Development of a knowledge-based potential for crystals of small organic molecules: Calculation of energy surfaces for C=0 center dot center dot center dot H-N hydrogen bonds

This paper describes the derivation of a Knowledge-Based Potential for intermolecular interactions from the statistical information stored in the Cambridge Structural Database. We develop a statistical mechanical method that relates the occurrences of intermolecular contacts in the database to their energies. Our approach allows us to quantify (in the form of energy) the geometrical preferences of interactions. We use our method to construct energy maps for a hydrogen bond between carbonyl oxygen and amino hydrogen. Our results demonstrate high orientational selectivity of this type of hydrogen bonding

Effect of complex oxide promoters and Pd on activity and stability of Ni/YSZ (ScSZ) cermets as anode materials for IT SOFC

Effect of fluorite-like or perovskite-like complex oxide promoters, Pd and Cu on the performance of Ni/8YSZ and Ni/ScCeSZ anode materials in CH4 steam reforming (SR) or selective oxidation (SO) by O-2 into syngas was studied. The spatial distribution of dopants in composites before and after contact with the reaction feed, features of components mutual interaction and forms of deposited coke were controlled by TEM combined with EDX analysis. The lattice oxygen mobility and reactivity were estimated by CH4 and H-2 temperature-programmed reduction (TPR), and the amount of deposited carbon after operation in the feed with stoichiometric H2O/CH4 ratio was estimated by the temperature-programmed oxidation. Promoters decrease the amount of deposited coke, while doping by Pd or Cu ensures also a good and stable performance at moderate (similar to 550 degrees C) temperatures required for the intermediate-temperature solid oxide fuel cells (IT SOFC) operation. (c) 2007 Elsevier B.V. All rights reserved.</p

Ligand and structure-based methodologies for the prediction of the activity of G protein-coupled receptor ligands

Accurate in silico models for the quantitative prediction of the activity of G protein-coupled receptor (GPCR) ligands would greatly facilitate the process of drug discovery and development. Several methodologies have been developed based on the properties of the ligands, the direct study of the receptor-ligand interactions, or a combination of both approaches. Ligand-based three-dimensional quantitative structure-activity relationships (3D-QSAR) techniques, not requiring knowledge of the receptor structure, have been historically the first to be applied to the prediction of the activity of GPCR ligands. They are generally endowed with robustness and good ranking ability; however they are highly dependent on training sets. Structure-based techniques generally do not provide the level of accuracy necessary to yield meaningful rankings when applied to GPCR homology models. However, they are essentially independent from training sets and have a sufficient level of accuracy to allow an effective discrimination between binders and nonbinders, thus qualifying as viable lead discovery tools. The combination of ligand and structure-based methodologies in the form of receptor-based 3D-QSAR and ligand and structure-based consensus models results in robust and accurate quantitative predictions. The contribution of the structure-based component to these combined approaches is expected to become more substantial and effective in the future, as more sophisticated scoring functions are developed and more detailed structural information on GPCRs is gathered

A graph-based approach to construct target-focused libraries for virtual screening

BACKGROUND: Due to exorbitant costs of high-throughput screening, many drug discovery projects commonly employ inexpensive virtual screening to support experimental efforts. However, the vast majority of compounds in widely used screening libraries, such as the ZINC database, will have a very low probability to exhibit the desired bioactivity for a given protein. Although combinatorial chemistry methods can be used to augment existing compound libraries with novel drug-like compounds, the broad chemical space is often too large to be explored. Consequently, the trend in library design has shifted to produce screening collections specifically tailored to modulate the function of a particular target or a protein family. METHODS: Assuming that organic compounds are composed of sets of rigid fragments connected by flexible linkers, a molecule can be decomposed into its building blocks tracking their atomic connectivity. On this account, we developed eSynth, an exhaustive graph-based search algorithm to computationally synthesize new compounds by reconnecting these building blocks following their connectivity patterns. RESULTS: We conducted a series of benchmarking calculations against the Directory of Useful Decoys, Enhanced database. First, in a self-benchmarking test, the correctness of the algorithm is validated with the objective to recover a molecule from its building blocks. Encouragingly, eSynth can efficiently rebuild more than 80 % of active molecules from their fragment components. Next, the capability to discover novel scaffolds is assessed in a cross-benchmarking test, where eSynth successfully reconstructed 40 % of the target molecules using fragments extracted from chemically distinct compounds. Despite an enormous chemical space to be explored, eSynth is computationally efficient; half of the molecules are rebuilt in less than a second, whereas 90 % take only about a minute to be generated. CONCLUSIONS: eSynth can successfully reconstruct chemically feasible molecules from molecular fragments. Furthermore, in a procedure mimicking the real application, where one expects to discover novel compounds based on a small set of already developed bioactives, eSynth is capable of generating diverse collections of molecules with the desired activity profiles. Thus, we are very optimistic that our effort will contribute to targeted drug discovery. eSynth is freely available to the academic community at www.brylinski.org/content/molecular-synthesis. [Figure: see text