{"payload":{"pageCount":1,"repositories":[{"type":"Public","name":"chemical_representation_learning_for_toxicity_prediction","owner":"PaccMann","isFork":false,"description":"Chemical representation learning paper in Digital Discovery","allTopics":["deep-learning","chemoinformatics","qsar","property-prediction","toxicity-prediction","pytorch"],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":0,"starsCount":54,"forksCount":15,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T20:38:48.312Z"}},{"type":"Public","name":"paccmann_generator","owner":"PaccMann","isFork":false,"description":"Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).","allTopics":["deep-reinforcement-learning","generative-model","vae","drug-discovery","molecule-generation"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":8,"forksCount":7,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T20:36:24.648Z"}},{"type":"Public","name":"transcriptomic_signature_sampling","owner":"PaccMann","isFork":false,"description":"","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":0,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-22T07:55:49.629Z"}},{"type":"Public","name":"paccmann_predictor","owner":"PaccMann","isFork":false,"description":"PyTorch implementation of bimodal neural networks for drug-cell (pharmarcogenomics) and drug-protein (proteochemometrics) interaction prediction","allTopics":["pharmacogenomics","drug-interaction","drug-sensitivity","proteochemometrics","deep-learning","pytorch"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":44,"forksCount":18,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-21T14:48:29.561Z"}},{"type":"Public","name":"paccmann_datasets","owner":"PaccMann","isFork":false,"description":"pytoda - PaccMann PyTorch Dataset Classes. Read the docs: <a href=\"https://paccmann.github.io/paccmann_datasets/\" rel=\"nofollow\">https://paccmann.github.io/paccmann_datasets/</a>","allTopics":["bioinformatics","pytorch","rdkit","chemoinformatics","smiles","python","deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":26,"forksCount":7,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-17T12:07:55.795Z"}},{"type":"Public","name":"paccmann.github.io","owner":"PaccMann","isFork":false,"description":"PaccMann website","allTopics":[],"primaryLanguage":{"name":"HTML","color":"#e34c26"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":0,"license":null,"participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-05-17T07:44:04.952Z"}},{"type":"Public","name":"fdsa","owner":"PaccMann","isFork":false,"description":"A fully differentiable set autoencoder","allTopics":["deep-learning","multimodal-data","set-autoencoder"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":1,"issueCount":0,"starsCount":17,"forksCount":3,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-04-03T14:55:46.573Z"}},{"type":"Public","name":"paccmann_gp","owner":"PaccMann","isFork":false,"description":"PyTorch/skopt based implementation of Bayesian optimization with Gaussian processes - build to optimize latent spaces of VAEs to generate molecules with desired properties","allTopics":["machine-learning","generative-model","chemoinformatics","gaussian-processes","bayesian-optimization"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":10,"forksCount":5,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2024-03-23T20:56:53.708Z"}},{"type":"Public","name":"TITAN","owner":"PaccMann","isFork":false,"description":"Code for \"T Cell Receptor Specificity Prediction with Bimodal Attention Networks\" (<a href=\"https://doi.org/10.1093/bioinformatics/btab294\" rel=\"nofollow\">https://doi.org/10.1093/bioinformatics/btab294</a>, ISMB 2021)","allTopics":["immunology","tcr-epitope","compound-protein-interaction","protein-ligand","deep-learning"],"primaryLanguage":{"name":"Jupyter Notebook","color":"#DA5B0B"},"pullRequestCount":0,"issueCount":1,"starsCount":27,"forksCount":6,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-08-04T08:51:33.942Z"}},{"type":"Public","name":"paccmann_chemistry","owner":"PaccMann","isFork":false,"description":"Generative models of chemical data for PaccMann^RL","allTopics":["generative-model","vae","drug-discovery","molecule-generation","deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":13,"forksCount":8,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2023-06-02T07:12:28.415Z"}},{"type":"Public","name":"paccmann_kinase_binding_residues","owner":"PaccMann","isFork":false,"description":"Comparison of active site and full kinase sequences for drug-target affinity prediction and molecular generation. Full paper: <a href=\"https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889\" rel=\"nofollow\">https://pubs.acs.org/doi/10.1021/acs.jcim.1c00889</a>","allTopics":["knn","protein-ligand-interactions","kinases","active-sites","proteochemometrics","machine-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":34,"forksCount":6,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-10-03T10:55:10.934Z"}},{"type":"Public