12,573 research outputs found
Spherical harmonics coeffcients for ligand-based virtual screening of cyclooxygenase inhibitors
Background: Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. Methodology/Principal Findings: We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. Conclusions/Significance: 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort
Big-Data-Driven Materials Science and its FAIR Data Infrastructure
This chapter addresses the forth paradigm of materials research -- big-data
driven materials science. Its concepts and state-of-the-art are described, and
its challenges and chances are discussed. For furthering the field, Open Data
and an all-embracing sharing, an efficient data infrastructure, and the rich
ecosystem of computer codes used in the community are of critical importance.
For shaping this forth paradigm and contributing to the development or
discovery of improved and novel materials, data must be what is now called FAIR
-- Findable, Accessible, Interoperable and Re-purposable/Re-usable. This sets
the stage for advances of methods from artificial intelligence that operate on
large data sets to find trends and patterns that cannot be obtained from
individual calculations and not even directly from high-throughput studies.
Recent progress is reviewed and demonstrated, and the chapter is concluded by a
forward-looking perspective, addressing important not yet solved challenges.Comment: submitted to the Handbook of Materials Modeling (eds. S. Yip and W.
Andreoni), Springer 2018/201
QSAR study for carcinogenicity in a large set of organic compounds
In our continuing efforts to find out acceptable Absorption, Distribution, Metabolization, Elimination and Toxicity (ADMET) properties of organic compounds, we establish linear QSAR models for the carcinogenic potential prediction of 1464 compounds taken from the "Galvez data set", that include many marketed drugs. More than a thousand of geometry-independent molecular descriptors are simultaneously analyzed, obtained with the softwares E-Dragon and Recon. The variable subset selection method employed is the Replacement Method, and also the improved version Enhanced Replacement Method. The established models are properly validated through an external test set of compounds, and by means of the Leave-Group-Out Cross Validation method. In addition, we apply the Y-Randomization strategy and analyze the Applicability Domain of the developed model. Finally, we compare the results obtained in present study with the previous ones from the literature. The novelty of present work relies on the development of an alternative predictive structure-carcinogenicity relationship in a large heterogeneous set of organic compounds, by only using a reduced number of geometry independent molecular descriptors.Fil: Duchowicz, Pablo Román. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto de Investigaciones FisicoquÃmicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones FisicoquÃmicas Teóricas y Aplicadas; ArgentinaFil: Comelli, Nieves Carolina. Universidad Nacional de Catamarca. Facultad de Ciencias Agrarias; ArgentinaFil: Ortiz, Erlinda del Valle. Universidad Nacional de Catamarca. Facultad de TecnologÃa y Ciencias Aplicadas; ArgentinaFil: Castro, Eduardo Alberto. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - La Plata. Instituto de Investigaciones FisicoquÃmicas Teóricas y Aplicadas. Universidad Nacional de La Plata. Facultad de Ciencias Exactas. Instituto de Investigaciones FisicoquÃmicas Teóricas y Aplicadas; Argentin
Multifractal Scaling, Geometrical Diversity, and Hierarchical Structure in the Cool Interstellar Medium
Multifractal scaling (MFS) refers to structures that can be described as a
collection of interwoven fractal subsets which exhibit power-law spatial
scaling behavior with a range of scaling exponents (concentration, or
singularity, strengths) and dimensions. The existence of MFS implies an
underlying multiplicative (or hierarchical, or cascade) process. Panoramic
column density images of several nearby star- forming cloud complexes,
constructed from IRAS data and justified in an appendix, are shown to exhibit
such multifractal scaling, which we interpret as indirect but quantitative
evidence for nested hierarchical structure. The relation between the dimensions
of the subsets and their concentration strengths (the "multifractal spectrum'')
appears to satisfactorily order the observed regions in terms of the mixture of
geometries present: strong point-like concentrations, line- like filaments or
fronts, and space-filling diffuse structures. This multifractal spectrum is a
global property of the regions studied, and does not rely on any operational
definition of "clouds.'' The range of forms of the multifractal spectrum among
the regions studied implies that the column density structures do not form a
universality class, in contrast to indications for velocity and passive scalar
fields in incompressible turbulence, providing another indication that the
physics of highly compressible interstellar gas dynamics differs fundamentally
from incompressible turbulence. (Abstract truncated)Comment: 27 pages, (LaTeX), 13 figures, 1 table, submitted to Astrophysical
Journa
From electronic structure to catalytic activity: A single descriptor for adsorption and reactivity on transition-metal carbides
Adsorption and catalytic properties of the polar (111) surface of
transition-metal carbides (TMC's) are investigated by density-functional
theory. Atomic and molecular adsorption are rationalized with the
concerted-coupling model, in which two types of TMC surface resonances (SR's)
play key roles. The transition-metal derived SR is found to be a single
measurable descriptor for the adsorption processes, implying that the
Br{\o}nsted-Evans-Polanyi relation and scaling relations apply. This gives a
picture with implications for ligand and vacancy effects and which has a
potential for a broad screening procedure for heterogeneous catalysts.Comment: 5 pages, 3 figure
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