{"id":"W4243270491","doi":"10.26434/chemrxiv.13383266","title":"Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; Austrian Science Fund; Compute Canada; École de technologie supérieure; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Chemical space; Computer science; Virtual screening; Generative grammar; Deep learning; Machine learning; Benchmark (surveying); Generative model; Artificial intelligence; Tree traversal; Interpolation (computer graphics); Algorithm; Drug discovery; Theoretical computer science; Bioinformatics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043286,0.0008557722,0.001062979,0.0007440737,0.0005219121,0.001197488,0.001603179,0.001700213,0.004781968],"category_scores_gemma":[0.003221901,0.0007020611,0.001394611,0.0009364515,0.001138452,0.001803165,0.001857577,0.002137304,0.001329941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001078173,"about_ca_system_score_gemma":0.001597849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003059686,"about_ca_topic_score_gemma":0.007032656,"domain_scores_codex":[0.99961,0.0001217201,0.00001879777,0.00007575141,0.0001309083,0.00004275015],"domain_scores_gemma":[0.9991307,0.0005597061,0.0000520086,0.0001492682,0.00006309411,0.00004525524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001304043,0.00007908546,0.001224473,0.0002124836,0.00009505445,0.0001397631,0.00009374146,0.7403718,0.004400936,0.1232995,0.006993973,0.1229588],"study_design_scores_gemma":[0.00001835317,0.00001822666,0.00002696831,0.0000085433,0.000006075262,0.00002026699,0.000005809691,0.9640629,0.0009001972,0.03285826,0.002068415,0.000005896263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01184319,0.0003978144,0.9814696,0.0004203205,0.000059083,0.0000540788,0.0001849166,0.002185612,0.003385495],"genre_scores_gemma":[0.2499648,0.0005059396,0.7395115,0.0006283057,0.00005542722,0.0003839904,0.000978257,0.001091116,0.006880634],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004781968,"threshold_uncertainty_score":0.01599723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0541934749013716,"score_gpt":0.2852647401232292,"score_spread":0.2310712652218576,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}