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From: Greg L. <gre...@gm...> - 2020-06-11 07:13:16
|
Hi Brian,
The warning is actually because you have double bonds with unspecified
stereochemistry.
You are skipping sanitization of the molecules. When you do this no
stereochemistry perception is done, so the InChI code is called without any
stereochemistry information and you get the warning.
If you construct the molecule "normally" (i.e. with sanitization) you get
the correct InChI and no warning:
In [57]: m =
Chem.MolFromSmiles(r'O=C(/C=C/c1ccccc1)c1ccc(OC/C=C(/CC/C=C(\C)/C)\C)cc1')
In [58]: Chem.MolToInchi(m)
Out[58]:
'InChI=1S/C25H28O2/c1-20(2)8-7-9-21(3)18-19-27-24-15-13-23(14-16-24)25(26)17-12-22-10-5-4-6-11-22/h4-6,8,10-18H,7,9,19H2,1-3H3/b17-12+,21-18+'
If you really want to call the InChI code without sanitizing the molecules
and want the stereochemistry to be correct, you have to do a bit more work:
In [63]: m =
Chem.MolFromSmiles(r'O=C(/C=C/c1ccccc1)c1ccc(OC/C=C(/CC/C=C(\C)/C)\C)cc1',sanitize=False)
In [64]: m.UpdatePropertyCache(strict=False)
In [65]: Chem.AssignStereochemistry(m)
In [66]: Chem.MolToInchi(m)
Out[66]:
'InChI=1S/C25H28O2/c1-20(2)8-7-9-21(3)18-19-27-24-15-13-23(14-16-24)25(26)17-12-22-10-5-4-6-11-22/h4-6,8,10-18H,7,9,19H2,1-3H3/b17-12+,21-18+'
Best,
-greg
On Thu, Jun 11, 2020 at 3:46 AM Bennion, Brian via Rdkit-discuss <
rdk...@li...> wrote:
> Hello,
> Below I show a smiles string from MOE and the smiles string calculated
> from RDKit and the InChI string calculated by RDkit(2020_1).
>
> The error on conversion to inchi string is confusing me after entering
> both smiles strings into a viewer I don't see any undefined stereo center.
>
> O=C(/C=C/c1ccccc1)c1ccc(OC/C=C(/CC/C=C(\C)/C)\C)cc1
> CC(C)=CCC/C(C)=C/COc1ccc(C(=O)/C=C/c2ccccc2)cc1
> [18:10:42] WARNING: Omitted undefined stereo
>
> InChI=1S/C25H28O2/c1-20(2)8-7-9-21(3)18-19-27-24-15-13-23(14-16-24)25(26)17-12-22-10-5-4-6-11-22/h4-6,8,10-18H,7,9,19H2,1-3H3
>
>
> while len(line) != 0:
> fields = line.replace('","',' ').split()
> mol1 = fields[0].replace('"','')
> mol_name = fields[1]
>
> try:
> mol = Chem.MolFromSmiles(mol1,sanitize=False) #,
> removeHs=False)
> except:
> mol = None
> if mol is None:
> print("mol1 failed:",mol1)
> output.write("mol1 failes:",mol1)
> else:
> rkditsmiout.write('\"'+Chem.MolToSmiles(mol,
> isomericSmiles=True)+'\"\n')
> print(Chem.MolToSmiles(mol, isomericSmiles=True))
> rkditsmiout.write('\"'+Chem.inchi.MolToInchi(mol)+'\"\n')
> print(Chem.inchi.MolToInchi(mol))
> count += 1
> print(count)
>
> _______________________________________________
> Rdkit-discuss mailing list
> Rdk...@li...
> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
>
|
|
From: Greg L. <gre...@gm...> - 2020-06-11 06:56:24
|
Hi Shaozhen,
The function for creating reaction fingerprints is
rdChemReactions.CreateDifferenceFingerprintForReaction()
Here's a quick demo of using it on your reactions:
In [44]: rxn1 = rdChemReactions.ReactionFromSmarts('CCCO>>CCC=O')
In [45]: rxn2 = rdChemReactions.ReactionFromSmarts('CC(O)C>>CC(=O)C')
In [46]: fp1 = rdChemReactions.CreateDifferenceFingerprintForReaction(rxn1)
In [47]: fp2 = rdChemReactions.CreateDifferenceFingerprintForReaction(rxn2)
In [48]: DataStructs.TanimotoSimilarity(fp1,fp2)
Out[48]: 0.0
The similarity here is zero because as far as the reaction fingerprint is
concerned the parts which change within the reactions have nothing in
common with each other.
