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jump-cellpainting / JUMP-Target

Lists and 384-well plate maps of compounds and genetic perturbations designed to assess connectivity in profiling assays
MIT License
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Clarify that there are actually 301 unique compounds, not 306 #9

Closed shntnu closed 3 months ago

shntnu commented 3 years ago

These 3 compounds are duplicated (but different broad_sample values)

pert_iname n
BVT-948 2
dexamethasone 2
thiostrepton 2
niranjchandrasekaran commented 3 years ago

In the case of BVT-948 (44483339 and 6604934) and Thiostrepton (16154490 and 101290202), it looks they may be different molecules with the same chemical name (They have different IUPAC names or Isomeric SMILES).

In the case of Dexamethasone, I am not sure how the two are different though there are two different entries on PubChem (5743 and 5702035).

shntnu commented 3 years ago

My conclusion here is that given that they have the same molecular formula, we should treat them similar enough so as to always keep them in the same data split.

E.g. below, it is not ok to have the pair BRD-A22713669:PTPN1 in training and the pair BRD-K68567222:PTPN1 in testing.

With the fix in #17, pert_iname is now a sufficient and recommended identifier for compounds when doing train-test splits. Using broad_sample (or its first 13 characters, which ispert_id) may result in bad data splits wrt these 3 compounds.

pert_iname pert_id target
BVT-948 BRD-A22713669 PTPN1
BVT-948 BRD-A22713669 PTPN11
BVT-948 BRD-A22713669 PTPN2
BVT-948 BRD-K68567222 PTPN1
BVT-948 BRD-K68567222 PTPN11
BVT-948 BRD-K68567222 PTPN2
dexamethasone BRD-A10188456 ANXA1
dexamethasone BRD-A10188456 NOS2
dexamethasone BRD-A10188456 NR0B1
dexamethasone BRD-A10188456 NR3C1
dexamethasone BRD-A10188456 NR3C2
dexamethasone BRD-K38775274 ANXA1
dexamethasone BRD-K38775274 NOS2
dexamethasone BRD-K38775274 NR0B1
dexamethasone BRD-K38775274 NR3C1
dexamethasone BRD-K38775274 NR3C2
thiostrepton BRD-A20697603 FOXM1
thiostrepton BRD-K58049875 FOXM1 
data.frame(pert_iname = c("dexamethasone", "BVT-948", "thiostrepton")) %>% inner_join(drug_target_samples) %>% select(pert_iname, pert_id, target) %>% distinct(pert_iname, pert_id, target) %>% arrange(pert_iname, pert_id) %>% knitr::kable()
shntnu commented 2 years ago

We need to figure this out so that others trying to create these plates know what to pick

Here's the metadata again for these compounds

broad_sample pert_iname pubchem_cid target InChIKey smiles
BRD-A22713669-001-04-3 BVT-948 44483339 PTPN2 AJVXVYTVAAWZAP-UHFFFAOYSA-N CC1(C)C2C(=NC1=O)c1ccccc1C(=O)C2=O
BRD-K68567222-001-01-2 BVT-948 6604934 PTPN2 LLPBUXODFQZPFH-UHFFFAOYSA-N CC1(C)C(=O)NC2=C1C(=O)C(=O)c1ccccc21
BRD-K38775274-001-22-1 dexamethasone 5743 ANXA1 UREBDLICKHMUKA-CXSFZGCWSA-N C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO
BRD-A10188456-001-04-9 dexamethasone 5702035 ANXA1 UREBDLICKHMUKA-QCYOSJOCSA-N C[C@@H]1CC2C3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO
BRD-K58049875-001-03-9 thiostrepton 16154490 FOXM1 NSFFHOGKXHRQEW-AIHSUZKVSA-N CC[C@H](C)[C@@H]1N[C@@H]2C=Cc3c(cc(nc3[C@H]2O)C(=O)O[C@H](C)[C@@H]2NC(=O)c3csc(n3)[C@@H](NC(=O)[C@H]3CSC(=N3)\C(NC(=O)[C@@H](NC(=O)c3csc(n3)[C@]3(CCC(=N[C@@H]3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)[C@H](C)NC(=O)C(=C)NC(=O)[C@H](C)NC1=O)[C@@H](C)O)=C\C)[C@](C)(O)[C@@H](C)O)[C@H](C)O
BRD-A20697603-001-07-2 thiostrepton 101290202 FOXM1 NSFFHOGKXHRQEW-DVRIZHICSA-N CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O

