{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007657258,0.0002264612,0.0003520194,0.00003740149,0.0001202619,0.0002221145,0.0004047757,0.00009790565,0.00000144492],"category_scores_gemma":[0.0006014084,0.0001584103,0.0001241122,0.0001773207,0.0001195364,0.002757333,0.0001477304,0.0002827302,5.726743e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009547811,"about_ca_system_score_gemma":0.0004208137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008138784,"about_ca_topic_score_gemma":1.203968e-7,"domain_scores_codex":[0.9982701,0.00001050556,0.0008585131,0.0001858698,0.0004541601,0.0002208751],"domain_scores_gemma":[0.9972548,0.0008862775,0.0008517138,0.000133489,0.0007554924,0.0001182287],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002941655,0.0008544112,0.0004699495,0.003834547,0.001820288,0.000002554832,0.04179512,0.314514,0.5832819,0.02229647,0.0007813415,0.02740781],"study_design_scores_gemma":[0.00180453,0.0000630231,0.000008722457,0.000145613,0.00005428982,0.0001210802,0.001098373,0.7859384,0.2033049,0.007303105,0.00005539932,0.0001025346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4498427,0.00005486796,0.5497405,0.00006287263,0.00006888155,0.0001452414,0.00002040491,0.000007499102,0.00005706399],"genre_scores_gemma":[0.8754537,0.00001167235,0.1242472,0.00004762907,0.0001712107,0.00001793422,0.00002859212,0.00001214356,0.00000995206],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4714244,"threshold_uncertainty_score":0.6459783,"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."}}