Dissecting the Hype With Cheminformatics

A recent paper in Nature Biotechnology reported the use of AI in the discovery of inhibitors of DDR1, a kinase which has been implicated in fibrosis. Many who have worked in kinase drug discovery may have noticed that the most prominent compound (Compound 1) from this paper bears a striking resemblance to a certain marketed drug. Let's assume the compound appears familiar, but we can't specifically place it. How can we use Cheminformatics to find drugs similar to the compound highlighted in this paper? Let's take look.

In case you're interested in running the code, there is a Jupyter notebook on Github.  If you don't like code, skip to the bottom of this post for the punch line.  If you want a really easy way to run the code, try this link to a Binderized version.   This will allow you to run the notebook without downloading anything. Thanks to Peter Rose for showing me how to do this.

First, we will import the Python libraries that we'll need.  We'll also set a couple of flags to make the chemical structures prettier.
from chembl_webresource_client.new_client import new_client
from rdkit import Chem
from rdkit.Chem.Draw import MolsToGridImage
from rdkit.Chem.Draw import IPythonConsole
import pandas as pd
from rdkit.Chem import rdFMCS
from tqdm import tqdm
from rdkit.Chem import PandasTools
from rdkit.Chem import AllChem
from rdkit.Chem import rdDepictor
from rdkit.Chem.Fingerprints import FingerprintMols
from rdkit import DataStructs
rdDepictor.SetPreferCoordGen(True)
IPythonConsole.ipython_useSVG = True

We will start with Compound 1 from the paper.
compound_1_smiles = "Cc1ccc2c(Nc3cccc(c3)C(F)(F)F)noc2c1C#Cc1cnc2cccnn12"compound_1_mol = Chem.MolFromSmiles(compound_1_smiles)

A quick check to ensure that we have the correct SMILES.
compound_1_mol

Now let's use the newly released ChEMBL API to grab the SMILES for all of the small molecule drugs.  Once we have the SMILES and ChEMBL Ids for the drugs, we'll put this into a Pandas dataframe. 
molecule = new_client.molecule
approved_drugs = molecule.filter(max_phase=4)
small_molecule_drugs = [x for x in approved_drugs if x['molecule_type'] == 'Small molecule']
struct_list = [(x['molecule_chembl_id'],x['molecule_structures'])for x in small_molecule_drugs if x]
smiles_list = [(a,b['canonical_smiles']) for (a,b) in struct_list if b]
smiles_df = pd.DataFrame(smiles_list)
smiles_df.columns = ['ChEMBL_ID','SMILES']

Let's add a molecule column to the dataframe to make it easier to view the chemical structures
PandasTools.AddMoleculeColumnToFrame(smiles_df,'SMILES','Mol')

Now we'll add a fingerprint column to our Pandas table so that we can do a similarity search.
smiles_df['fp'] = [FingerprintMols.FingerprintMol(x) for x in smiles_df.Mol]

Next, we can generate a fingerprint for Compound 1 and use that to do a similarity search.
compound_1_fp = FingerprintMols.FingerprintMol(compound_1_mol)
smiles_df['fp_sim'] = [DataStructs.TanimotoSimilarity(compound_1_fp,x) for x in smiles_df.fp]
smiles_df.sort_values("fp_sim",inplace=True,ascending=False)

Let's look at the 5 most similar compounds.
top5_sim_df = smiles_df.head()
MolsToGridImage(top5_sim_df.Mol,molsPerRow=5,legends=["%.2f" % x for x in top5_sim_df.fp_sim])



Hmmm, the first compound above looks a lot like Compound 1.  Just to be certain that we've found what we need, why don't we try an alternate method of calculating similarity.  In this case, we'll calculate the number of atoms in the maximum common subgraph (MCS) for Compound 1 and each of the small molecule drugs.  MCS calculations are time-consuming so this isn't the sort of thing we want to do with a large database.  However, in this case we only have a few thousand drugs, so the calculation isn't prohibitive.  On my MacBook Pro, this takes about a minute and a half.

We'll start by defining a function that calculates the number of atoms in the MCS for two molecules.
def mcs_size(mol1,mol2):
    mcs = rdFMCS.FindMCS([mol1,mol2])
    return mcs.numAtoms

Next, we will run this over the small molecule drugs in our dataframe.
mcs_list = []
for mol in tqdm(smiles_df.Mol):
    mcs_list.append(mcs_size(compound_1_mol,mol))

We can add the number of atoms in the MCS to our dataframe.
smiles_df['mcs'] = mcs_list

Now we sort the dataframe by the number of atoms in the MCS and take a look at the 5 compounds with the largest MCS.
smiles_df.sort_values("mcs",inplace=True,ascending=False)
top5_mcs_df = smiles_df.head()
MolsToGridImage(top5_mcs_df.Mol,molsPerRow=5,legends=["%d" % x for x in top5_mcs_df.mcs])