@niranjchandrasekaran said

In the case of BVT-948 (44483339 and 6604934) and Thiostrepton (16154490 and 101290202), it looks they may be different molecules with the same chemical name (They have different IUPAC names or Isomeric SMILES).

In the case of Dexamethasone, I am not sure how the two are different though there are two different entries on PubChem (5743 and 5702035).

In a separate conversation, we had some confusion about dexamethasone

https://github.com/jump-cellpainting/normalization/issues/3#issuecomment-824366920

Side note: I'm not sure how we ended up with these duplicates, but I think it was because we were using something other than pert_iname as identifiers when selecting (which is fine because its <1%).

So the main question is: what should we recommend to people trying to recreate this plate? I'll tag @dkuhn in case he has some insights given his comment in #24

shntnu commented 8 months ago

I used https://github.com/jump-cellpainting/compound-annotator/blob/79596e411fc119b2b71970a8b698ce33658f55a6/StandardizeMolecule.py below

Prep data

wget https://raw.githubusercontent.com/jump-cellpainting/JUMP-Target/6db6edbc2360c2bb5dfd2d082459acd6055d209c/JUMP-Target-2_compound_metadata.tsv

grep -E 'thiostrepton|dexamethasone|BVT-948|pert_iname' ~/Downloads/JUMP-Target-2_compound_metadata.tsv  > ~/Downloads/JUMP-Target-2_compound_metadata-only-dups.tsv

Run JUMP standardizer 

python StandardizeMolecule.py \
  run --num_cpu=8 \
  --input=/Users/shsingh/Downloads/JUMP-Target-2_compound_metadata-only-dups.tsv \
  --output=/Users/shsingh/Downloads/JUMP-Target-
2_compound_metadata-only-dups-std.tsv

Output:

Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
Tautomer enumeration stopped at maximum 1000
6/6 [04:11<00:00, 41.93s/it]
/Users/shsingh/Downloads/JUMP-Target-2_compound_metadata-only-dups-std.tsv

Original SMILES are distinct

cat /Users/shsingh/Downloads/JUMP-Target-2_compound_metadata-only-dups-std.tsv|csvcut -c SMILES_original|tail -n +2|sort|uniq -c
   1 CC1(C)C(=O)NC2=C1C(=O)C(=O)c1ccccc21
   1 CC1(C)C2C(=NC1=O)c1ccccc1C(=O)C2=O
   1 CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O
   1 CC[C@H](C)[C@@H]1N[C@@H]2C=Cc3c(cc(nc3[C@H]2O)C(=O)O[C@H](C)[C@@H]2NC(=O)c3csc(n3)[C@@H](NC(=O)[C@H]3CSC(=N3)\C(NC(=O)[C@@H](NC(=O)c3csc(n3)[C@]3(CCC(=N[C@@H]3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)[C@H](C)NC(=O)C(=C)NC(=O)[C@H](C)NC1=O)[C@@H](C)O)=C\C)[C@](C)(O)[C@@H](C)O)[C@H](C)O
   1 C[C@@H]1CC2C3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO
   1 C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO

Standardized SMILES reveal "duplicates"

cat /Users/shsingh/Downloads/JUMP-Target-2_compound_metadata-only-dups-std.tsv|csvcut -c SMILES_standardized|tail -n +2|sort|uniq -c
   2 C=C(NC(=O)C(=C)NC(=O)c1csc(C2=NC3c4csc(n4)C4NC(=O)c5csc(n5)C(C(C)(O)C(C)O)NC(=O)C5CSC(=N5)C(=CC)NC(=O)C(C(C)O)NC(=O)c5csc(n5)C3(CC2)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC(=O)C(C(C)CC)Nc2ccc3c(c2O)NC(=CC3C(C)O)C(=O)OC4C)n1)C(N)=O
   2 CC1(C)C(=O)N=C2c3ccccc3C(=O)C(=O)C21
   2 CC1CC2C3CC=C4CC(=O)C=CC4(C)C3(F)C(O)CC2(C)C1(O)C(=O)CO
David-Araripe commented 8 months ago