An example where there is some similarity in what changes:
In [49]: rxn3 = rdChemReactions.ReactionFromSmarts('NCCO>>NCC=O')
In [50]: fp3 = rdChemReactions.CreateDifferenceFingerprintForReaction(rxn3)
In [51]: DataStructs.TanimotoSimilarity(fp1,fp3)
Out[51]: 0.42857142857142855
The reaction fingerprinting algorithm is described in this paper:
https://pubs.acs.org/doi/abs/10.1021/ci5006614
Best,
-greg
On Wed, Jun 10, 2020 at 6:13 AM 丁邵珍 <164...@qq...> wrote:
> Hi, I want to calculate Tanimoto similarity score of two reactions
> ('CCCO>>CCC=O', 'CC(O)C>>CC(=O)C'), I found all methods of Tanimoto
> similarity score calculation are for compounds. Could you please tell me
> how to calculate the Tanimoto similarity score of reactions? I am looking
> forward to your reply.
>
> Yours,
> shaozhen
> _______________________________________________
> Rdkit-discuss mailing list
> Rdk...@li...
> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
>
|
|
From: Francois B. <ml...@li...> - 2020-06-11 03:39:52
|
On 10/06/2020 13:11, 丁邵珍 wrote:
> Hi, I want to calculate Tanimoto similarity score of two reactions
> ('CCCO>>CCC=O', 'CC(O)C>>CC(=O)C'), I found all methods of Tanimoto
> similarity score calculation are for compounds. Could you please tell
> me how to calculate the Tanimoto similarity score of reactions? I am
> looking forward to your reply.
I don't know how to do it in rdkit, but if you need some inspiration,
here is how chemaxon does it:
https://docs.chemaxon.com/display/docs/Reaction_fingerprint_RF.html
> Yours,
> shaozhen
> _______________________________________________
> Rdkit-discuss mailing list
> Rdk...@li...
> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
|
|
From: Bennion, B. <ben...@ll...> - 2020-06-11 01:44:09
|
Hello,
Below I show a smiles string from MOE and the smiles string calculated from RDKit and the InChI string calculated by RDkit(2020_1).
The error on conversion to inchi string is confusing me after entering both smiles strings into a viewer I don't see any undefined stereo center.
O=C(/C=C/c1ccccc1)c1ccc(OC/C=C(/CC/C=C(\C)/C)\C)cc1
CC(C)=CCC/C(C)=C/COc1ccc(C(=O)/C=C/c2ccccc2)cc1
[18:10:42] WARNING: Omitted undefined stereo
InChI=1S/C25H28O2/c1-20(2)8-7-9-21(3)18-19-27-24-15-13-23(14-16-24)25(26)17-12-22-10-5-4-6-11-22/h4-6,8,10-18H,7,9,19H2,1-3H3
while len(line) != 0:
fields = line.replace('","',' ').split()
mol1 = fields[0].replace('"','')
mol_name = fields[1]
try:
mol = Chem.MolFromSmiles(mol1,sanitize=False) #, removeHs=False)
except:
mol = None
if mol is None:
print("mol1 failed:",mol1)
output.write("mol1 failes:",mol1)
else:
rkditsmiout.write('\"'+Chem.MolToSmiles(mol, isomericSmiles=True)+'\"\n')
print(Chem.MolToSmiles(mol, isomericSmiles=True))
rkditsmiout.write('\"'+Chem.inchi.MolToInchi(mol)+'\"\n')
print(Chem.inchi.MolToInchi(mol))
count += 1
print(count)
|
|
From: Eduardo M. <edu...@gm...> - 2020-06-10 18:12:41
|
Hi Tuan, I already realized, but since I'm working with various molecules. I was wondering if there is a way to let RDKit figure out the formal charge of each atom. On Wed, Jun 10, 2020, 3:25 AM Quoc-Tuan DO <quo...@gr...> wrote: > Hi Eduardo, > > Perhaps it lacks a positive charge on the nitrogen. > > Best regards, > > QT > > > Le 10/06/2020 à 03:23, Eduardo Mayo a écrit : > > Hi I'm working in a script for processing autodock vina screening output. > I got problem with protonated molecules as the molecule attached. Any idea > how I can load molecule with a given protonated state. > Attached is the RDKit error and the sdf file. > Best s, > Eduardo > > > _______________________________________________ > Rdkit-discuss mailing lis...@li...://lists.sourceforge.net/lists/listinfo/rdkit-discuss > > -- > <img > 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... 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From: Ivan Tubert-B. <iva...@sc...> - 2020-06-10 13:07:02
