cluster_mols is a PyMOL plugin that allows the user to quickly select compounds from a virtual screen to be purchased or synthesized.
The most up to date version (recommended) of clustermols is available through BitBucket at: https://bitbucket.org/mpb21/clustermols_py/overview
This plugin has a number of dependencies that are required. And it is currently only supported on Linux and OSX.
Baumgartner, Matthew (2016) IMPROVING RATIONAL DRUG DESIGN BY INCORPORATING NOVEL BIOPHYSICAL INSIGHT. Doctoral Dissertation, University of Pittsburgh.
In the previous workflow I described docking a set of ligands with known activity into a target protein, in this workflow we will be using a set of ligands from the ZINC dataset searching for novel ligands. Once docked the workflow moves on to finding vendors and selecting subsets for purchase.
Whilst high-throughput screening (HTS) has been the starting point for many successful drug discovery programs the cost of screening, the lack of access to a large diverse sample collection, or the low throughput of the primary assay may preclude HTS as a starting point and identification of a smaller selection of compounds with a higher probability of being a hit may be desired. Directed or Virtual screening is a computational technique used in drug discovery research designed to identify potential hits for evaluation in primary assays. It involves the rapid in silico assessment of large libraries of chemical structures in order to identify those structures that most likely to be active against a drug target. The in silico screen can be based on known ligand similarity or based on docking ligands into the desired binding site.
I've updated the description to give more information about preparing the target protein.
A publication currently in press, SwissSimilarity: A Web Tool for Low to Ultra High Throughput Ligand-Based Virtual Screening DOI describes a new web tool for virtual screening of vast virtual libraries.
SwissSimilarity is a new web tool for rapid ligand-based virtual screening of small to unprecedented ultralarge libraries of small molecules. Screenable compounds include drugs, bioactive and commercial molecules, as well as 205 million of virtual compounds readily synthesizable from commercially available synthetic reagents. Predictions can be carried out on-the-fly using six different screening approaches, including 2D molecular fingerprints as well as superpositional and fast nonsuperpositional 3D similarity methodologies. SwissSimilarity is part of a large initiative of the SIB Swiss Institute of Bioinformatics to provide online tools for computer-aided drug design, such as SwissDock, SwissBioisostere or SwissTargetPrediction with which it can interoperate, and is linked to other well-established online tools and databases. User interface and backend have been designed for simplicity and ease of use, to provide proficient virtual screening capabilities to specialists and nonexperts in the field.
The website is at http://www.swisssimilarity.ch.
One thing to bear in mind is that any potential hits from screening virtual libraries will require synthetic chemistry resources to make the molecules for confirmation!
ZINC is a free database of commercially-available compounds for virtual screening. ZINC contains over 100 million purchasable compounds in ready-to-dock, 3D formats. Sterling and Irwin, J. Chem. Inf. Model, 2015. This is an invaluable resource for any type of virtual screening or for anyone looking to create a physical screening or fragment collection.
Once you have done the virtual screening you will rapidly realise that the really time-consuming a tedious part now lies ahead. Finding out which vendors stock a particular molecule and then ordering them. Looking up the vendor details for individual compounds is extremely tedious and so this Vortex script may be very useful.
DOCK 6 is written in C++ and is functionally separated into independent components, allowing a high degree of program flexibility. Accessory programs are written in C and Fortran 77. Source code for all programs is provided. Read the FAQ for details of installation under MacOSX.
Allen, W. J.; Balius, T. E.; Mukherjee, S.; Brozell, S. R.; Moustakas, D. T.; Lang, P. T.; Case, D. A.; Kuntz, I. D.; Rizzo, R. C. DOCK 6: Impact of New Features and Current Docking Performance. J. Comput. Chem. Submitted.