It seems like the first pair of compounds is a tautomer (PubChem CID as molecule label): image, The second one has a difference in one stereocenter (stated v.s. not shown): image In the third, it seems like there's one SMILES with stereochemistry and one without: image

Standardizing compounds with the ChEMBL standardizer didn't change the structures either, even after trying to get the parent structures for each of the compounds.

To reproduce the analysis:

from io import StringIO
from pubchempy import get_compounds
from chembl_structure_pipeline.standardizer import standardize_mol, get_parent_mol
from rdkit.Chem import Draw

data = """broad_sample  pert_iname  pubchem_cid target  InChIKey    smiles
BRD-A22713669-001-04-3  BVT-948 44483339    PTPN2   AJVXVYTVAAWZAP-UHFFFAOYSA-N CC1(C)C2C(=NC1=O)c1ccccc1C(=O)C2=O
BRD-K68567222-001-01-2  BVT-948 6604934 PTPN2   LLPBUXODFQZPFH-UHFFFAOYSA-N CC1(C)C(=O)NC2=C1C(=O)C(=O)c1ccccc21
BRD-K38775274-001-22-1  dexamethasone   5743    ANXA1   UREBDLICKHMUKA-CXSFZGCWSA-N C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO
BRD-A10188456-001-04-9  dexamethasone   5702035 ANXA1   UREBDLICKHMUKA-QCYOSJOCSA-N C[C@@H]1CC2C3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO
BRD-K58049875-001-03-9  thiostrepton    16154490    FOXM1   NSFFHOGKXHRQEW-AIHSUZKVSA-N CC[C@H](C)[C@@H]1N[C@@H]2C=Cc3c(cc(nc3[C@H]2O)C(=O)O[C@H](C)[C@@H]2NC(=O)c3csc(n3)[C@@H](NC(=O)[C@H]3CSC(=N3)\C(NC(=O)[C@@H](NC(=O)c3csc(n3)[C@]3(CCC(=N[C@@H]3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)[C@H](C)NC(=O)C(=C)NC(=O)[C@H](C)NC1=O)[C@@H](C)O)=C\C)[C@](C)(O)[C@@H](C)O)[C@H](C)O
BRD-A20697603-001-07-2  thiostrepton    101290202   FOXM1   NSFFHOGKXHRQEW-DVRIZHICSA-N CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O"""

def get_inchi(smi):
    mol = Chem.MolFromSmiles(smi)
    return Chem.MolToInchiKey(mol)

def get_info_from_pubchem(cid):
    cpds = get_compounds(cid)
    assert len(cpds) == 1, "More than one compound found"
    return cpds[0].to_dict(
        properties=["isomeric_smiles", "inchikey", "iupac_name", "synonyms"]
    )

def standardize_smiles(smi):
    mol = Chem.MolFromSmiles(smi)
    parent_mol = get_parent_mol(mol)[0]
    mol = standardize_mol(parent_mol, sanitize=True)
    return Chem.MolToSmiles(mol, isomericSmiles=True, canonical=True)

df = pd.read_csv(StringIO(data), sep="\t").assign(
    rdInChIKey=lambda x: x["smiles"].apply(get_inchi),
    std_smiles=lambda x: x["smiles"].apply(standardize_smiles),
)
pubchem_info = df.apply(
    lambda x: get_info_from_pubchem(x["pubchem_cid"]), axis=1, result_type="expand"
)
pubchem_info = pubchem_info.rename(
    columns={c: f"pubchem_{c}" for c in pubchem_info.columns}
)
df = pd.concat([df, pubchem_info], axis=1)

