Protein-Ligand Scoring with Convolutional Neural Networks

Computational approaches to drug discovery can reduce the time and cost associated with experimental assays and enable the screening of novel chemotypes. Structure-based drug design methods rely on scoring functions to rank and predict binding affinities and poses. The ever-expanding amount of protein-ligand binding and structural data enables the use of deep machine learning techniques for protein-ligand scoring. We describe convolutional neural network (CNN) scoring functions that take as input a comprehensive 3D representation of a protein-ligand interaction. A CNN scoring function automatically learns the key features of protein-ligand interactions that correlate with binding. We train and optimize our CNN scoring functions to discriminate between correct and incorrect binding poses and known binders and non-binders. We find that our CNN scoring function outperforms the AutoDock Vina scoring function when ranking poses both for pose prediction and virtual screening

Space Propulsion Technology Program Overview

The topics presented are covered in viewgraph form. Focused program elements are: (1) transportation systems, which include earth-to-orbit propulsion, commercial vehicle propulsion, auxiliary propulsion, advanced cryogenic engines, cryogenic fluid systems, nuclear thermal propulsion, and nuclear electric propulsion; (2) space platforms, which include spacecraft on-board propulsion, and station keeping propulsion; and (3) technology flight experiments, which include cryogenic orbital N2 experiment (CONE), SEPS flight experiment, and cryogenic orbital H2 experiment (COHE)