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Thank you everyone for the suggestions. For now I don't have immediate plans to adopt the cartridge but it's good to know these things when the time comes. Best, Ivan On Mon, Jun 8, 2020 at 6:49 PM Finnerty, Jim via Rdkit-discuss < rdk...@li...> wrote: > If you have a billion molecule data source and would like to try an > at-scale test, I'd be willing to help out with provisioning the hardware, > looking at the efficiency of the plans, etc., using rdkit with Aurora > PostgreSQL. > > If I understand how the rdkit GIST index filtering mechanism works for a > given similarity metric, a parallel GIST index scan ought to be able to > scale almost linearly scale with the number of cores, provided that the > RDBMS is built on a scalable storage subsystem. > > If so, the largest instance size that's currently supported has 96 cores, > so we can do a fairly high degree of parallelism. > > On 6/5/20, 1:07 PM, "dmaziuk via Rdkit-discuss" < > rdk...@li...> wrote: > > CAUTION: This email originated from outside of the organization. Do > not click links or open attachments unless you can confirm the sender and > know the content is safe. > > > > On 6/5/2020 4:45 AM, Greg Landrum wrote: > > > Having said that, the team behind ZINC used to use the RDKit > cartridge with > > PostgreSQL as the backend for ZINC. They had the database sharded > > across multiple instances and managed to get the fingerprint indices > to > > work there. I don't remember the substructure search performance > being > > terrible, but it wasn't great either. They have since switched to a > > specialized system (Arthor from NextMove software), which offers > > significantly better performance. > > Generally speaking a database of a billion rows needs hardware capable > of running it. Buy a server with 1TB RAM and 64 cores and a couple of > U.2 NVME drives and see how Postgres runs on that. > > Then you need to look at the database, e.g. query in an indexed > billion-row table could be OK but inserting a billion-first row will > not be. > > If you want to scale to these kinds of volumes, you need to do some > work. > > (And much of the point of no-sql hadoop "cloud" workflows is that if > you > can parallelize what you're doing to multiple machines, at some data > size they will start outperforming a centralized fast search engine.) > > Dima > > > _______________________________________________ > Rdkit-discuss mailing list > Rdk...@li... > https://lists.sourceforge.net/lists/listinfo/rdkit-discuss > > > > _______________________________________________ > Rdkit-discuss mailing list > Rdk...@li... > https://lists.sourceforge.net/lists/listinfo/rdkit-discuss > |
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From: Quoc-Tuan DO <quo...@gr...> - 2020-06-10 07:22:54
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<p>Hi Eduardo,</p>
<p>Perhaps it lacks a positive charge on the nitrogen.</p>
<p>Best regards,<br>
</p>
<p>QT</p>
<p><br>
</p>
<div class="moz-cite-prefix">Le 10/06/2020 à 03:23, Eduardo Mayo a
écrit :<br>
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<div dir="auto">Hi I'm working in a script for processing autodock
vina screening output. I got problem with protonated molecules
as the molecule attached. Any idea how I can load molecule with
a given protonated state.
<div dir="auto">Attached is the RDKit error and the sdf file.</div>
<div dir="auto">Best s, </div>
<div dir="auto">Eduardo</div>
</div>
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...
[truncated message content] |
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From: Shani L. <lev...@gm...> - 2020-06-10 06:47:28
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Thank you so much! It helps me a lot
On Tue, Jun 9, 2020 at 6:24 PM Greg Landrum <gre...@gm...> wrote:
> Hi Shani,
>
> If you have mapped atoms in the reactants that are not in the products,
> those end up being removed
>
> I'm not sure exactly what reaction you're trying to do, but I think you
> want something like this;
>
> rxn =
> AllChem.ReactionFromSmarts("([C:1]=[C:2].[*:3][*+:4])>>[*:2]-[*:1][*+0:4][*:3]")
> m1 = Chem.MolFromSmiles('C=CC([CH2+])CCC=C(C)C')
> ps = rxn.RunReactants((m1,))
> for p in ps: print(Chem.MolToSmiles(p[0]))
>
> Note that I also explicitly neutralized the carbocation in the products.
