Cheminformatics analysis and learning in a data pipelining environment

Summary Workflow technology is being increasingly applied in discovery information to organize and analyze data. SciTegic's Pipeline Pilot is a chemically intelligent implementation of a workflow technology known as data pipelining. It allows scientists to construct and execute workflows using components that encapsulate many cheminformatics based algorithms. In this paper we review SciTegic's methodology for molecular fingerprints, molecular similarity, molecular clustering, maximal common subgraph search and Bayesian learning. Case studies are described showing the application of these methods to the analysis of discovery data such as chemical series and high throughput screening results. The paper demonstrates that the methods are well suited to a wide variety of tasks such as building and applying predictive models of screening data, identifying molecules for lead optimization and the organization of molecules into families with structural commonality.     

Analysis of the Pressure Gradients According to Capsule Flow Mechanism

This study investigated the experimental coefficient in the mathematical correlation for the pressure gradient of spherical capsule train flow according to flow regimes. The investigation was carried out in the 1.2 × 10⁴ < Re < 1.5 × 10⁵ range and under transport concentration of 5 – 30 %. Deviation between the pressure drops measured by experiment and the mathematical correlation was calculated based on new experimental coefficients. The total pressure gradient in the two‐phase flow of the mixture was distinguished as pressure gradients caused by homogeneous flow and those caused by pressure drops, depending on the rotational movement of the capsules. The experimental findings indicate that at lower concentrations, the losses caused by the rotational movement are low, but the losses caused by the homogeneous flow are high. Losses caused by the rotational movement increase as the concentration increases.