Dear All,
I've just started working with RDKIT focusing on the application of
the Lipinsky rule on the set of my ligands. Basically I take a 3D
coordinates of each ligand file (in SDF format) and then calculate for
it required 4 properties
Here is my code:
# make a list of all .sdf filles present in data folder:
dirlist = [os.path.basename(p) for p in glob.glob('data' + '/*.sdf')]
# create empty data file with 5 columns:
# name of the file, value of variable p, value of ac, value of
don, value of wt
df = pd.DataFrame(columns=["key", "p", "ac", "don", "wt"])
# for each sdf file get its name and calculate 4 different
properties: p, ac, don, wt
for sdf in dirlist:
sdf_name=sdf.rsplit( ".", 1 )[ 0 ]
key = f'{sdf_name}'
mol = open(sdf,'rb')
m = Chem.ForwardSDMolSupplier(mol)
for conf in m:
if conf is None: continue
p = MolLogP(conf) # coeff conc-perm
ac = CalcNumLipinskiHBA(conf)#
don = CalcNumLipinskiHBD(conf)
wt = MolWt(conf)
#two=AllChem.Compute2DCoords(conf)
Draw.MolToFile(conf,results+f'/{key}.png')
#df[key] = [p, ac, don, wt]
Could you suggest how can I summarize the calculation of each ligand
in pandas-like DF and to then apply lipinsky filter on it?
Is it possible to convert 3D coordinates to 2D in order that I could
draw it (presently it makes a sketch based on 3d coordinates directly
from SDF)?
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