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🦠 Model Request: PhaKinPro Incorporation #1114
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Hi @sucksido before approving the model we need to add a bit more information to it. You can see any other working model example to get an idea. Feel free to edit the first comment of the issue for a first attempt! |
Hi @miquelduranfrigola , I have added more details, please let know if that is enough |
Hi @sucksido description should be longer in and the tags need to be correct. In this case, this model is not related to malaria, for example. @GemmaTuron @Zainab-ik could you offer assistance/guidelines on writing more complete and correct annotation for the model? Then I will be happy to approve it. |
Hi @sucksido Please use as guidance what is set in other models. The description must have a minimmum of 250 characters, which you can get by reading the abstract of the paper and summarising it |
Hi @sucksido The information approved for incorporation in the metadata is listed here. For example, ADME and Metabolism would be appropriate tags in this case. |
Hi @GemmaTuron , I have updated it |
@sucksido |
/approve |
New Model Repository Created! 🎉@sucksido ersilia model respository has been successfully created and is available at: Next Steps ⭐Now that your new model respository has been created, you are ready to start contributing to it! Here are some brief starter steps for contributing to your new model repository:
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@GemmaTuron @miquelduranfrigola @JHlozek @DhanshreeA Findings on PhaKinPro Incorporation: The GitHub repository for PhaKinPro (https://github.com/molecularmodelinglab/PhaKinPro) has seen minimal updates or support in the past two years. |
it seems like i cant attach .py files here but I have attached on slack |
Current output: [['SMILES', 'Hepatic Stability', 'Microsomal Half-life Sub-cellular', 'Microsomal Half-life Tissue', 'Renal Clearance', 'BBB Permeability', 'CNS Activity', 'CACO2', 'Plasma Protein Binding', 'Plasma Half-life', 'Microsomal Intrinsic Clearance', 'Oral Bioavailability'], ['CCCCCCCC', '', '', '', '', '', '', '', '', '', '', ''], ['CCCCOCNC', '', '', '', '', '', '', '', '', '', '', ''], ['CCCCNCCCOC', '', '', '', '', '', '', '', '', '', '', '']] @GemmaTuron does this look |
Hi @sucksido This is looking good, but I think you might be missing the model checkpoints as all the values are empty? This is the format we want to get from the phakinpro.py function, but make sure the results are actually calculated |
@GemmaTuron I have set up a new Ubuntu env, I also have installed VSCode and Miniconda 3, im bust with model incorporation , busy debugging at the moment, still getting some errors at the moment, currently debugging to make sure we are getting reuslts |
Output: [['SMILES', 'Hepatic Stability', 'Microsomal Half-life Sub-cellular', 'Microsomal Half-life Tissue', 'Renal Clearance', 'BBB Permeability', 'CNS Activity', 'CACO2', 'Plasma Protein Binding', 'Plasma Half-life', 'Microsomal Intrinsic Clearance', 'Oral Bioavailability'], ['COc1c(/C=N/NC(N)=S)c(CC(C)Cl)c(OC)c2c1OCO2', 0.6733, 0.71, 0.56, None, 0.632, 0.8320000000000001, 0.6679999999999999, 0.596, 0.637, 0.54, 0.5413], ['O=N+c1ccc(N/N=C/c2ccc(N+[O-])s2)cc1', 0.6347, 0.74, 0.52, None, 0.72, 0.828, 0.604, 0.524, 0.5820000000000001, 0.66, 0.58], ['CCCCOc1ccc(N2CC(C(=O)NCCc3ccc(OC)c(OC)c3)CC2=O)cc1', None, 0.53, 0.53, None, 0.604, 0.88, 0.584, 0.516, 0.624, 0.564, 0.54], ['O=C(Nc1cc(Cl)ccc1O)c1ccco1', 0.5533, 0.7, 0.8, None, 0.54, 0.892, 0.688, 0.7559999999999999, None, 0.708, 0.536]] |
Command to run inside: eos39dp/model/framework/ : bash run.sh . ~/test.csv ~/out.csv |
test.csv |
Hi @sucksido I've provided feedback on the PR. The automated tests should pass - they provide detailed info on why are they failing, so have a look to amend. |
Hi @GemmaTuron noted thanks, I will fix |
I have fixed the model dependencies in https://github.com/ersilia-os/eos39dp/commits/main/ But there is still work to be done:
|
Thanks @GemmaTuron - do we have an assignee? |
CLOSING COMMENT:
@miquelduranfrigola suggested the following:
The classifications are binary and multi class. I’ve attached an example here of a multiclass PK parameter |
I understand, this is very useful. We will take it from here! |
Thanks @kurysauce for the nice summary |
Model Name
Pharmacokinetics Profiler (PhaKinPro)
Model Description
Pharmacokinetics Profiler (PhaKinPro) is a recently developed web-based tool that helps predict the pharmacokinetic (PK) properties of drug candidates. In essence, it assists scientists in determining how a drug will behave within the body.
Pharmacokinetics refers to the processes by which a drug is absorbed, distributed, metabolized, and excreted (ADME) . Understanding these processes is critical in drug development, as they can affect a drug's efficacy and safety. For example, a drug that is rapidly metabolized may not be effective in the body, while a drug that is slowly excreted may accumulate to toxic levels.
Slug
PhaKinPro
Tag
Microsomal stability, ADME, Metabolism
Publication
https://pubs.acs.org/doi/10.1021/acs.jmedchem.3c02446
Source Code
https://github.com/molecularmodelinglab/PhaKinPro
License
MIT
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