PaccMann's repositories
chemical_representation_learning_for_toxicity_prediction
Chemical representation learning paper in Digital Discovery
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
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
paccmann_chemistry
Generative models of chemical data for PaccMann^RL
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
paccmann_predictor_tf
Tensorflow implementation of PaccMann (drug sensitivity prediction)
paccmann_polymer
Graph-regularized VAE and the impact of topology on learned representations
reinvent_models
PaccMann fork of Reinvent Models
guacamol
Benchmarks for generative chemistry
guacamol_baselines
Baselines models for GuacaMol benchmarks
moses
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
paccmann.github.io
PaccMann website