTIVITIES

In this study, the series of novel azomethines were synthesized from methyl salicylate and their structural determination was done by IR, NMR and mass spectral data. The screening of these synthesized compounds were done against some Gram positive and Gram negative bacteria in DMF and DMSO solvents by agar disc method. It is observed that solvent and compound structure both affect the inhibition. DMF is found to be good solvent in the present study. Amongst all studied compounds, JB-4 showed maximum zone of inhibition against most of the studied bacterial strains while compound JB-2 is least effective. Thus, substitution affects inhibition and methyl salicylate moiety with cinnamaldehyde side chain is most effective. Further, virtual screening of these compounds was done to evaluate molecular properties and bioactive scores of these compounds.Keywords: azomethines, methyl salicylate, antibacterial activity, agar disc method, DMF, DMS

FAF-Drugs2: Free ADME/tox filtering tool to assist drug discovery and chemical biology projects

<p>Abstract</p> <p>Background</p> <p>Drug discovery and chemical biology are exceedingly complex and demanding enterprises. In recent years there are been increasing awareness about the importance of predicting/optimizing the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small chemical compounds along the search process rather than at the final stages. Fast methods for evaluating ADMET properties of small molecules often involve applying a set of simple empirical rules (educated guesses) and as such, compound collections' property profiling can be performed <it>in silico</it>. Clearly, these rules cannot assess the full complexity of the human body but can provide valuable information and assist decision-making.</p> <p>Results</p> <p>This paper presents FAF-Drugs2, a free adaptable tool for ADMET filtering of electronic compound collections. FAF-Drugs2 is a command line utility program (e.g., written in Python) based on the open source chemistry toolkit OpenBabel, which performs various physicochemical calculations, identifies key functional groups, some toxic and unstable molecules/functional groups. In addition to filtered collections, FAF-Drugs2 can provide, via Gnuplot, several distribution diagrams of major physicochemical properties of the screened compound libraries.</p> <p>Conclusion</p> <p>We have developed FAF-Drugs2 to facilitate compound collection preparation, prior to (or after) experimental screening or virtual screening computations. Users can select to apply various filtering thresholds and add rules as needed for a given project. As it stands, FAF-Drugs2 implements numerous filtering rules (23 physicochemical rules and 204 substructure searching rules) that can be easily tuned.</p