{"id":"W4251781122","doi":"10.26434/chemrxiv.14572050.v1","title":"De Novo Design with Deep Generative Models Based on 3D Similarity Scoring","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"Pharmacophore; Similarity (geometry); Chemical space; Context (archaeology); Artificial intelligence; Generative grammar; Computer science; Orthogonality; Generative model; Machine learning; Quantitative structure–activity relationship; Mathematics; Bioinformatics; Drug discovery; Biology","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000965175,0.0004285069,0.0004286364,0.0001639836,0.0001458823,0.0007092853,0.001226035,0.0002135184,0.00001672734],"category_scores_gemma":[0.0001389822,0.000414762,0.0001465236,0.0003924233,0.00006799118,0.0003504071,0.000960229,0.0007997477,0.000004360102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004024688,"about_ca_system_score_gemma":0.001721289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001860349,"about_ca_topic_score_gemma":0.000006768244,"domain_scores_codex":[0.9968967,0.0005622,0.0002860884,0.001221263,0.0006175048,0.0004162078],"domain_scores_gemma":[0.9973268,0.0007829756,0.0001891988,0.001222365,0.0003042762,0.0001744163],"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":[0.00002594886,0.0001169221,0.00004552834,0.00006702218,0.00004820539,0.000132783,0.0006059833,0.9923398,0.0002183966,0.002688479,0.00001824854,0.003692634],"study_design_scores_gemma":[0.0002931382,0.0000479489,0.0002630113,0.000239454,0.00002179984,0.0000121975,0.00001154991,0.9549585,0.02649698,0.01719407,0.000008401758,0.0004529433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0245987,0.0001247358,0.9724227,0.0004549274,0.0003210162,0.0003550851,0.000001675048,0.000177648,0.001543486],"genre_scores_gemma":[0.3284874,0.000004986784,0.670462,0.0007815616,0.0001087734,0.00009083303,0.00001592317,0.00002650553,0.00002203041],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3038887,"threshold_uncertainty_score":0.9998304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08735537391331043,"score_gpt":0.3060896658001475,"score_spread":0.2187342918868371,"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."}}