PaccMann/paccmann_chemistry

Stars
13
Rank 1,512,713 (Top 30 %)
Language
Python
License
MIT License
Created about 5 years ago
Updated over 1 year ago

PaccMann/paccmann_chemistry

PaccMann

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

Generative models of chemical data for PaccMann^RL

chemical_representation_learning_for_toxicity_prediction

Chemical representation learning paper in Digital Discovery

Jupyter Notebook

paccmann_predictor

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

paccmann_proteomics

PaccMann models for protein language modeling

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

paccmann_rl

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

TITAN

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

Jupyter Notebook

paccmann_datasets

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

paccmann_sarscov2

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

fdsa

A fully differentiable set autoencoder

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

paccmann_generator

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

paccmann_omics

Generative models for transcriptomics profiles and proteins

transcriptomic_signature_sampling

reinvent_models

PaccMann fork of Reinvent Models

paccmann_polymer

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