MolTaut: A Tool for Rapid Generation of Favorable Tautomer in Aqueous Solution

Fast and proper treatment of tautomeric state for drug-like molecules is critical in computer-aided drug discovery since the major tautomer of a molecule determines its pharmacophore features and physical properties. We present MolTaut, a tool for the rapid generation of favorable states of drug-like molecules in water. MolTaut works by enumerating possible tautomer states with tautomeric transformation rules, ranking tautomers with their relative internal energies and solvation energies calculated by AI-based models, and generating preferred ionization states according to predicted microscopic pKa. We found that the ranking ability of the AI-based tautomer scoring approach is comparable to the DFT method from which the AI models try to learn. To facilitate the usage of MolTaut, we made a web server, which is available at http://moltaut.xundrug.cn. MolTaut is a handy tool for generating low energy tatuomers when preparing chemical structures in computer-aided drug discovery. Besides, MolTaut can be used for analyzing substitution effect on the tautomeric equilibrium in lead optimization.

Requirements

• Python 3.6
• openbabel >= 3.0
• numpy 1.18.1
• RDKit 2020.09.1.0
• scipy
• pandas 0.25.3
• freesasa
• pytorch 1.10.2
• pytorch geometric 2.0.3
• torch-scatter 2.0.9
• torch-sparse 0.6.12
• torchani 2.2
• ase 3.22.1

You also can create the python environment by conda configure file:

conda env create -f environment.yaml

If you run torch-sparse with error, please uninstall the package torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric:

pip uninstall torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric

and then reinstall them:

pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.10.0+cpu.html

Or you can use the environment created by conda-pack, and activate the python env by conda. Some errors may occur when you do from openbabel import pybel, you just need to reinstall openbabel by conda. The environment file download URL is as fellow:

https://drive.google.com/file/d/1xhJRTJa49Qdj1R00PISWGVKHK2WeQKnJ/view?usp=share_link

mkdir solv_env
mv solv_rdkit_2020_env.tar.gz solv_env
cd solv_env
tar -zxvf solv_rdkit_2020_env.tar.gz
source activate bin/active
conda remove openbabel
conda install openbabel -c conda-forge

Or you can use the shell script to install the python environment.

./install_moltaut_env.sh

Usage

python predict_tautomer.py --help

usage: predict_tautomer.py [-h] [--smi SMI]
                           [--low_energy_tautomer_cutoff LOW_ENERGY_TAUTOMER_CUTOFF]
                           [--cutmol CUTMOL] [--num_confs NUM_CONFS] [--ph PH]
                           [--tph TPH] [--output OUTPUT]

calculate low-energy tautomer for small molecules

optional arguments:
  -h, --help            show this help message and exit
  --smi SMI             the molecular smiles
  --low_energy_tautomer_cutoff LOW_ENERGY_TAUTOMER_CUTOFF
                        the energy cutoff for low energy
  --cutmol CUTMOL       determine to frag the molecule
  --num_confs NUM_CONFS
                        the number of conformation for solvation energy
                        prediction
  --ph PH               the target pH for protonation states generation
  --tph TPH             pH tolerance for protonation states generation
  --output OUTPUT       the output SDF file name

These is a example for the MolTaut usage, the ligand is extracted from pdbid 5v7i:

python predict_tautomer.py --smi "Cc1c2c([nH]n1)OC(=C([C@@]2(c3cc(cc(c3)N4CCCC4)C(F)(F)F)C(C)C)C#N)N"

Or you can access the web server of MolTaut--URL