> Otherwise the +1 from the reactants would be carried over.
>
> -greg
>
>
> On Tue, Jun 9, 2020 at 4:42 PM Shani Levi <lev...@gm...> wrote:
>
>> Hello,
>> I'm interested in using AllChem.ReactionFromSmarts to predict product for
>> a specific reaction.
>> For example, I want to describe the reaction between double bonds and a
>> carbo-cations.
>>
>> *I tried: *
>> rxn = AllChem.ReactionFromSmarts("([C:1]=[C:2].[*:3][*+:4])>>[*:1][*:4]")
>> m1 = Chem.MolFromSmiles('C=CC([CH2+])CCC=C(C)C')
>> ps = rxn.RunReactants((m1,))
>>
>> *and it gave me four molecules: *
>>
>> [CH2+]C [CH2+]C [CH2+]CCC
>> [CH2+]C(C)C
>>
>> the problem here that it does not describe the ring-closure molecules and
>> it somehow cuts the rest of the molecule, if someone has any suggestions of
>> how to change the SMARTS descriptions that it will define the right
>> reaction.
>>
>> Thank you very much,
>> Shani
>> _______________________________________________
>> Rdkit-discuss mailing list
>> Rdk...@li...
>> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
>>
>
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From: <164...@qq...> - 2020-06-10 04:12:01
|
Hi, I want to calculate Tanimoto similarity score of two reactions ('CCCO>>CCC=O', 'CC(O)C>>CC(=O)C'), I found all methods of Tanimoto similarity score calculation are for compounds. Could you please tell me how to calculate the Tanimoto similarity score of reactions? I am looking forward to your reply.
Yours,
shaozhen |
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From: Eduardo M. <edu...@gm...> - 2020-06-10 01:23:58
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Hi I'm working in a script for processing autodock vina screening output. I got problem with protonated molecules as the molecule attached. Any idea how I can load molecule with a given protonated state. Attached is the RDKit error and the sdf file. Best s, Eduardo |
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From: Alan K. M. <ala...@ho...> - 2020-06-09 20:17:01
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Hi Navid,
I think you have a few options. One is to loop over your molecule’s atoms and delete those hydrogens without any neighbors (degree = 0). In Python this would look something like the following:
import rdkit
from rdkit import Chem
from rdkit.Chem import rdmolops
# mol = Chem.MolFromSmiles("C#CC(O)C1CCN1.[HH]")
mol = Chem.MolFromSmiles("C#CC(O)C1CCN1.[H].[H]")
disconnected_hydrogens = [atom for atom in mol.GetAtoms() if atom.GetAtomicNum() == 1 and atom.GetDegree() == 0]
print([atom.GetIdx() for atom in disconnected_hydrogens])
If you know that your dummy hydrogens aren’t connected to the rest of the graph you could also do the following:
disconnected_fragments = rdmolops.GetMolFrags(mol, asMols=True)
print([Chem.MolToSmiles(fragment) for fragment in disconnected_fragments])
As for using dummy atoms, one thing that comes to mind is using atoms with an atomic number of 0. Depending on the molecular property you are calculating this may be good enough. You can set the atomic number with the atom.SetAtomicNum(0) function.
As a side note, I’m not sure the SMILES you provided is valid. Perhaps you should separate each hydrogen as their own molecule (see the code above)?
Best regards,
Alan
From: Navid Shervani-Tabar<mailto:ns...@gm...>
Sent: 09 June 2020 21:47
To: RDKit Discuss<mailto:rdk...@li...>
Subject: [Rdkit-discuss] Removing disconnected hydrogens
Hello RDKitters,
I'm using a function to convert a molecular graph to RDKit's mol object. Input molecules have a maximum size of N atoms. Molecules with less than N atoms have dummy atoms on the corresponding node. Currently, I use hydrogen as the dummy atom when building the editable RWmol object. This results in hydrogen atoms without neighbours. An example of such a molecule has SMILES representation 'C#CC(O)C1CCN1.[HH]'. I was wondering
1. How can I remove the hydrogen's without neighbours? These hydrogen are currently affecting the molecular properties.
2. Is there a better option to use as the dummy atom? Something that potentially would not affect the molecular properties.
