Considering the conformational effects of the compound is important in Structure Based Drug Design, this paper discussed about it, in terms Protein-Ligand binding using torsional scan of each ligands(PDB:2JH0,2JH5,2JH6). They calculated torsional energy, and explained the relation between inhibitory activity and torsional energy.

Torsional scanning is the task of the quantum chemistry rather than that of chemoinformatics. But I wanted to conduct quantum chemical calculation as an extention of chemoinformatics way, so I implemented it in Psikit

• Torsion scan example

日本語訳

jupyterでRDKitからのB3LYP/6-31G*でのDFT計算さっくりうごくの素晴らしい。

Now pen and I are planning to develop a library for a Electronic-Structure Informatics named Psikit (Psi4 + RDKit).

Which coding style do you like?

The repository is here.

psikit as a wrapper library?

import psikit
import numpy as np
from rdkit import Chem

psikit.core.set_output_file("out.dat", True)
psikit.set_memory('4 GB')
psikit.set_num_threads(4)

mol = Chem.MolFromSmiles("c1ccccc1")
benz = psikit.geometry(mol)
scf_e, scf_wfn = psikit.energy("B3LYP/cc-pVDZ", return_wfn=True)
HOMO = psikit.get_homo(scf_wfn)
LUMO = psikit.get_lumo(scf_wfn)
print(HOMO, LUMO, scf_e)

or psikit as a set of util functions library?

import psi4
from psikit import mol2xyz, get_homo, get_lumo
import numpy as np
from rdkit import Chem
psi4.core.set_output_file("out.dat", True)
mol = Chem.MolFromSmiles("c1ccccc1")
xyz, mol = mol2xyz(mol)
psi4.set_memory('4 GB')
psi4.set_num_threads(4)
benz = psi4.geometry(xyz)
scf_e, scf_wfn = psi4.energy("B3LYP/cc-pVDZ", return_wfn=True)
HOMO = get_homo(scf_wfn)
LUMO = get_lumo(scf_wfn)
print(HOMO, LUMO, scf_e)