MolecularFingerprints

MolecularFingerprints.jl is a Julia package for calculating molecular fingerprints using various algorithms. It provides an easy-to-use interface for generating fingerprints from molecular structures, enabling efficient similarity searches, clustering, and machine learning applications in cheminformatics.

Quick Start

Installation

There are two ways of using the MolecularFingerprints.jl package:

1. Using it with temporary environment (best for trying out the package).
2. Using it with a persistent environment (best for using the package in your own projects).

Using with Temporary Environment

You can try out the MolecularFingerprints.jl package without installing it permanently by using a temporary environment. Open a Julia REPL and run the following commands:

using Pkg
Pkg.activate(temp=true)
Pkg.add(url="https://github.com/LukaszSztukiewicz/MolecularFingerprints.jl")
using MolecularFingerprints

Minimalistic Usage

Once you have installed the MolecularFingerprints.jl package, you can start using it to calculate molecular fingerprints. Here is a simple example:

using MolecularFingerprints
# Load a molecule from a SMILES string
molecule = "C1=CC=CC=C1"  # Benzene
# Choose the fingerprint calculator
fp = ECFP{1024}(2)  # Create an ECFP fingerprint generator of size 1024 with radius 2
# Calculate the fingerprint
fingerprint_vector = fingerprint(molecule, fp)
println(fingerprint_vector)
findall(fingerprint_vector)  # Indices of bits set to 1

Usage of all Fingerprint Types

The MolecularFingerprints.jl package supports several types of molecular fingerprints. Here is an example of how to use all available fingerprint types:

smiles = "C1=CC=CC=C1"

# 2. This package implements 4 types of fingerprints. 
# All of them could be customized with parameters, but here we use default settings.
ecfp_calc = ECFP() # Extended Connectivity Fingerprints
mhfp_calc = MHFP() # MinHash Fingerprints
torsion_calc = TopologicalTorsion() # Topological Torsion Fingerprints
maccs_calc = MACCS() # MACCS Keys

# 3. Execution: Compute the fingerprint for each type
ecfp_vector = fingerprint(smiles, ecfp_calc)
mhfp_vector = fingerprint(smiles, mhfp_calc)
torsion_vector = fingerprint(smiles, torsion_calc)
maccs_vector = fingerprint(smiles, maccs_calc)

# 4. Analysis: Find indices of active features

# ECFP returns BitVector to see active bits, we can use findall
println("ECFP active bits: ", findall(ecfp_vector))

# MACCS returns BitVector to see active bits, we can use findall
println("MACCS active bits: ", findall(maccs_vector))

# MHFP returns Vector{Int64} with each non-zero entry, so all bits are active
# You will see that are of the 2048 bits are being listed
println("MHFP active bits: ", findall(mhfp_vector .!= 0))

# TopologicalTorsion returns SparseArrays.SparseVector{Int32, Int64} so it is easy to find non-zero entries
using SparseArrays
println("Topological Torsion active bits: ", SparseArrays.findnz(torsion_vector)[1])

Documentation

The documentation for MolecularFingerprints.jl can be found at https://molecularfingerprints.lukaszsztukiewicz.com/stable.