Computer Aided Aroma Design. I. Molecular knowledge framework

Computer Aided Aroma Design (CAAD) is likely to become a hot issue as the REACH EC document targets many aroma compounds to require substitution. The two crucial steps in CAMD are the generation of candidate molecules and the estimation of properties, which can be difficult when complex molecular structures like odours are sought and when their odour quality are definitely subjective whereas their odour intensity are partly subjective as stated in Rossitier’s review (1996). In part I, provided that classification rules like those presented in part II exist to assess the odour quality, the CAAD methodology presented proceeds with a multilevel approach matched by a versatile and novel molecular framework. It can distinguish the infinitesimal chemical structure differences, like in isomers, that are responsible for different odour quality and intensity. Besides, its chemical graph concepts are well suited for genetic algorithm sampling techniques used for an efficient screening of large molecules such as aroma. Finally, an input/output XML format based on the aggregation of CML and ThermoML enables to store the molecular classes but also any subjective or objective property values computed during the CAAD process

Compound Perfect Squared Squares of the Order Twenties

P. J. Federico used the term low-order for perfect squared squares with at most 28 squares in their dissection. In 2010 low-order compound perfect squared squares (CPSSs) were completely enumerated. Up to symmetries of the square and its squared subrectangles there are 208 low-order CPSSs in orders 24 to 28. In 2012 the CPSSs of order 29 were completely enumerated, giving a total of 620 CPSSs up to order 29.Comment: 44 pages, 10 figures. For associated pdf illustrations of enumerated compound perfect squared squares up to order 29, see http://squaring.net/downloads/downloads.html#cps

Gypsum-DL: an open-source program for preparing small-molecule libraries for structure-based virtual screening

Computational techniques such as structure-based virtual screening require carefully prepared 3D models of potential small-molecule ligands. Though powerful, existing commercial programs for virtual-library preparation have restrictive and/or expensive licenses. Freely available alternatives, though often effective, do not fully account for all possible ionization, tautomeric, and ring-conformational variants. We here present Gypsum-DL, a free, robust open-source program that addresses these challenges. As input, Gypsum-DL accepts virtual compound libraries in SMILES or flat SDF formats. For each molecule in the virtual library, it enumerates appropriate ionization, tautomeric, chiral, cis/trans isomeric, and ring-conformational forms. As output, Gypsum-DL produces an SDF file containing each molecular form, with 3D coordinates assigned. To demonstrate its utility, we processed 1558 molecules taken from the NCI Diversity Set VI and 56,608 molecules taken from a Distributed Drug Discovery (D3) combinatorial virtual library. We also used 4463 high-quality protein-ligand complexes from the PDBBind database to show that Gypsum-DL processing can improve virtual-screening pose prediction. Gypsum-DL is available free of charge under the terms of the Apache License, Version 2.0

Automated Identification and Classification of Stereochemistry: Chirality and Double Bond Stereoisomerism

Stereoisomers have the same molecular formula and the same atom connectivity and their existence can be related to the presence of different three-dimensional arrangements. Stereoisomerism is of great importance in many different fields since the molecular properties and biological effects of the stereoisomers are often significantly different. Most drugs for example, are often composed of a single stereoisomer of a compound, and while one of them may have therapeutic effects on the body, another may be toxic. A challenging task is the automatic detection of stereoisomers using line input specifications such as SMILES or InChI since it requires information about group theory (to distinguish stereoisomers using mathematical information about its symmetry), topology and geometry of the molecule. There are several software packages that include modules to handle stereochemistry, especially the ones to name a chemical structure and/or view, edit and generate chemical structure diagrams. However, there is a lack of software capable of automatically analyzing a molecule represented as a graph and generate a classification of the type of isomerism present in a given atom or bond. Considering the importance of stereoisomerism when comparing chemical structures, this report describes a computer program for analyzing and processing steric information contained in a chemical structure represented as a molecular graph and providing as output a binary classification of the isomer type based on the recommended conventions. Due to the complexity of the underlying issue, specification of stereochemical information is currently limited to explicit stereochemistry and to the two most common types of stereochemistry caused by asymmetry around carbon atoms: chiral atom and double bond. A Webtool to automatically identify and classify stereochemistry is available at http://nams.lasige.di.fc.ul.pt/tools.ph

CASE via MS: Ranking Structure Candidates by Mass Spectra

Two important tasks in computer-aided structure elucidation (CASE) are the generation of candidate structures from a given molecular formula, and the ranking of structure candidates according to compatibility with an experimental spectrum. Candidate ranking with respect to electron impact mass spectra is based on virtual fragmentation of a candidate structure and comparison of the fragments’ isotope distributions against the spectrum of the unknown compound, whence a structure–spectrum compatibility matchvalue is computed. Of special interest is the matchvalue’s ability to distinguish between the correct and false constitutional isomers. Therefore a quality score was computed in the following way: For a (randomly selected) spectrum–structure pair from the NIST MS library all constitutional isomers are generated using the structure generator MOLGEN. For each isomer the matchvalue with respect to the library spectrum is calculated, and isomers are ranked according to their matchvalues. The quality of the ranking can be quantified in terms of the correct structure’s relative ranking position (RRP). This procedure was repeated for 100 randomly selected spectrum–structure pairs belonging to small organic compounds. In this first approach the RRP of the correct isomer was 0.27 on average