{"id":"W4380487298","doi":"10.26434/chemrxiv-2023-tjzr4","title":"Machine Learning-Augmented Docking. 1. CYP inhibition prediction","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Docking (animal); Training set; Computational biology; Test set; Protein–ligand docking; Artificial intelligence; Computer science; Machine learning; Transferability; Chemistry; Drug discovery; Virtual screening; Biochemistry; Biology","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":[0.001457538,0.001688886,0.002097996,0.000811772,0.000535538,0.0009749579,0.002322263,0.001893621,0.01316544],"category_scores_gemma":[0.004853556,0.0008195275,0.001136041,0.001583375,0.0005938307,0.001269061,0.001400031,0.001900408,0.00459946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007473938,"about_ca_system_score_gemma":0.0008370477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004960762,"about_ca_topic_score_gemma":0.004510683,"domain_scores_codex":[0.998913,0.0004953126,0.00005009674,0.0001555833,0.0003274897,0.0000584203],"domain_scores_gemma":[0.9981458,0.001070116,0.0001580354,0.0003492381,0.000215085,0.00006164651],"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":[0.0002842361,0.0001611376,0.001517923,0.0004322014,0.0001642108,0.0001120335,0.00004468189,0.9177089,0.003612072,0.01084359,0.01152368,0.05359532],"study_design_scores_gemma":[0.00003982538,0.0000267064,0.0003468784,0.0000131247,0.000009569764,0.00003857292,0.000004061343,0.9896021,0.001931989,0.005259806,0.002708336,0.00001910746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04433723,0.001070206,0.9073825,0.0008571687,0.0001837696,0.0002307828,0.004624811,0.0290832,0.01223037],"genre_scores_gemma":[0.3499924,0.00117468,0.6259883,0.0005572863,0.000124196,0.0007334827,0.007955766,0.003862689,0.009611259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01316544,"threshold_uncertainty_score":0.04404283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04460522536639598,"score_gpt":0.3020840659179958,"score_spread":0.2574788405515998,"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."}}