PS: I can't skip the dummy atoms while building the mol object b/c some graphs mistakenly have bonds connected to these atoms and I need the statistics on the defective molecules.
Thanks,
Navid
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From: Navid Shervani-T. <ns...@gm...> - 2020-06-09 19:46:41
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Hello RDKitters, I'm using a function to convert a molecular graph to RDKit's mol object. Input molecules have a maximum size of N atoms. Molecules with less than N atoms have dummy atoms on the corresponding node. Currently, I use hydrogen as the dummy atom when building the editable RWmol object. This results in hydrogen atoms without neighbours. An example of such a molecule has SMILES representation 'C#CC(O)C1CCN1.[HH]'. I was wondering 1. How can I remove the hydrogen's without neighbours? These hydrogen are currently affecting the molecular properties. 2. Is there a better option to use as the dummy atom? Something that potentially would not affect the molecular properties. PS: I can't skip the dummy atoms while building the mol object b/c some graphs mistakenly have bonds connected to these atoms and I need the statistics on the defective molecules. Thanks, Navid |
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From: Greg L. <gre...@gm...> - 2020-06-09 15:24:54
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Hi Shani,
If you have mapped atoms in the reactants that are not in the products,
those end up being removed
I'm not sure exactly what reaction you're trying to do, but I think you
want something like this;
rxn =
AllChem.ReactionFromSmarts("([C:1]=[C:2].[*:3][*+:4])>>[*:2]-[*:1][*+0:4][*:3]")
m1 = Chem.MolFromSmiles('C=CC([CH2+])CCC=C(C)C')
ps = rxn.RunReactants((m1,))
for p in ps: print(Chem.MolToSmiles(p[0]))
Note that I also explicitly neutralized the carbocation in the products.
Otherwise the +1 from the reactants would be carried over.
-greg
On Tue, Jun 9, 2020 at 4:42 PM Shani Levi <lev...@gm...> wrote:
> Hello,
> I'm interested in using AllChem.ReactionFromSmarts to predict product for
> a specific reaction.
> For example, I want to describe the reaction between double bonds and a
> carbo-cations.
>
> *I tried: *
> rxn = AllChem.ReactionFromSmarts("([C:1]=[C:2].[*:3][*+:4])>>[*:1][*:4]")
> m1 = Chem.MolFromSmiles('C=CC([CH2+])CCC=C(C)C')
> ps = rxn.RunReactants((m1,))
>
> *and it gave me four molecules: *
>
> [CH2+]C [CH2+]C [CH2+]CCC
> [CH2+]C(C)C
>
> the problem here that it does not describe the ring-closure molecules and
> it somehow cuts the rest of the molecule, if someone has any suggestions of
> how to change the SMARTS descriptions that it will define the right
> reaction.
>
> Thank you very much,
> Shani
> _______________________________________________
> Rdkit-discuss mailing list
> Rdk...@li...
> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
>
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From: Shani L. <lev...@gm...> - 2020-06-09 14:40:16
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Hello,
I'm interested in using AllChem.ReactionFromSmarts to predict product for a
specific reaction.
For example, I want to describe the reaction between double bonds and a
carbo-cations.
*I tried: *
rxn = AllChem.ReactionFromSmarts("([C:1]=[C:2].[*:3][*+:4])>>[*:1][*:4]")
m1 = Chem.MolFromSmiles('C=CC([CH2+])CCC=C(C)C')
ps = rxn.RunReactants((m1,))
*and it gave me four molecules: *
[CH2+]C [CH2+]C [CH2+]CCC
[CH2+]C(C)C
the problem here that it does not describe the ring-closure molecules and
it somehow cuts the rest of the molecule, if someone has any suggestions of
how to change the SMARTS descriptions that it will define the right
reaction.
