Contents of this file

• HiMap Introduction
• Requirements
• Authors
• Installation
• Usage
• Troubleshooting

HiMap Introduction

HiMap includes design generation based on statistical optimality. 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 rare, partly because of the difficulty of planning and setting up calculations. The lead optimization mapper (LOMAP) was introduced as an automated algorithm to plan relative free energy calculations between potential ligands. LOMAP was further developed to be based on free, open-source APIs such as RDKit. HiMap now includes clustering of ligand series, and optimization of free energy perturbation networks.

Requirements

• Lomap2
• Matplotlib
• python >= 3.8
• R
• rpy2=3.4.5
• kneed=0.7.0
• scikit-learn
• scipy
• numpy<1.24

Authors

• Mary Pitman [email protected]
• David Mobley [email protected]

Installation

To install HiMap with LOMAP included, build the conda environment and install from file:

[https://github.com/pitmanme/HiMap_dev/blob/main/devtools/conda-envs/himap_env.yml]

with:

conda env create -f himap_env.yml

Usage

Example scripts are included for various purposes:

• To run HiMap with optimization
  python examples/example_optimize.py
• To read in scores and optimize
  python examples/example_optimize_read_data.py

If you would rather use the API directly:

• To generate optimal designs, try:

cd examples/

import lomap
import himap

#-------------------------------------------------------#
# Generate similarity scores.
#-------------------------------------------------------#
# Read molecules from test directory.
db_mol = lomap.DBMolecules('../test/radial/', output=True, radial=True)
    
# Generate the strict and loose symmetric similarity score matrices.
strict, loose = db_mol.build_matrices()
    
# Convert the similarity matrix to numpy array
sim_np = strict.to_numpy_2D_array()

# Clean data if Lomap produces rare error. If score is NaN, replace with 0.0
n_arr = himap.clean_NaN(sim_np)

#-------------------------------------------------------#
# Clustering.
#-------------------------------------------------------#
# Create ID_list from db_mol prior to clustering.
ID_list = himap.db_mol_IDs(db_mol, n_arr)

# Perform clustering.
#   sub_arr, sub_ID:   the n_arr and ID_list subdivided by clusters
#   selected_clusters: user selected clusters during interaction.
sub_arr, sub_ID, selected_clusters = himap.cluster_interactive(n_arr, ID_list)

#-------------------------------------------------------#
# Optimization.
#-------------------------------------------------------#
# Example reference ligand.
ref_ligs = ['ejm_31']

# Send the user selected clusters for optimization.
himap.clusters2optimize(sub_arr, sub_ID, clusters2optim = selected_clusters, ref_ligs=ref_ligs)

• To generate optimal designs using external scores or weights, try:

import himap

#-------------------------------------------------------#
# Define input files, read data.
#-------------------------------------------------------#
# Input files for weight scores and ligand names.
sim_scores_in = '../test/optimize/sim_scores.csv'
IDs_in = '../test/optimize/mol_names.txt'

# Read files, clean any potential NaN scores.
#   Added optional parameter:
#             delimiter: default is ','
n_arr, ID_list = himap.read_data(sim_scores_in, IDs = IDs_in)

#-------------------------------------------------------#
# Clustering.
#-------------------------------------------------------#
# Perform clustering.
#   sub_arr, sub_ID:   the n_arr and ID_list subdivided by clusters
#   selected_clusters: user selected clusters during interaction.
sub_arr, sub_ID, selected_clusters = himap.cluster_interactive(n_arr, ID_list)

#-------------------------------------------------------#
# Optimization.
#-------------------------------------------------------#
# Example reference ligands.
ref_ligs = ['mol_0', 'mol_1', 'mol_2', 'mol_3', 'mol_4']

# Send the user selected clusters for optimization.
himap.clusters2optimize(sub_arr, sub_ID, clusters2optim = selected_clusters,
                        ref_ligs=ref_ligs, num_edges = '2n', optim_types = ['A', 'D']
                        )