{"id":"W2065170882","doi":"10.1021/ci2004779","title":"Integrating Medicinal Chemistry, Organic/Combinatorial Chemistry, and Computational Chemistry for the Discovery of Selective Estrogen Receptor Modulators with F<scp>orecaster</scp>, a Novel Platform for Drug Discovery","year":2011,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Institutes of Health Research; AstraZeneca","keywords":"Virtual screening; Drug discovery; Chemistry; Computer science; Estrogen receptor; Selective estrogen receptor modulator; Computational biology; Biochemical engineering; Combinatorial chemistry; Breast cancer; Biochemistry; Cancer; Biology; Engineering","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":[0.001075573,0.0006828213,0.0005729219,0.0006281823,0.0002145066,0.0008886144,0.0004795819,0.0003375093,0.001932106],"category_scores_gemma":[0.0009040461,0.0002712267,0.0005259837,0.0005888388,0.0003686495,0.0007637143,0.0004623315,0.0007633709,0.0005175227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006836065,"about_ca_system_score_gemma":0.001708285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001981903,"about_ca_topic_score_gemma":0.002586076,"domain_scores_codex":[0.9997101,0.00008178746,0.0000150208,0.00004845105,0.0001164018,0.00002811278],"domain_scores_gemma":[0.9997491,0.0001452091,0.00002879661,0.00002578703,0.00002796163,0.00002302119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001047896,0.001005906,0.003727859,0.0007910172,0.0002552131,0.0003638755,0.0001077149,0.4184944,0.1612227,0.05130744,0.007656199,0.3540197],"study_design_scores_gemma":[0.0002465667,0.0004263668,0.0009533319,0.00002859742,0.00008640319,0.0001075537,0.00001866057,0.8982902,0.07423077,0.008783503,0.01678205,0.00004601118],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2089446,0.003920692,0.74726,0.002113016,0.0001761195,0.0004030397,0.00112257,0.01459735,0.02146262],"genre_scores_gemma":[0.4009416,0.002981206,0.5911798,0.000484857,0.0001022523,0.0002587117,0.001037617,0.000421099,0.002592999],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001981903,"threshold_uncertainty_score":0.006463587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02448450876702811,"score_gpt":0.2557500418729276,"score_spread":0.2312655331058995,"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."}}