Grapheme: Advancing Protein-Ligand Visualization

How can we more quickly and efficiently extract the rich complexity of information and knowledge embodied in the three-dimensional structure of a protein-ligand complex? In the February 2015 toolkit release OpenEye extends the ability to represent complex, three-dimensional protein-ligand structures in two dimensions with the deployment of Ligand Depiction in Proteins, LDiP 1.0. This will save medicinal chemists and protein biophysicists countless hours staring at lists of complexes in 3D molecule viewers, instead enabling them to focus quickly on key compounds, key interactions or key properties. Example output from LDiP is shown in Figure 1. The emphasis is on clarity and immediacy of representation, without sacrifice of important information.

[application science, Grapheme TK, OEDocking, Python, ROCS]
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Recommendation System for Compound Selection

Just recently, Swann et al. of Abbott Labs published "A Unified, Probabilistic Framework for Structure- and Ligand-Based Virtual Screening" in the Journal of Medicinal Chemistry. If you haven’t read it yet, I highly recommend it. The paper is a very interesting extension of previous work done by Muchmore et al., also of Abbott Labs. Muchmore’s paper, "Application of Belief Theory to Similarity Data Fusion for Use in Analog Searching and Lead Hopping," presented a system for calculating a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity based on ligand similarity. Swann’s paper extends this work to include information obtained from structure-based virtual screening using docking.

[business, FastROCS, FRED, GraphSim TK, ROCS]
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