Novel methods for predicting logP, pK(a), and logD values have been developed using data sets (592 molecules for logP and 1029 for pK(a)) containing a wide range of molecular structures. An equation with three molecular properties (polarizability and partial atomic charges on nitrogen and oxygen) correlates highly with logP (r2 = 0.89). The pK(a)s are estimated for both acids and bases using a novel tree structured fingerprint describing the ionizing centers. The new models have been compared with existing models and also experimental measurements on test sets of common organic compounds and pharmaceutical molecules.

Mammalian heart development is built on highly conserved molecular mechanisms with polygenetic perturbations resulting in a spectrum of congenital heart diseases (CHD). However, knowledge of cardiogenic ontogeny that regulates proper cardiogenesis remains largely based on candidate-gene approaches. Mapping the dynamic transcriptional landscape of cardiogenesis from a genomic perspective is essential to integrate the knowledge of heart development into translational applications that accelerate disease discovery efforts toward mechanistic-based treatment strategies. Herein, we designed a time-course transcriptome analysis to investigate the genome-wide dynamic expression landscape of innate murine cardiogenesis ranging from embryonic stem cells to adult cardiac structures. This comprehensive analysis generated temporal and spatial expression profiles, revealed stage-specific gene functions, and mapped the dynamic transcriptome of cardiogenesis to curated pathways. Reconciling known genetic underpinnings of CHD, we deconstructed a disease-centric dynamic interactome encoded within this cardiogenic atlas to identify stage-specific developmental disturbances clustered on regulation of epithelial-to-mesenchymal transition (EMT), BMP signaling, NF-AT signaling, TGFb-dependent EMT, and Notch signaling. Collectively, this cardiogenic transcriptional landscape defines the time-dependent expression of cardiac ontogeny and prioritizes regulatory networks at the interface between health and disease.

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This is the second phase of the pK(a) predictor published earlier (J. Chem. Inf. Comput. Sci. 2002, 42, 796-805). The algorithm has been extended by treating specific chemical classes separately and generating tree-structured molecular descriptors tailored to each individual class. A training set consisting of 625 acids and 412 bases covers the major areas of chemical space involved in protonation and deprotonation. The models obtained demonstrate excellent statistics (SE = 0.41 for acids and 0.30 for bases) and yielded accurate predictions on an external test set. The quality and statistical performance of pK(a) prediction has been improved considerably over the initial implementation of the method.

Virtual Reality, an immersive technology that replicates an environment via computer-simulated reality, gets a lot of attention in the entertainment industry. However, VR has also great potential in other areas, like the medical domain, Examples are intervention planning, training and simulation. This is especially of use in medical operations, where an aesthetic outcome is important, like for facial surgeries. Alas, importing medical data into Virtual Reality devices is not necessarily trivial, in particular, when a direct connection to a proprietary application is desired. Moreover, most researcher do not build their medical applications from scratch, but rather leverage platforms like MeVisLab, MITK, OsiriX or 3D Slicer. These platforms have in common that they use libraries like ITK and VTK, and provide a convenient graphical interface. However, ITK and VTK do not support Virtual Reality directly. In this study, the usage of a Virtual Reality device for medical data under the MeVisLab platform is presented. The OpenVR library is integrated into the MeVisLab platform, allowing a direct and uncomplicated usage of the head mounted display HTC Vive inside the MeVisLab platform. Medical data coming from other MeVisLab modules can directly be connected per drag-and-drop to the Virtual Reality module, rendering the data inside the HTC Vive for immersive virtual reality inspection.

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Computers in chemistryComputers in chemistry V 0380 Predicting pK a by Molecular Tree Structured Fingerprints and PLS. -(XING*, L.; GLEN, R. C.; CLARK, R. D.; J. Chem. Inf. Comput. Sci. 43 (2003) 3, 870-879; Tripos, Inc., St. Louis, MO 63144, USA; Eng.) -Lindner 34-221

Nanoplastics contamination is one of the pressing environmental concerns globally. Among many environmental factors in the aquatic system, ubiquitous proteins are expected to affect the physicochemical properties of nanoplastics, and...