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5

DrugBank seems to have a tool to map ATC codes to drug names and DrugBank IDs. A quick look in the XSD schema on the release page suggests the complete database includes ATC codes for drugs, you could then do a fuzzy match of the BioBank names against all of DrugBank's synonyms, or match on some other data (e.g. canonicalised SMILES) if available. The ...


5

The CART tool let's you upload a set of names and map them (optionally in a fuzzy way) to STITCH 4 identifiers, and then use those to map to ATC codes (using the chemicals sources download file). It's a bit indirect, and I'm not sure what CART will do with the dosage info you mention.


3

It turns out there is a tool that does this beautifully. I used the proteinToGenome function from the ensembldb package. The code looks like this: # Define the Ensemble database object for the edbX <- filter(EnsDb.Hsapiens.v75, filter = ~ seq_name == "X") # Set the first and last amino acid you want to convert and the ensembl protein id GAGE10_prt &...


3

Since this question's been asked there's been a (what looks like a first useful?) release of Drug Ontology DRON. Perhaps worth checking again?


3

You could take a look at the rentrez package for R. You can use the entrez_search function to get the GEO IDs for the studies related to your query, and then the entrez_summary function for the results. So, for example: x <- rentrez::entrez_search(db="gds", term=paste0("tolvaptan AND gse[ETYP]"), use_history = T) res <- ...


2

One of the problems I've found with GEO and even ArrayExpress is that it seems like there are tons of false positives that come up in the search. Additionally, there may be a ton of results that you miss because you didn't include the synonym of the drug. In order to make sure I have all the information relating to name, etc, you can write a script which ...


2

Cosmic Cell Lines Project provides gene expression data for 1000+ cell lines (including NCI-60 panel together with NCI-H23 cell line). For each gene in a cell line, you can get Z-score and over/under-expression status calculated across all tumor samples diploid for that gene.


2

CellMiner portal has plenty of data about NCI-60 cell lines, inclusive microarray gene-expression. Also, NCI-60 is a subset of the ~1000 cell lines covered by the GDSC project for which you have multiple types of measurements (e.g. copy-number, mutation, gene-expression).


2

I don't know any python (or R package) to do this), but this is how I would do it based on these matrices: Do a multidimensional scaling (MDS) plot of the target_similarity matrix. Explore the visualization: Find which drug targets the drug closest to the 0,0 point in the 2D space However this doesn't say anything about the affinity between the drug and ...


1

A positive logP is good (hydrophobic), but high results in ADMET problems (cytochromes break overly hydrophobic compounds) and aggregation (gunk, tar, crud are words happily thrown around). However, in most cases the logP is not greater than +3 because good binding is result of hydrogen bonds etc —some bad algorithms will suggest a polyaromatic hydrocarbon ...


1

It would be great if you can provide more information about the gene expression data set. If you have response to single drug, the general approach is to find the deferentially expressed genes with tools like DESeq2, or make a heat map based on the expression of your listed genes to see the drug effect before and after the treatment. But, if you want to ...


1

That PDB page includes a publication reference and abstract, containing the following text: Mammalian CYP450s recognize and metabolize diverse xenobiotics such as drug molecules, environmental compounds and pollutants. ... Here we describe the crystal structure of a human CYP450, CYP2C9, both unliganded and in complex with the anti-coagulant drug warfarin. ...


1

If you have information on which part of the protein are interacting, and its sequence, you can search for homologous or similar sequences in other proteins. This would give you a list of potential targets and probably a similarity score which you can also use. Then you would have something to run simulations on. And I think that with 3D structure you ...


1

Potential pitfall! I'm not sure about predictive models, but you need to be aware of a potential pitfall in blindly aligning PDX or PDO based sequencing data without first removing contaminating host organism reads, as otherwise these will lead to a lot of false positive variants caused by miss alignment. In my experience even a small mount of host ...


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