{"id":"W3090915092","doi":"10.1021/acs.jcim.0c00833","title":"<tt>OptiMol</tt> : Optimization of Binding Affinities in Chemical Space for Drug Discovery","year":2020,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Agence Nationale de la Recherche; Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Chemical space; Computer science; Drug discovery; Docking (animal); Leverage (statistics); Oracle; Graph; Autoencoder; Artificial intelligence; Theoretical computer science; Bioinformatics; Deep learning; 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":[0.0008715205,0.0009138167,0.0008619386,0.0005332568,0.0003117387,0.001078246,0.001345327,0.001517342,0.004776011],"category_scores_gemma":[0.002224703,0.0006597938,0.0007701592,0.0006362946,0.001171293,0.001253905,0.001296481,0.001853046,0.00182108],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001152129,"about_ca_system_score_gemma":0.001440742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004443483,"about_ca_topic_score_gemma":0.007282042,"domain_scores_codex":[0.9996709,0.00009585504,0.00001418613,0.00007644367,0.0001095054,0.00003311784],"domain_scores_gemma":[0.999507,0.0002676804,0.00005164625,0.00006814718,0.00007152471,0.00003411845],"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":[0.00009274793,0.00006590319,0.0005433804,0.0002060617,0.00008717542,0.00007177786,0.00005066624,0.8004102,0.01056293,0.0390966,0.01284764,0.135965],"study_design_scores_gemma":[0.000005901399,0.00001652376,0.00003848676,0.000007141292,0.000004885941,0.00001354348,0.00000277037,0.9898649,0.001926483,0.006419255,0.001695427,0.000004641799],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009337597,0.0005490371,0.981486,0.0007467505,0.00008848857,0.0000639689,0.0003307125,0.002471215,0.004926157],"genre_scores_gemma":[0.3547815,0.001355888,0.6275654,0.001381743,0.0001701052,0.0002801927,0.001430847,0.001539136,0.01149511],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004776011,"threshold_uncertainty_score":0.01597732,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04022574581930739,"score_gpt":0.2862528614148656,"score_spread":0.2460271155955582,"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."}}