tiejundong / flexpose Goto Github PK
View Code? Open in Web Editor NEWFlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.
License: MIT License
FlexPose, a framework for AI-based flexible modeling of protein-ligand binding pose.
License: MIT License
The web server only works when I use the provided input example but as soon as I use my protein and ligand it shows this error.
"FileNotFoundError: [Errno 2] No such file or directory: '/tmp/tmpdlk2psln/structure_output_path/0.pdb'
Traceback:
File "/data/lab_website/.conda/envs/streamlit/lib/python3.11/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 541, in _run_script
exec(code, module.dict)
File "/data/lab_website/lab_website_code/pages/1_FlexPose.py", line 141, in
view.addModel(grep_pdb_rename_chain(f'{structure_output_path}/0.pdb', 'ATOM', 'A'), 'pdb')
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/lab_website/lab_website_code/pages/1_FlexPose.py", line 132, in grep_pdb_rename_chain
lines = open(pdb_path, 'r').readlines()
^^^^^^^^^^^^^^^^^^^
Hi Tiejun,
Thanks for the brilliant work! I took a look at https://github.com/tiejundong/FlexPose/blob/master/FlexPose/utils/prediction.py and find the save_struct function only updates the sidechain angles. Since the alpha carbon coordinates are predicted, have you considered also changing the backbone?
Bests,
Great job!
But we would like to use this method locally. Could you provide the trained model? Thank you very much!
Hi Authors,
Thanks for the brilliant work! I am a little confused with the training process, where apo structures, holo-structures, or fake apo structures are randomly selected as input. For flexible binding, is it more intuitive to input apo structures and predict holo-structures in the training process?
Thanks!
Thanks for the work!
I tried to download the weights for local execution, but both at command line
and direct download the speed is slow (< 160 Kb/s) and fails at around 80%.
When trying the web server version, I noticed that aromatics and double-bonded in non-protein compounds tend
often to violate (more or less) co-planar constraints (could be a small setting for minimization in the web server?),
which is not happening in other systems such as DiffDock.
Moreover, big problems occurs when docking symmetric compounds that have multi-aromatics (see the attached example).
Atoms did not stay in place, as it seems that during inference the system cannot distinguish the aromatic ring to which each atom belongs to, and final things result messed up.
Thanks for any support!
Marco
Hi, your work is really good and solid! I'm wondering if you can upload your pretraining scripts for protein and ligand encoders? I think your pretraining tasks are quite interesting and informative. Thank you so much!
Hi Tiejun,
I noticed that the .chk
file seems to be uploaded to your personal website, and the download speed is about 150kb/s. Is there any other faster download method? For example, Google Drive or Baidu Cloud Disk.
Thank you.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.