assert all(
    [
        (df["rdInChIKey"] == df["InChIKey"]).all(),
        (df["InChIKey"] == df["pubchem_inchikey"]).all(),
    ]
)

for name in df.pert_iname.unique():
    subset = df.query("pert_iname == @name")
    # plot the two molecules in a molecular grid
    mols = [Chem.MolFromSmiles(smi) for smi in subset["smiles"]]
    img = Draw.MolsToGridImage(
        mols,
        subImgSize=(300, 300),
        molsPerRow=2,
        returnPNG=True,
        legends=subset["pubchem_cid"].astype(str).tolist(),
    )
    display(img)
shntnu commented 8 months ago

@David-Araripe

Thank you very much for your help! I spoke with @srijitseal and this has helped us conclude that we should indeed report this as 303, not 306 compounds.

I'll report back here closing this out

shntnu commented 8 months ago

All set in #31

Thank you once again David and Srijit!

shntnu commented 8 months ago

@srijitseal @David-Araripe -- sorry to reopen, but it appears that we might have 2 more "duplicates" as noted here https://github.com/jump-cellpainting/datasets/issues/80#issuecomment-1787924639

Is it possible for you to verify that these two additional cases are also isomers?

If so, then we should report 301, not 303 compounds here.

David-Araripe commented 8 months ago

Hi @shntnu, here what I've observed:

ME-0328 seems to be indeed a duplicate, where one of the repeats doesn't have stereochemistry information: image

quinine and quinidine are isomers: image

The code for making the molecular drawings:

import pandas as pd
from rdkit import Chem
from rdkit.Chem import Draw

df = (
    pd.read_csv(
        "https://raw.githubusercontent.com/jump-cellpainting/JUMP-Target/09f11aefd6b550cfb2d2074e38a65c3be39ddc39/JUMP-Target-2_compound_metadata.tsv",
        sep="\t",
    )
    .query("pert_iname.str.contains('ME-0328|quinidine|quinine')")
    .assign(
        pubchem_cid=lambda x: x["pubchem_cid"].astype(int),
    )
)

for names in [["ME-0328"], ["quinidine", "quinine"]]:
    subset = df.query("pert_iname.isin(@names)")
    # plot the two molecules in a molecular grid
    mols = [Chem.MolFromSmiles(smi) for smi in subset["smiles"]]
    img = Draw.MolsToGridImage(
        mols,
        subImgSize=(300, 300),
        molsPerRow=2,
        returnPNG=True,
        legends=subset["pubchem_cid"].astype(str).tolist(),
    )
    display(img)
srijitseal commented 8 months ago

Hi David,

I think we still need to check for this one:

thiostrepton

On Tue, Oct 31, 2023 at 4:53 PM David Araripe @.***> wrote:

Hi @shntnu https://github.com/shntnu, here what I've observed:

ME-0328 seems to be indeed a duplicate, where one of the repeats doesn't have stereochemistry information: [image: image] https://user-images.githubusercontent.com/79095854/279512162-b848ac4b-b67c-492d-a256-afb7101172c0.png

quinine and quinidine are isomers: [image: image] https://user-images.githubusercontent.com/79095854/279512178-03c60289-ca3d-48c5-9903-9b9fcb345242.png

The code for making the figures:

import pandas as pdfrom rdkit import Chemfrom rdkit.Chem import Draw df = ( pd.read_csv( "https://raw.githubusercontent.com/jump-cellpainting/JUMP-Target/09f11aefd6b550cfb2d2074e38a65c3be39ddc39/JUMP-Target-2_compound_metadata.tsv", sep="\t", ) .query("pert_iname.str.contains('ME-0328|quinidine|quinine')") .assign( pubchem_cid=lambda x: x["pubchem_cid"].astype(int), ) ) for names in [["ME-0328"], ["quinidine", "quinine"]]: subset = @.***)")

plot the two molecules in a molecular grid

mols = [Chem.MolFromSmiles(smi) for smi in subset["smiles"]]
img = Draw.MolsToGridImage(
    mols,
    subImgSize=(300, 300),
    molsPerRow=2,
    returnPNG=True,
    legends=subset["pubchem_cid"].astype(str).tolist(),
)
display(img)

