Coder Social home page Coder Social logo

chaitjo / geometric-rna-design Goto Github PK

View Code? Open in Web Editor NEW
115.0 6.0 10.0 140.59 MB

gRNAde: Geometric Deep Learning for RNA Design

Home Page: https://arxiv.org/abs/2305.14749

License: MIT License

Python 3.61% Jupyter Notebook 96.39%
geometric-deep-learning graph-neural-networks inverse-design pytorch pytorch-geometric rna-structure biomolecule-design

geometric-rna-design's People

Contributors

chaitjo avatar eltociear avatar

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar  avatar  avatar  avatar  avatar

geometric-rna-design's Issues

Where is RibonanzaNet?

Hello,

Thanks for your paper and open source codes. Currently I am working on the implementation of your codes. I found one problem which is missing from tools.ribonanzanet.network import RibonanzaNet in notebooks/design.ipynb. How can I solve this issue?

Thanks in advance!

Best,

eval和test的速度很慢,这种情况正常吗

作者你好,
请问我在复现实验的时候,出现了eval 和 test的时间很长的现象,这是正常情况吗?
image

因为数据集和原始数据集有了一些更新,导致出现了很多新的数据(这些数据中有部分的 sec_struc中 左括号数量多于右括号,导致计算对应的adj的时候报错),于是我对原始的代码做了如下修改,将计算过程放到了try里面,如果是错误的就跳出:

def dotbracket_to_adjacency(sec_struct: str) -> np.ndarray: """ Convert secondary structure in dot-bracket notation to adjacency matrix. """ n = len(sec_struct) adj = np.zeros((n, n), dtype=np.int8) stack = [] empty_count = 0 for i, db_char in enumerate(sec_struct): if db_char == '(': stack.append(i) elif db_char == ')': try: j = stack.pop() adj[i, j] = 1 adj[j, i] = 1 except IndexError: break return adj

其他地方都没有改变

大概eval 和 test 每个都要接近8小时,查看占用,发现gpu长时间不用,一直是cpu在跑一个叫做contrafold的进程,希望大佬解惑。

Splits for benchmark

Hi,

Congrats on the great work. As I understand data/seqid_split.pt data/das_split.pt and data/structsim_split.pt contains the train, test, validation idx for different splits. And the corresponding pdbid_<chain> for those can be extracted from processed_df.csv. Can you provide this preprocessed processed_df.csv file or directly the PDB ids for easier access?

Thanks!

Ask for checkpoints

Hi @chaitjo,

Thanks for your great work first!
Could you provide a pretrained checkpoint to run inference directly?

Best regards,
Han

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. 📊📈🎉

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google ❤️ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.