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    ivy1997
    @ivy1997:matrix.org
    [m]
    I know this might be because of the old version code (at least for "deepchem.models.tensorgraph.layers"). But I cannot find the solution for the third line, which I want to import Tensorgraph. Could someone please give me help? Thank you very much.
    Bharath Ramsundar
    @rbharath
    TensorGraph is no longer present in DeepChem. Are you working with code written for an older version of DeepChem?
    1 reply
    The Book code has been converted to work with the latest DeepChem if that's what you're working with @ivy1997:matrix.org
    alat-rights
    @alat-rights
    Yes, that is correct! @davidRFB
    Gemechis Degaga
    @GsGithub17
    @TrikiSadok for sequence featurization try looking into propy.
    Bharath Ramsundar
    @rbharath
    @GsGithub17 I'm not familiar with propy. Would you mind giving me a quick pointer to it? (Couldn't find it after a quick google search)
    Gemechis Degaga
    @GsGithub17
    @rbharath check it here https://pypi.org/project/propy3/
    Bharath Ramsundar
    @rbharath
    @alat-rights ^ propy3 looks like a cool tool that might be of interest to you
    alat-rights
    @alat-rights
    Thank you! I’ll check it out.
    Gemechis Degaga
    @GsGithub17
    @alat-rights @rbharath I have used propy for vaccine peptide designing using GAN, if you are lookikg for potentially significant collaboration let me know.
    Bharath Ramsundar
    @rbharath
    @GsGithub17 That sounds really cool! If you're open, consider contributing some of your code into DeepChem :). I think a lot of folks would be interested in your work
    davidRFB
    @davidRFB
    @alat-rights I also found this git hub that could be useful https://github.com/sacdallago/bio_embeddings hope it helps :D
    alat-rights
    @alat-rights
    Thank you! @davidRFB
    Gemechis Degaga
    @GsGithub17
    @rbharath hoping to do that in the near future!
    9H Fluorene
    @ninehfluorene:matrix.org
    [m]
    Hello i'm new here (and sorry for my poor english skill)
    anyway..
    i has been working on predict some Quantum propoties by Used QM9 Database
    (thanks Deepchem for making it easy )
    after training (i used tensorflow keras) i want to use my model to predict some of propoties
    (in my case is HOMO,LUMO,Bandgap)
    but the result come out is numpy arrays not HOMO,LUMO,Bandgap
    i'm try to find someway to convert numpy arrays back to readable data (Energy)
    but i can't find that
    so i has a question:
    can i convert numpy arrays back to readable data ?
    is there example of prediction of Quantum Propoties that make readable data?
    (thanks for your help)
    9H Fluorene
    @ninehfluorene:matrix.org
    [m]
    kurokawaikki
    @kurokawaikki
    Hi,
    I am new to deepchem. Currently, I am now try to train a model to calculate the complxation energy of Host-Guest interaction. Therefore, I am trying to find the avalible dataset on the internet. So far, I have collected only around 500 samples... Is there a dataset in the deepchem? Or do you have any suggestion for me to find the datasets? Thank you very much!
    Bharath Ramsundar
    @rbharath
    @ninehfluorene:matrix.org What do you mean by readable data? You can visualize numpy arrays in the console but I think that's now what you're asking for
    4 replies
    @kurokawaikki Could you clarify what you mean by host-guest interaction? Sorry my knowledge of materials science might be lacking here!
    9H-Fluorene (Kasitinard M.)
    @ninehfluorene:matrix.org
    [m]
    9H-Fluorene (Kasitinard M.)
    @ninehfluorene:matrix.org
    [m]
    i'm try to recreate this paper they use 15 propoties
    https://pubs.acs.org/doi/10.1021/acs.jpca.8b09376
    Bharath Ramsundar
    @rbharath
    You'll want to pick out the same 15 properties as in that paper
    9H-Fluorene (Kasitinard M.)
    @ninehfluorene:matrix.org
    [m]
    yes or just one or two as well
    9H-Fluorene (Kasitinard M.)
    @ninehfluorene:matrix.org
    [m]
    i might do something wrong , maybe my model is really bad that why it not close to the input label data
    9H-Fluorene (Kasitinard M.)
    @ninehfluorene:matrix.org
    [m]
    again thanks for your help @rbharath
    Atreya Majumdar
    @atreyamaj
    Hey everyone! I was wondering if we could maybe add a description to the PyPI release of Deepchem? I would love to contribute and write the description as well if someone could point me in the right direction as to where I can start writing it!
    alat-rights
    @alat-rights
    I think that would be really helpful! I’m not too sure how that would work. Maybe @rbharath would have a better idea?
    alat-rights
    @alat-rights
    Would it make sense for us to separate the flaky tests from the non-flaky tests in the test-suite so that our “build passing/failing” badge is more useful?
    Bharath Ramsundar
    @rbharath
    @atreyamaj That would be a great idea :). If you'd like to help, DM me your pypi username and I'll give you access to edit the description. It might be good to make the description documented in the main repo and add a note to the release docs to update the pypi description if needed
    @alat-rights It's a little tricky basically. The flaky tests are already separated out so most of their failures don't affect the CI but we have a hundreds of tests and we run into edge cases that are hard to route around. The only solution right now is to just look into each case individually and try to understand the cause of the failure, but maybe there's a better idea
    Atreya Majumdar
    @atreyamaj
    Thank you, I have sent you a DM
    meihua
    @MhDang
    Hello! I am new to AI drug and deepchem library. I have to say this work is remarkable and very user-friendly to beginners like me. I am now trying to play with the models and reproduce the results in http://moleculenet.ai/latest-results, I find the benchmark scripts in deepchem/examples/benchmark.py, I am wondering whether you happen to keep the record of hyperparameters to reproduce the results ?
    Bharath Ramsundar
    @rbharath
    @MhDang Welcome to the project! I'd suggest checking out the new moleculenet repo: https://github.com/deepchem/moleculenet
    We have a new leaderboard up with maintained results. The original model benchmarks were run on TF 1.x and the underlying libraries have changed a lot so it's not easy to directly replicate those results
    meihua
    @MhDang
    @rbharath Thanks a lot for the reference, let me try to reproduce the new results!
    kurokawaikki
    @kurokawaikki
    @rbharath I am sorry for the late responses. In my research, the host compounds are the cyclic (or ring like) compounds such as valinomycin. I try to find a guest compound that can fit into the ring area and predicts the binding energy. Host–guest chemistry is currently being tested in SAMPL challenge. Since I would like to build a prediction model, I hope I could have as many sample as possible. Therefore, is there a dataset in the deepchem? Or do you have any suggestion for me to find the datasets? Thank you very much!
    Bharath Ramsundar
    @rbharath
    @kurokawaikki Ah, I see! That makes sense. Hmm, unfortunately, I'm not aware of a good dataset in DeepChem for host-guest interactions. Closest would be pdbbind but that's for more generic protein-ligand interactions and not for the types of host-guest interactions you're envisioning