Thank you very much,
Shani
|
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From: Max P. Jr <ma...@gm...> - 2020-06-09 08:29:02
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Hi Eduardo, Have you tried a python package called RMSD? Here is the link for this package https://pypi.org/project/rmsd/. I have used this program to calculate the RMSD between conformers and it works pretty well. You just need to give the xyz matrices of each molecule as input. There are a few examples on the Github page. I hope it helps. Max Pinheiro Jr Em ter., 9 de jun. de 2020 às 08:13, Eduardo Mayo < edu...@gm...> escreveu: > Hi I'm trying to calculate the RMSD between conformers of the same > molecules stores in separate mol file. > I figured out a way: > > m1= Chem.FromMolFile('1.mol') > m2= Chem.FromMolFile('2.mol') > > m1.AddConformer(M2.GetConformer(-1),1) > AllChem.GetConformerRMS(m1,0,1) > > Is there another way?? > > _______________________________________________ > Rdkit-discuss mailing list > Rdk...@li... > https://lists.sourceforge.net/lists/listinfo/rdkit-discuss > |
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From: Eduardo M. <edu...@gm...> - 2020-06-09 06:11:41
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Hi I'm trying to calculate the RMSD between conformers of the same
molecules stores in separate mol file.
I figured out a way:
m1= Chem.FromMolFile('1.mol')
m2= Chem.FromMolFile('2.mol')
m1.AddConformer(M2.GetConformer(-1),1)
AllChem.GetConformerRMS(m1,0,1)
Is there another way??
|
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From: <vj...@pa...> - 2020-06-08 23:36:48
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I have RDKit Jython on prtable apps but it seems to have dlls and no exe. I finally tealised the three lines all were one line and java stopped bombing, but now all I get is silence. |
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From: Finnerty, J. <jfi...@am...> - 2020-06-08 22:49:04
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If you have a billion molecule data source and would like to try an at-scale test, I'd be willing to help out with provisioning the hardware, looking at the efficiency of the plans, etc., using rdkit with Aurora PostgreSQL.
If I understand how the rdkit GIST index filtering mechanism works for a given similarity metric, a parallel GIST index scan ought to be able to scale almost linearly scale with the number of cores, provided that the RDBMS is built on a scalable storage subsystem.
If so, the largest instance size that's currently supported has 96 cores, so we can do a fairly high degree of parallelism.
On 6/5/20, 1:07 PM, "dmaziuk via Rdkit-discuss" <rdk...@li...> wrote:
CAUTION: This email originated from outside of the organization. Do not click links or open attachments unless you can confirm the sender and know the content is safe.
On 6/5/2020 4:45 AM, Greg Landrum wrote:
> Having said that, the team behind ZINC used to use the RDKit cartridge with
> PostgreSQL as the backend for ZINC. They had the database sharded
> across multiple instances and managed to get the fingerprint indices to
> work there. I don't remember the substructure search performance being
> terrible, but it wasn't great either. They have since switched to a
> specialized system (Arthor from NextMove software), which offers
> significantly better performance.
Generally speaking a database of a billion rows needs hardware capable
of running it. Buy a server with 1TB RAM and 64 cores and a couple of
U.2 NVME drives and see how Postgres runs on that.
Then you need to look at the database, e.g. query in an indexed
billion-row table could be OK but inserting a billion-first row will not be.
If you want to scale to these kinds of volumes, you need to do some work.
(And much of the point of no-sql hadoop "cloud" workflows is that if you
can parallelize what you're doing to multiple machines, at some data
size they will start outperforming a centralized fast search engine.)
Dima
_______________________________________________
Rdkit-discuss mailing list
Rdk...@li...
