{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009116795,0.0007656486,0.0009421249,0.0007071836,0.0004803206,0.001048366,0.001509723,0.001483091,0.004783823],"category_scores_gemma":[0.003089312,0.0006206553,0.001296778,0.0008668834,0.0009865072,0.001625977,0.001692257,0.00196021,0.001176756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001028856,"about_ca_system_score_gemma":0.00163117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003492726,"about_ca_topic_score_gemma":0.008137553,"domain_scores_codex":[0.9996719,0.00009818988,0.00001692283,0.00006298552,0.0001126111,0.00003736402],"domain_scores_gemma":[0.9991795,0.00054183,0.00004678934,0.0001246538,0.00006463935,0.00004248821],"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":[0.0001182507,0.00007690662,0.001297625,0.0001853374,0.00008357311,0.0001388128,0.0000955596,0.7492704,0.003694145,0.1046332,0.005861445,0.1345447],"study_design_scores_gemma":[0.00001722696,0.00001976885,0.0000271859,0.000008618253,0.000006744105,0.00002212917,0.000006178442,0.9705445,0.0007953944,0.0265969,0.001949923,0.00000548814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01213248,0.0003807557,0.9814876,0.0003887009,0.00005386649,0.00006019404,0.0001702547,0.001838559,0.003487556],"genre_scores_gemma":[0.26203,0.0005032186,0.7282571,0.0006203316,0.00005057523,0.0003740634,0.0008631799,0.0008366226,0.006464965],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004783823,"threshold_uncertainty_score":0.01600343,"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."}}