— Reply to this email directly, view it on GitHub https://github.com/jump-cellpainting/JUMP-Target/issues/9#issuecomment-1788019358, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN34ZTYU2HRY4MKJV76NDK3YCFQK3AVCNFSM4V2JF662U5DIOJSWCZC7NNSXTN2JONZXKZKDN5WW2ZLOOQ5TCNZYHAYDCOJTGU4A . You are receiving this because you were mentioned.Message ID: @.***>

David-Araripe commented 8 months ago

@srijitseal This is the third compound pair from my first comment. Where that doesn't have the stereochemistry information. PubChem CIDs 101290202 and 16154490

srijitseal commented 8 months ago

The smiles also seem to be broken? I see N with 7 bonds and C=O floating around. Is it possible that it’s a fault of rdkit Draw.

If it’s not too late in your time zone, can you get the smiles from pubchem/chembl/drugbank and draw that? See if that repairs it? If that does repair, then we need to think about replacing the smiles with the proper ones,

Best, Srijit

On Tue, Oct 31, 2023 at 5:32 PM David Araripe @.***> wrote:

Again seems like the same but without stereochemistry information: [image: image] https://user-images.githubusercontent.com/79095854/279520428-38c7b0d4-6494-4335-b11a-74243e39e377.png

— Reply to this email directly, view it on GitHub https://github.com/jump-cellpainting/JUMP-Target/issues/9#issuecomment-1788065969, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN34ZTYAYHAVSRMZR5EILPDYCFU5LAVCNFSM4V2JF662U5DIOJSWCZC7NNSXTN2JONZXKZKDN5WW2ZLOOQ5TCNZYHAYDMNJZGY4Q . You are receiving this because you were mentioned.Message ID: @.***>

David-Araripe commented 8 months ago

Here's the molecular drawing but made with the PubChem smiles instead: image

I wanted to check the difference between the SMILES that are currently registered in the smiles column and the ones from PubChem so compared their Morgan fingerprints (ignoring chirality). Here's the code:

from io import StringIO
from rdkit.Chem import AllChem, DataStructs

data = """broad_sample  pert_iname  pubchem_cid target  InChIKey    smiles
BRD-K58049875-001-03-9  thiostrepton    16154490    FOXM1   NSFFHOGKXHRQEW-AIHSUZKVSA-N CC[C@H](C)[C@@H]1N[C@@H]2C=Cc3c(cc(nc3[C@H]2O)C(=O)O[C@H](C)[C@@H]2NC(=O)c3csc(n3)[C@@H](NC(=O)[C@H]3CSC(=N3)\C(NC(=O)[C@@H](NC(=O)c3csc(n3)[C@]3(CCC(=N[C@@H]3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)[C@H](C)NC(=O)C(=C)NC(=O)[C@H](C)NC1=O)[C@@H](C)O)=C\C)[C@](C)(O)[C@@H](C)O)[C@H](C)O
BRD-A20697603-001-07-2  thiostrepton    101290202   FOXM1   NSFFHOGKXHRQEW-DVRIZHICSA-N CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O"""

df = pd.read_csv(StringIO(data), sep="\t")

def get_info_from_pubchem(cid):
    cpds = get_compounds(cid)
    assert len(cpds) == 1, "More than one compound found"
    return cpds[0].to_dict(
        properties=["isomeric_smiles", "inchikey", "iupac_name", "synonyms"]
    )

def get_morgan_fp(smi):
    mol = Chem.MolFromSmiles(smi)
    fp = AllChem.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048, useChirality=False)
    return fp

def check_same_fp(fp1, fp2):
    return DataStructs.FingerprintSimilarity(fp1, fp2)