https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
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From: Stiefl, N. <nik...@no...> - 2020-06-08 13:36:21
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Dear all, I wanted to bring to your attention that our position for a cheminformatics expert in the CADD group in our global chemistry community at NIBR is opened again. https://www.novartis.com/careers/career-search/job-details/288340BR If you feel like you want to apply your skill set to real-world drug discovery problems within a group of molecular modellers, data scientists and other CIx experts please go ahead :). Looking forward to your applications. Best Nik (Stiefl) |
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From: Nicolas B. <nb...@eb...> - 2020-06-08 08:24:27
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Hi Max, Third alternative: https://github.com/chembl/ChEMBL_Structure_Pipeline <https://github.com/chembl/ChEMBL_Structure_Pipeline> parent_molblock, _ = standardizer.get_parent_molblock(o_molblock) This will strip the molecule. Nicolas > On 8 Jun 2020, at 08:19, Pierre-Marie Allard <Pie...@un...> wrote: > > Hi Max, > > You can also use MolVS https://molvs.readthedocs.io/en/latest/ <https://molvs.readthedocs.io/en/latest/> > This should suit most of your needs, > > PM > _________________________________________ > > Pierre-Marie Allard > Research Assistant - Natural Products Chemistry > ISPSO - UniGe - Geneva > pie...@un... <mailto:pie...@un...> > >> On 8 Jun 2020, at 08:46, Francois Berenger <ml...@li... <mailto:ml...@li...>> wrote: >> >> On 06/06/2020 17:33, Max Pinheiro Jr wrote: >>> Hi RDkit team, >>> I am working on a chemically diverse dataset of smiles strings and I >>> need to do some preprocessing to clean a bit the data before starting >>> the modeling part. So I was looking for some tools or built-in >>> functions in RDkit to make such preprocessing by removing, for >>> instance, solvent (water) molecules and ions. I found the >>> "SaltRemover" module that may solve my problem with removing ions from >>> the database, but I could not find an equivalent module for the case >>> of solvent molecules. Does anyone know a specific tool in RDkit (or >>> any other python program) to make such preprocessing in the smile >>> strings? If so, could you please provide just a simple example of how >>> to do it? I will be really thankful for any help you may provide. >> >> I have used this program several times: >> >> https://github.com/flatkinson/standardiser <https://github.com/flatkinson/standardiser> >> >> You can try this: >> ``` >> pip3 install chemo-standardizer >> standardiser -i input.smi -o output_std.smi >> ``` >> >> I believe it uses rdkit under the hood. >> >> Regards, >> F. >> >>> Max Pinheiro Jr >>> --------------------------------------------- >>> Université Aix-Marseille, France >>> Institut de Chimie Radicalaire >>> _______________________________________________ >>> Rdkit-discuss mailing list >>> Rdk...@li... >>> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss >> >> >> _______________________________________________ >> Rdkit-discuss mailing list >> Rdk...@li... >> https://lists.sourceforge.net/lists/listinfo/rdkit-discuss > > _______________________________________________ > Rdkit-discuss mailing list > Rdk...@li... > https://lists.sourceforge.net/lists/listinfo/rdkit-discuss |
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From: Pierre-Marie A. <Pie...@un...> - 2020-06-08 07:35:36
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Hi Max, You can also use MolVS https://molvs.readthedocs.io/en/latest/ This should suit most of your needs, PM _________________________________________ Pierre-Marie Allard Research Assistant - Natural Products Chemistry ISPSO - UniGe - Geneva pie...@un...<mailto:pie...@un...> On 8 Jun 2020, at 08:46, Francois Berenger <ml...@li...<mailto:ml...@li...>> wrote: On 06/06/2020 17:33, Max Pinheiro Jr wrote: Hi RDkit team, I am working on a chemically diverse dataset of smiles strings and I need to do some preprocessing to clean a bit the data before starting the modeling part. So I was looking for some tools or built-in functions in RDkit to make such preprocessing by removing, for instance, solvent (water) molecules and ions. I found the "SaltRemover" module that may solve my problem with removing ions from the database, but I could not find an equivalent module for the case of solvent molecules. Does anyone know a specific tool in RDkit (or any other python program) to make such preprocessing in the smile strings? If so, could you please provide just a simple example of how to do it? I will be really thankful for any help you may provide. I have used this program several times: https://github.com/flatkinson/standardiser You can try this: ``` pip3 install chemo-standardizer standardiser -i input.smi -o output_std.smi ``` I believe it uses rdkit under the hood. Regards, F. Max Pinheiro Jr --------------------------------------------- Université Aix-Marseille, France Institut de Chimie Radicalaire _______________________________________________ Rdkit-discuss mailing list Rdk...@li... https://lists.sourceforge.net/lists/listinfo/rdkit-discuss _______________________________________________ Rdkit-discuss mailing list Rdk...@li... https://lists.sourceforge.net/lists/listinfo/rdkit-discuss |
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From: Francois B. <ml...@li...> - 2020-06-08 07:09:49
