Lomap

Alchemical free energy calculations hold increasing promise as an aid to drug discovery efforts. However, applications of these techniques in discovery projects have been relatively few, partly because of the difficulty of planning and setting up calculations. The lead optimization mapper (LOMAP) was introduced as an automated algorithm to plan efficient relative free energy calculations between potential ligands within a substantial of compounds. The original LOMAP code was mainly based on commercial APIs such as OpenEye and Schrodinger. The aim of this project is to deveop a new version of LOMAP based on free avalaible APIs such as RDKit offering the scientific community a free tool to plan in advance binding free energy calculations

Prerequisites

RDKit Release >2015.09.2
Graphviz 2.38
pygraphviz
NetworkX
Matplotlib
PyQt 4.11

Authors

Gaetano Calabro' gcalabro@uci.edu
David Mobley dmobley@uci.edu

Installation

Add to the conda channels:

conda config --add channels nividic

and then:

conda install lomap

Add to the conda channels:

conda config --add channels mobleylab

and then:

conda install lomap

Usage

import lomap

# Generate the molecule database starting from a directory containing .mol2 files

db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True)

    #More graphing options:
    # Use the complete radial graph option. The ligand with the most structural similarity to all of the others will be picked as the 'lead compounds' and used as the central compound.
    db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True)

    # Use a radial graph with a manually specified hub compound
    db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True, hub=filename.mol2)

    # Use a radial graph with a manually specified hub compound and fast graphing option
    #the fast graphing option create the initial graph by connecting the hub ligand with the possible surrounding ligands and add surrounding edges based on the similarities accoss surrounding nodes
    db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, radial=True, hub=filename.mol2, fast=True)

# Calculate the similarity matrix betweeen the database molecules. Two molecules are generated
# related to the scrict rule and loose rule 

strict, loose = db_mol.build_matrices()

# Generate the NetworkX graph and output the results
nx_graph = db_mol.build_graph() 


# Calculate the Maximum Common Subgraph (MCS) between 
# the first two molecules in the molecule database 
# ignoring hydrogens and depicting the mapping in a file
    
MC = lomap.MCS.getMapping(db_mol[0].getMolecule(), db_mol[1].getMolecule(), hydrogens=False, fname='mcs.png')


# Alchemical transformation are usually performed between molecules with
# the same charges. However, it is possible to allow this transformation
# manually setting the electrostatic score for the whole set of molecules 
# producing a connected graph. The electrostatic scrore must be in the 
# range [0,1]


db_mol = lomap.DBMolecules("python string pointing to a directory with mol2 files", output=True, ecrscore=0.1)
strict, loose = db_mol.build_matrices()
nx_graph = db_mol.build_graph()

Issues

Lomap is in debugging stage and it has been tested on Ubuntu 14.04 and OSX Yosemite
Lomap has been developed in python 2.7 and 3.4

Disclaimers

This code is currently in alpha release status. Use at your own risk. We will almost certainly be making changes to the API in the near future.