pubchem_info = df.apply(
    lambda x: get_info_from_pubchem(x["pubchem_cid"]), axis=1, result_type="expand"
)
pubchem_info = pubchem_info.rename(
    columns={c: f"pubchem_{c}" for c in pubchem_info.columns}
)
df = pd.concat([df, pubchem_info], axis=1)

fp_JUMP_smiles = subset.smiles.apply(get_morgan_fp).tolist()
fp_pubchem_smiles = subset.pubchem_isomeric_smiles.apply(get_morgan_fp).tolist()
cids = subset.pubchem_cid.tolist()

for cid, fp1, fp2 in zip(cids, fp_JUMP_smiles, fp_pubchem_smiles):
    print(
        f"CID: {cid}, fingerprint similarity (JUMP v.s. PubChem): {check_same_fp(fp1, fp2)}"
    )

and the output

>>>CID: 16154490, fingerprint similarity (JUMP v.s. PubChem): 1.0
>>>CID: 101290202, fingerprint similarity (JUMP v.s. PubChem): 0.46788990825688076

So there seems to be something wrong with the smiles from BRD-A20697603-001-07-2 indeed. Maybe consider using the smiles from PubChem since the CIDs are already there? These would be the new smiles:

{
    "BRD-K58049875-001-03-9": "CC[C@H](C)[C@H]1C(=O)N[C@H](C(=O)NC(=C)C(=O)N[C@H](C(=O)N[C@]23CCC(=N[C@@H]2C4=CSC(=N4)[C@H]([C@H](OC(=O)C5=NC6=C(C=C[C@H]([C@@H]6O)N1)C(=C5)[C@H](C)O)C)NC(=O)C7=CSC(=N7)[C@@H](NC(=O)[C@H]8CSC(=N8)/C(=C/C)/NC(=O)[C@@H](NC(=O)C9=CSC3=N9)[C@@H](C)O)[C@@](C)([C@@H](C)O)O)C1=NC(=CS1)C(=O)NC(=C)C(=O)NC(=C)C(=O)N)C)C",
    "BRD-A20697603-001-07-2": "CCC(C)C1C(=NC(C(=NC(=C)C(=NC(C(=NC23CCC(=NC2C4=CSC(=N4)C(C(OC(=O)C5=NC6=C(C=CC(C6O)N1)C(=C5)C(C)O)C)N=C(C7=CSC(=N7)C(N=C(C8CSC(=N8)/C(=C\\C)/N=C(C(N=C(C9=CSC3=N9)O)C(C)O)O)O)C(C)(C(C)O)O)O)C1=NC(=CS1)C(=NC(=C)C(=NC(=C)C(=N)O)O)O)O)C)O)O)C)O"
}
shntnu commented 8 months ago

Really helpful!

At this point, we should go back to the source of the metadata https://repo-hub.broadinstitute.org/repurposing to figure this out, because that may also resolve some of the other inconsistencies.

For example, here it is for the two samples of thiostrepton from repurposing_samples_20200324.txt:

broad_id    pert_iname  qc_incompatible purity  vendor  catalog_no  vendor_name expected_mass   smiles  InChIKey    pubchem_cid deprecated_broad_id
BRD-A20697603-001-07-2  thiostrepton    0   42.09   MicroSource 1505111 THIOSTREPTON    "1,663.49"  CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O NSFFHOGKXHRQEW-DVRIZHICSA-N 101290202   
BRD-K58049875-001-03-9  thiostrepton    0   96.6    MedChemEx   HY-B0990    Thiostrepton    "1,663.49"  CC[C@H](C)[C@@H]1N[C@@H]2C=Cc3c(cc(nc3[C@H]2O)C(=O)O[C@H](C)[C@@H]2NC(=O)c3csc(n3)[C@@H](NC(=O)[C@H]3CSC(=N3)\C(NC(=O)[C@@H](NC(=O)c3csc(n3)[C@]3(CCC(=N[C@@H]3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)[C@H](C)NC(=O)C(=C)NC(=O)[C@H](C)NC1=O)[C@@H](C)O)=C\C)[C@](C)(O)[C@@H](C)O)[C@H](C)O   NSFFHOGKXHRQEW-AIHSUZKVSA-N 16154490    BRD-A89819812-001-02-8

We might as well just retrieve all these annotations over again from the source. Will report back here later, but all this info is super useful.

