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@alat-rights I also found this git hub that could be useful https://github.com/sacdallago/bio_embeddings hope it helps :D
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)
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)
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!
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!
@kurokawaikki Could you clarify what you mean by host-guest interaction? Sorry my knowledge of materials science might be lacking here!
i'm try to recreate this paper they use 15 propoties
https://pubs.acs.org/doi/10.1021/acs.jpca.8b09376
https://pubs.acs.org/doi/10.1021/acs.jpca.8b09376
You can find a description of the qm9 labels here: https://deepchem.readthedocs.io/en/latest/api_reference/moleculenet.html#deepchem.molnet.load_qm9
You'll want to pick out the same 15 properties as in that paper
i might do something wrong , maybe my model is really bad that why it not close to the input label data
@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
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 ?
@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
You can find the original hyperparameters at https://github.com/deepchem/deepchem/blob/2.3.0/deepchem/molnet/preset_hyper_parameters.py
@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!
@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
Hi everyone,
I have working with generative modelling for Molecules (SMILES) and I was exploring the AspuruGuzikAutoEncoder given on seqtoseq.py. The original paper has a step for Gaussian Process step for exploring the latent space and I couldn't find its implementation in deepchem. It would be really helpful if someone could suggest me generative models research or frameworks which can provide us with the option of exploring the latent space for finding more optimized molecules.
reference -
- Aspuru Guzik's Mol VAE paper - https://arxiv.org/abs/1610.02415 (Gaussian Process is given in Page 11 , Optimization of molecules via properties)
Thanks in advance :)