While the two most similar molecules are the same as those we identified using the similarity search, we can see that the MCS search uncovers a few different molecules. Let's learn a bit more about the drug that is most similar to Compound 1. We can start by getting its ChEMBL identifier.
top5_mcs_df.ChEMBL_ID.to_list()[0]

We can use the ChEMBL API to get the names associated with this molecule.
molecule = new_client.molecule
m1 = molecule.get('CHEMBL1171837')
pd.DataFrame([(x['molecule_synonym'],x['syn_type']) for x in m1['molecule_synonyms']],columns=['molecule_synonym','syn_type'])



This molecule is Pontatinib.  A quick Google search shows us that this is a marketed drug originally developed as an inhibitor of BCR-ABL. Ponatinib is also a promiscuous inhibitor of a number of other kinases including DDR1.  In fact, extensive SAR around the activity of Ponatinib analogs against DDR1 was reported in a 2013 paper  in J.Med.Chem.

Let's generate a side-by-side visualization of Compound 1 and Ponatinib with the MCS highlighted.
ponatinib_mol = top5_mcs_df.Mol.to_list()[0]
compound_1_mcs = rdFMCS.FindMCS([compound_1_mol,ponatinib_mol])
mcs_query = Chem.MolFromSmarts(compound_1_mcs.smartsString)
AllChem.Compute2DCoords(mcs_query)
for m in [compound_1_mol,ponatinib_mol]: AllChem.GenerateDepictionMatching2DStructure(m,mcs_query)
compound_1_match = compound_1_mol.GetSubstructMatch(mcs_query)
ponatinib_match = ponatinib_mol.GetSubstructMatch(mcs_query)
MolsToGridImage([compound_1_mol,ponatinib_mol],highlightAtomLists=[compound_1_match,ponatinib_match],subImgSize=(400, 400))


Given the similarity of Compound 1 to a marketed drug with 9nM biochemical activity against DDR1 and good pharmacokinetics (PK), the activity and PK profiles of Compound 1 are not particularly surprising.

Comments

Post a Comment

Popular posts from this blog

AI in Drug Discovery 2023 - A Highly Opinionated Literature Review (Part I)

Image
Here’s the first part of my review of some interesting machine learning (ML) papers I read in 2023.  As with the previous editions , this shouldn’t be considered a comprehensive review.  The papers covered here reflect my research interests and biases, and I’ve certainly overlooked areas that others consider vital.  This post is pretty long, so I've split it into three parts, with parts II and III to be posted in the next couple of weeks.    I. Docking, protein structure prediction, and benchmarking II. Large Language Models, active learning, federated learning, generative models, and explainable AI III. Review articles 2023 was a bit of a mixed bag for AI in drug discovery.  Several groups reported that the deep learning methods for protein-ligand docking weren’t quite what they were initially cracked up to be.  AlphaFold2 became pervasive, and people started to investigate, with mixed success, the utility of predicted protein structures.  There were reports of significant advanc
Read more

Generative Molecular Design Isn't As Easy As People Make It Look

Image
I was taken aback by a recent CNBC article entitled “ Generative AI will be designing new drugs all on its own in the near future ”.  I should know better than to pay attention to AI articles in the popular press, but I feel that even scientists working in drug discovery may have a skewed perception of what generative AI can and can’t do.  To understand exactly what’s involved, it might be instructive to walk through a typical generative molecular design workflow and point out a few things.  First, these programs are far from autonomous.  Even when presented with a well-defined problem, generative algorithms produce a tremendous amount of nonsense.  Second, domain expertise is essential when sifting through the molecules produced by a generative algorithm.  Without a significant medicinal chemistry background, one can’t make sense of the results.  Third, while a few nuggets exist in the generative modeling output, a lot of work and good old-fashioned cheminformatics are required to ext
Read more

AI in Drug Discovery - A Highly Opinionated Literature Review (Part II)

Image
Picking up where we left off in Part I , this post covers several other ML in drug discovery topics that interested me in 2023.  Some areas, like large language models, are new, and most of the work is at the proof-of-concept stage.  Others, like active learning, are more mature, and several groups are starting to explore nuances of the methods.   Here’s the structure of Part II.  4. Large Language Models 5. Active Learning 6. Federated Learning 7. Generative Models 8. Explainable AI 9. Other Stuff 4. Large Language Models The emergence of GPT-4 and ChatGPT brought considerable attention to large language models (LLMs) in 2023.  In November and December, several large pharmas held “AI Day” presentations featuring LLM applications for clinical trial data analysis. Many of these groups demonstrated the ability of LLMs to ingest large bodies of unstructured clinical data and subsequently generate tables and reports based on natural language queries.  Aside from some very brief demos on co
Read more