{"id":"W4240713108","doi":"10.26434/chemrxiv.13383266.v2","title":"Beyond Generative Models: Superfast Traversal, Optimization, Novelty, Exploration and Discovery (STONED) Algorithm for Molecules using SELFIES","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":7,"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; Generative grammar; Generative model; Benchmark (surveying); Deep learning; Tree traversal; Artificial intelligence; Interpolation (computer graphics); Machine learning; Virtual screening; Algorithm; Theoretical computer science; Drug discovery; Chemistry","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":[0.001043484,0.0008571138,0.00106576,0.0007447766,0.0005229581,0.001201613,0.001603118,0.001703365,0.00480224],"category_scores_gemma":[0.003234024,0.0007038366,0.001399294,0.0009367147,0.001138493,0.001803628,0.001860004,0.002136096,0.001337199],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077486,"about_ca_system_score_gemma":0.001596879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003056224,"about_ca_topic_score_gemma":0.007017731,"domain_scores_codex":[0.9996099,0.000121623,0.00001883718,0.00007588402,0.0001310863,0.00004271612],"domain_scores_gemma":[0.9991277,0.0005617019,0.00005216547,0.0001495691,0.00006357737,0.00004526884],"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":[0.0001304141,0.00007922291,0.001229999,0.0002145517,0.00009544922,0.0001405784,0.00009392248,0.7396286,0.004405134,0.1237929,0.007043346,0.1231459],"study_design_scores_gemma":[0.00001844504,0.00001826494,0.00002712893,0.000008653389,0.000006138367,0.00002040804,0.000005849706,0.9637251,0.0009055352,0.03316789,0.002090693,0.000005929276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01184522,0.0004014544,0.9814324,0.0004224168,0.00005956077,0.00005421684,0.0001867496,0.002189271,0.003408697],"genre_scores_gemma":[0.2496001,0.0005112646,0.7398121,0.0006297869,0.00005568848,0.0003854058,0.0009864611,0.001102187,0.006916931],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00480224,"threshold_uncertainty_score":0.01606506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04168675759208199,"score_gpt":0.2802105068024201,"score_spread":0.2385237492103381,"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."}}