Efficient Generation of Conformer Ensembles Using Internal Coordinates and a Generative Directional Graph Convolution Neural Network.

We present a neural-network-based high-throughput molecular conformer-generation algorithm. A chemical graph-convolutional network is trained to predict low-energy conformers in internal coordinate representation (bond lengths, bond, and torsion angles), starting from two-dimensional (2D) chemical topology. Generative neural network (NN) architecture performs denoising from torsion space, producing conformer ensembles with populations that are well correlated with torsion energy profiles.

Graph-Convolutional Neural Net Model of the Statistical Torsion Profiles for Small Organic Molecules.

We present a graph-convolutional neural network (GCNN)-based method for learning and prediction of statistical torsional profiles (STP) in small organic molecules based on the experimental X-ray structure data. A specialized GCNN torsion profile model is trained using the structures in the Crystallography Open Database (COD). The GCNN-STP model captures torsional preferences over a wide range of torsion rotor chemotypes and correctly predicts a variety of effects from the vicinal atoms and moieties.

Etiology of invasive candidosis agents in Russia: a multicenter epidemiological survey.

A multicenter prospective epidemiological survey on the etiologic agents of invasive candidosis was conducted in Russia in the period of 2012-2014. Samples were collected from 284 patients with invasive candidosis and Candida species isolated by culture. The species were identified by DNA sequencing and MALDI-TOF massspectrometry.

FOCUS--Development of a Global Communication and Modeling Platform for Applied and Computational Medicinal Chemists.

Communication of data and ideas within a medicinal chemistry project on a global as well as local level is a crucial aspect in the drug design cycle. Over a time frame of eight years, we built and optimized FOCUS, a platform to produce, visualize, and share information on various aspects of a drug discovery project such as cheminformatics, data analysis, structural information, and design. FOCUS is tightly integrated with internal services that involve-among others-data retrieval systems and in-silico models and provides easy access to automated modeling procedures such as pharmacophore searches, R-group analysis, and similarity searches.

Discovery of novel membrane binding structures and functions.

The function of a protein is determined by its intrinsic activity in the context of its subcellular distribution. Membranes localize proteins within cellular compartments and govern their specific activities. Discovering such membrane-protein interactions is important for understanding biological mechanisms and could uncover novel sites for therapeutic intervention.