PaccMann/TITAN PaccMann/TITAN

  • Stars
    star
    27
  • Rank 905,827 (Top 18 %)
  • Language
    Jupyter Notebook
  • License
    MIT License
  • Created over 3 years ago
  • Updated over 1 year ago
Twitter logo Share LinkedIn logo Share Facebook logo Share
PaccMann/TITAN
github

PaccMann

Reviews

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Repository Details

Code for "T Cell Receptor Specificity Prediction with Bimodal Attention Networks" (https://doi.org/10.1093/bioinformatics/btab294, ISMB 2021)

More Repositories

1

chemical_representation_learning_for_toxicity_prediction

Chemical representation learning paper in Digital Discovery
Jupyter Notebook
52
star
2

paccmann_predictor

PyTorch implementation of bimodal neural networks for drug-cell (pharmarcogenomics) and drug-protein (proteochemometrics) interaction prediction
Python
43
star
3

paccmann_proteomics

PaccMann models for protein language modeling
Python
37
star
4

paccmann_kinase_binding_residues

Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889
Python
34
star
5

paccmann_rl

Code pipeline for the PaccMann^RL in iScience: https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6
30
star
6

paccmann_datasets

pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/
Python
26
star
7

paccmann_sarscov2

Code for paper on automation of discovery and synthesis of targeted molecules: https://iopscience.iop.org/article/10.1088/2632-2153/abe808
18
star
8

fdsa

A fully differentiable set autoencoder
Python
17
star
9

paccmann_chemistry

Generative models of chemical data for PaccMann^RL
Python
13
star
10

paccmann_gp

PyTorch/skopt based implementation of Bayesian optimization with Gaussian processes - build to optimize latent spaces of VAEs to generate molecules with desired properties
Python
10
star
11

paccmann_generator

Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).
Python
8
star
12

paccmann_omics

Generative models for transcriptomics profiles and proteins
Python
7
star
13

transcriptomic_signature_sampling

Python
1
star
14

reinvent_models

PaccMann fork of Reinvent Models
Python
1
star
15

paccmann_polymer

Graph-regularized VAE and the impact of topology on learned representations
Python
1
star