{"id":"W3158331815","doi":"10.1039/d1sc00231g","title":"Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES","year":2021,"lang":"en","type":"article","venue":"Chemical Science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":160,"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":"Tree traversal; Novelty; Chemical space; Computer science; Generative grammar; Inverse; Interpolation (computer graphics); Algorithm; Artificial intelligence; Theoretical computer science; Drug discovery; Chemistry; Mathematics; Psychology; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004095248,0.0001576174,0.0001675387,0.00008619653,0.0003314364,0.0007954187,0.0004823904,0.00004828268,0.000002852014],"category_scores_gemma":[0.000253736,0.0001547821,0.00004849825,0.0009399132,0.0004020648,0.004241596,0.0004669416,0.00007681394,7.375976e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001254246,"about_ca_system_score_gemma":0.0005491431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006693852,"about_ca_topic_score_gemma":9.417683e-7,"domain_scores_codex":[0.9981909,0.00005023299,0.0002299439,0.0007485088,0.0004803127,0.0003000359],"domain_scores_gemma":[0.9988334,0.0002944264,0.00006851779,0.0002578028,0.0004156722,0.0001301654],"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":[0.000002652513,0.00005121817,0.0000021554,0.000008225819,0.000006483861,0.000002718691,0.0008619917,0.8102419,0.1198194,0.05078318,0.0000252737,0.01819477],"study_design_scores_gemma":[0.0001698904,0.00001192878,0.000003352095,0.000009925641,0.000005656262,0.00001494212,0.0001152229,0.7274465,0.237243,0.03481797,0.00001106059,0.0001505952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02392007,0.0001886423,0.9746837,0.000646999,0.0001999137,0.0001562949,0.00002228769,0.00004500911,0.0001370701],"genre_scores_gemma":[0.05888788,0.00003310675,0.9406771,0.0002525224,0.00007509896,0.00001912296,0.00002578934,0.000008817578,0.00002056523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1174235,"threshold_uncertainty_score":0.7670242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04921104154932282,"score_gpt":0.3046049321350987,"score_spread":0.2553938905857759,"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."}}