@David-Araripe Thank you again!

srijitseal commented 8 months ago

I think that's a great idea. I think in this particular case, the one with the higher purity is better, but you can see that they are indeed the same compound for ML purposes (same molecular weight as well). The second SMILES has data on stereoisomers, the first doesn't.

I am happy to look at all 300+ molecules individually and then we can make a call on what is the number of unique compounds (unique based on chemistry/stereoisomers).

Best, Srijit ᐧ

On Wed, 1 Nov 2023 at 22:02, Shantanu Singh @.***> wrote:

Really helpful!

At this point, we should go back to the source of the metadata https://repo-hub.broadinstitute.org/repurposing to figure this out, because that may also resolve some of the other inconsistencies.

For example, here it is for the two samples of thiostrepton from repurposing_samples_20200324.txt:

broad_id pert_iname qc_incompatible purity vendor catalog_no vendor_name expected_mass smiles InChIKey pubchem_cid deprecated_broad_id BRD-A20697603-001-07-2 thiostrepton 0 42.09 MicroSource 1505111 THIOSTREPTON "1,663.49" CCC(C)C1NC2C=Cc3c(cc(nc3C2O)C(=O)OC(C)C2NC(=O)c3csc(n3)C(NC(=O)C3CSC(=N3)\C(NC(=O)C(NC(=O)c3csc(n3)C3(CCC(=NC3c3csc2n3)c2nc(cs2)C(=O)NC(=C)C(=O)NC(=C)C(N)=O)NC(=O)C(C)NC(=O)C(=C)NC(=O)C(C)NC1=O)C(C)O)=C/C)C(C)(O)C(C)O)C(C)O NSFFHOGKXHRQEW-DVRIZHICSA-N 101290202
BRD-K58049875-001-03-9 thiostrepton 0 96.6 MedChemEx HY-B0990 Thiostrepton "1,663.49" @.(C)[C@@H]1N[C@@@*.**@*.(C)[C@@H]2NC(=O)c3csc(n3)[C@@@.3CSC(=N3)\C(NC(=O)C@@HC@@HO)=C\C)C@(O)[C@@@.***(C)O NSFFHOGKXHRQEW-AIHSUZKVSA-N 16154490 BRD-A89819812-001-02-8

We might as well just retrieve all these annotations over again from the source. Will report back here later, but all this info is super useful.

@David-Araripe https://github.com/David-Araripe Thank you again!

— Reply to this email directly, view it on GitHub https://github.com/jump-cellpainting/JUMP-Target/issues/9#issuecomment-1789943146, or unsubscribe https://github.com/notifications/unsubscribe-auth/AN34ZTYV5CTSIDKYFTGLGPLYCL5KLAVCNFSM4V2JF662U5DIOJSWCZC7NNSXTN2JONZXKZKDN5WW2ZLOOQ5TCNZYHE4TIMZRGQ3A . You are receiving this because you were mentioned.Message ID: @.***>

srijitseal commented 3 months ago

Refer to /jump-cellpainting/datasets/issues/80 for detailed analysis.

srijitseal commented 3 months ago

There are 301 unique compounds in this dataset.

We found there are 5 compounds with duplicate entries, hence 301 and not 306 unique compounds. After standardization, we don't see this problem anymore In the original Target2 dataset, they seemed different because of the following.

1.0

Image

It seems like the pair of compounds is a tautomer 2.0

Image

This one has a difference in one stereocenter (stated v.s. not shown) 3.0

Image

ME-0328 seems to be indeed a duplicate, where one of the repeats doesn't have stereochemistry information. 4.0

Image

quinine and quinidine are isomers, we should keep both as "unique", they have different biological signal, so no data leak when using cell painting, but caution is advised when using ECFP, that would leak data as it cant differentiate stereochemistry. 5.0

Image

For thiostrepton, connectivity is same, it seems stereochemistry is different