{"id":"W3166837026","doi":"10.20944/preprints202105.0567.v1","title":"Artificial Intelligence and Machine Learning in Medicinal Chemistry and Validation of Emerging Drug Targets","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Drug discovery; Artificial intelligence; Repurposing; Machine learning; Computer science; Drug; Drug repositioning; Artificial neural network; Field (mathematics); Quantitative structure–activity relationship; Pharmaceutical industry; Drug development; Deep learning; Data science; Biochemical engineering; Engineering; Chemistry; Pharmacology; Medicine; Mathematics","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":[0.001931256,0.0002252543,0.0003808156,0.0001202635,0.00006008422,0.00005845989,0.0005044202,0.0001140017,0.00007442317],"category_scores_gemma":[0.0008225377,0.0002604212,0.00005710113,0.0002585999,0.0001028966,0.0002262675,0.003555583,0.0008158663,0.000003312709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006308166,"about_ca_system_score_gemma":0.000203134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002526263,"about_ca_topic_score_gemma":0.00001477103,"domain_scores_codex":[0.9975098,0.0003569969,0.0006219606,0.0009173085,0.0003967123,0.0001972299],"domain_scores_gemma":[0.9986241,0.0003646728,0.0003350879,0.0004619947,0.0001294184,0.00008469648],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000424808,0.0002085049,0.4347163,0.001230617,0.00009224137,0.0000669153,0.01790348,0.4058239,0.03012215,0.002475287,9.968321e-7,0.1073171],"study_design_scores_gemma":[0.00007518421,0.000006753986,0.08939919,0.0004237994,0.00001748954,0.0000229905,0.0003344524,0.5105497,0.3753394,0.0234828,0.00002605124,0.0003221714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7821392,0.0006629221,0.2160938,0.00049528,0.0001812479,0.0001363916,0.000002014675,0.00004218852,0.0002469751],"genre_scores_gemma":[0.9823697,0.0002049526,0.01726246,0.00001738212,0.00005210654,0.00001802498,0.00002826543,0.00001164791,0.00003553413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3453171,"threshold_uncertainty_score":0.9999848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1120433324312712,"score_gpt":0.3665246277646252,"score_spread":0.2544812953333541,"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."}}