PaccMann

chemical_representation_learning_for_toxicity_prediction

Chemical representation learning paper in Digital Discovery

fdsa

A fully differentiable set autoencoder

paccmann_chemistry

Generative models of chemical data for PaccMann^RL

paccmann_datasets

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

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_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_rl

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

paccmann_sarscov2

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

TITAN

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

PaccMann/chemical_representation_learning_for_toxicity_prediction

Chemical representation learning paper in Digital Discovery

PaccMann/paccmann_predictor

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

PaccMann/paccmann_proteomics

PaccMann models for protein language modeling

PaccMann/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/paccmann_rl

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

PaccMann/TITAN

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

PaccMann/paccmann_datasets

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

PaccMann/paccmann_sarscov2

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

PaccMann/fdsa

A fully differentiable set autoencoder

PaccMann/paccmann_chemistry

Generative models of chemical data for PaccMann^RL

PaccMann/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/paccmann_generator

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

PaccMann/paccmann_omics

Generative models for transcriptomics profiles and proteins

PaccMann/paccmann_predictor_tf

Tensorflow implementation of PaccMann (drug sensitivity prediction)

PaccMann/paccmann_polymer

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

PaccMann/reinvent_models

PaccMann fork of Reinvent Models

PaccMann/transcriptomic_signature_sampling

PaccMann/guacamol

Benchmarks for generative chemistry

PaccMann/docs

Documentation for PaccMann service

PaccMann/guacamol_baselines

Baselines models for GuacaMol benchmarks

PaccMann/moses

Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

PaccMann/paccmann.github.io

PaccMann website

PaccMann/tape

Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.