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On 06/06/2020 17:33, Max Pinheiro Jr wrote: > Hi RDkit team, > > I am working on a chemically diverse dataset of smiles strings and I > need to do some preprocessing to clean a bit the data before starting > the modeling part. So I was looking for some tools or built-in > functions in RDkit to make such preprocessing by removing, for > instance, solvent (water) molecules and ions. I found the > "SaltRemover" module that may solve my problem with removing ions from > the database, but I could not find an equivalent module for the case > of solvent molecules. Does anyone know a specific tool in RDkit (or > any other python program) to make such preprocessing in the smile > strings? If so, could you please provide just a simple example of how > to do it? I will be really thankful for any help you may provide. I have used this program several times: https://github.com/flatkinson/standardiser You can try this: ``` pip3 install chemo-standardizer standardiser -i input.smi -o output_std.smi ``` I believe it uses rdkit under the hood. Regards, F. > Max Pinheiro Jr > --------------------------------------------- > Université Aix-Marseille, France > Institut de Chimie Radicalaire > _______________________________________________ > Rdkit-discuss mailing list > Rdk...@li... > https://lists.sourceforge.net/lists/listinfo/rdkit-discuss |
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From: Max P. Jr <ma...@gm...> - 2020-06-06 08:33:59
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Hi RDkit team, I am working on a chemically diverse dataset of smiles strings and I need to do some preprocessing to clean a bit the data before starting the modeling part. So I was looking for some tools or built-in functions in RDkit to make such preprocessing by removing, for instance, solvent (water) molecules and ions. I found the "SaltRemover" module that may solve my problem with removing ions from the database, but I could not find an equivalent module for the case of solvent molecules. Does anyone know a specific tool in RDkit (or any other python program) to make such preprocessing in the smile strings? If so, could you please provide just a simple example of how to do it? I will be really thankful for any help you may provide. Max Pinheiro Jr --------------------------------------------- Université Aix-Marseille, France Institut de Chimie Radicalaire |
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From: David T. <dav...@uc...> - 2020-06-05 22:36:45
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Hi all, I am trying to create a library of calixarenes for use in machine learning. I can combine the fragments but when I embed the molecules they are the wrong geometry, one ring inverted when they are all meant to be the same direction. Does anyone know how to solve this, or to correct the bond angle as I would need to rotate 2 bonds at once as the calixarene is cyclic. Any help would be appreciated, thanks. David |
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From: Good E. <goo...@gm...> - 2020-06-05 22:24:23
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I notice that if I pass a mol object (derived from a molblock with the
chiral flag set) through GetMolFrags(), the resulting chiral molecules do
not have the chiral flag set when new molblocks are generated. I include a
molfile at the end that can be used to demonstrate this with the code
below. Am I missing a flag somewhere to preserve the chiral information?
Please note that I am aware that I can manually set the chiral flag with
SetProp() after fragments are generated, however I want to preserve the
user's original input and not force everything to have the chiral flag set.
mmol = Chem.MolFromMolBlock(open("C:\\Users\\...\\blahblah.mol","r").read())
print(Chem.MolToMolBlock(mmol, includeStereo=True))
# confirm we see chiral flag set in molblock header
frags = list(Chem.GetMolFrags(mmol,asMols=True))
print(Chem.MolToMolBlock(frags[0],includeStereo=True))
# confirm chiral flag missing in molblock header
print(Chem.MolToMolBlock(frags[0],includeStereo=True))
# confirm chiral flag missing in molblock header
-------------------------
ChemDraw06052017572D
15 15 0 0 1 0 0 0 0 0999 V2000
-3.7695 -0.7508 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.7695 0.0742 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.0550 -1.1633 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-2.3405 -0.7508 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-2.3405 0.0742 0.0000 O 0 0 0 0 0 0 0 0 0 0 0 0
-3.0550 0.4867 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
-3.0550 1.3117 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1.6260 -0.8992 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1.6260 -0.0742 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
2.3405 -1.3117 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
3.0550 -0.8992 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
3.0550 -0.0742 0.0000 N 0 0 0 0 0 0 0 0 0 0 0 0
2.3405 0.3383 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
2.3405 1.1633 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
3.7695 0.3383 0.0000 C 0 0 0 0 0 0 0 0 0 0 0 0
1 2 1 0
1 3 1 0
3 4 1 0
4 5 1 0
5 6 1 0
6 2 1 0
6 7 1 1
8 9 1 0
8 10 1 0
10 11 1 0
11 12 1 0
12 13 1 0
13 9 1 0
13 14 1 6
12 15 1 0
M END
-------------------------
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