archive","name":"guacamol_baselines","owner":"PaccMann","isFork":true,"description":"Baselines models for GuacaMol benchmarks","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":33,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-11T19:46:30.762Z"}},{"type":"Public archive","name":"moses","owner":"PaccMann","isFork":true,"description":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":243,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-10T20:08:11.853Z"}},{"type":"Public","name":"paccmann_rl","owner":"PaccMann","isFork":false,"description":"Code pipeline for the PaccMann^RL in iScience: <a href=\"https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6\" rel=\"nofollow\">https://www.cell.com/iscience/fulltext/S2589-0042(21)00237-6</a>","allTopics":["deep-learning","drug-discovery","transcriptomics","generative-models","drug-sensitivity","de-novo-drug-design"],"primaryLanguage":null,"pullRequestCount":0,"issueCount":1,"starsCount":30,"forksCount":9,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-10T15:51:38.395Z"}},{"type":"Public","name":"tape","owner":"PaccMann","isFork":true,"description":"Tasks Assessing Protein Embeddings (TAPE), a set of five biologically relevant semi-supervised learning tasks spread across different domains of protein biology.","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":129,"license":"BSD 3-Clause \"New\" or \"Revised\" License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-02-09T12:16:18.510Z"}},{"type":"Public archive","name":"guacamol","owner":"PaccMann","isFork":true,"description":"Benchmarks for generative chemistry","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":83,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2022-01-28T19:59:03.894Z"}},{"type":"Public","name":"paccmann_proteomics","owner":"PaccMann","isFork":false,"description":"PaccMann models for protein language modeling","allTopics":["pytorch","transformer","protein-language-model"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":1,"starsCount":37,"forksCount":8,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-11-10T20:44:31.183Z"}},{"type":"Public","name":"paccmann_sarscov2","owner":"PaccMann","isFork":false,"description":"Code for paper on automation of discovery and synthesis of targeted molecules: <a href=\"https://iopscience.iop.org/article/10.1088/2632-2153/abe808\" rel=\"nofollow\">https://iopscience.iop.org/article/10.1088/2632-2153/abe808</a>","allTopics":["deep-learning","drug-discovery","sars-cov-2","de-novo-drug-design","proteochemometrics","computer-aided-synthesis-planning","accelerated-discovery"],"primaryLanguage":null,"pullRequestCount":0,"issueCount":0,"starsCount":18,"forksCount":6,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-18T00:06:30.460Z"}},{"type":"Public","name":"paccmann_omics","owner":"PaccMann","isFork":false,"description":"Generative models for transcriptomics profiles and proteins","allTopics":["generative-model","vae","proteomics","transcriptomics","variational-autoencoder","deep-learning"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":7,"forksCount":3,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-17T23:24:24.587Z"}},{"type":"Public","name":"paccmann_polymer","owner":"PaccMann","isFork":false,"description":"Graph-regularized VAE and the impact of topology on learned representations","allTopics":["deep-learning","vae","manifold-learning","gnn","graph-regularized-vae","pytorch"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-17T22:22:34.719Z"}},{"type":"Public","name":"paccmann_predictor_tf","owner":"PaccMann","isFork":true,"description":"Tensorflow implementation of PaccMann (drug sensitivity prediction)","allTopics":["deep-learning","cancer","tensorflow","drug-response-prediction","drug-sensitivity"],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":2,"forksCount":22,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-17T20:54:26.327Z"}},{"type":"Public","name":"reinvent_models","owner":"PaccMann","isFork":false,"description":"PaccMann fork of Reinvent Models","allTopics":[],"primaryLanguage":{"name":"Python","color":"#3572A5"},"pullRequestCount":0,"issueCount":0,"starsCount":1,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2021-09-06T11:22:31.581Z"}},{"type":"Public","name":"docs","owner":"PaccMann","isFork":false,"description":"Documentation for PaccMann service","allTopics":[],"primaryLanguage":{"name":"Ruby","color":"#701516"},"pullRequestCount":0,"issueCount":0,"starsCount":0,"forksCount":1,"license":"MIT License","participation":null,"lastUpdated":{"hasBeenPushedTo":true,"timestamp":"2020-04-03T12:15:09.545Z"}}],"repositoryCount":23,"userInfo":null,"searchable":true,"definitions":[],"typeFilters":[{"id":"all","text":"All"},{"id":"public","text":"Public"},{"id":"source","text":"Sources"},{"id":"fork","text":"Forks"},{"id":"archived","text":"Archived"},{"id":"template","text":"Templates"}],"compactMode":false},"title":"PaccMann repositories"}