{"id":"W2204875011","doi":"10.1021/ci034157x","title":"Use of Electron Density Critical Points as Chemical Function-Based Reduced Representations of Pharmacological Ligands","year":2004,"lang":"en","type":"article","venue":"Journal of Chemical Information and Computer Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Fonds De La Recherche Scientifique - FNRS","keywords":"Pharmacophore; Representation (politics); Similarity (geometry); Resolution (logic); Molecule; Set (abstract data type); Function (biology); Electron density; Critical point (mathematics); Topology (electrical circuits); Order (exchange); Computer science; Electron; Computational chemistry; Biological system; Mathematics; Chemical physics; Chemistry; Physics; Artificial intelligence; Image (mathematics); Combinatorics; Stereochemistry; Quantum mechanics; Biology; Geometry; Organic chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.0006783559,0.0001074236,0.0002540861,0.0002288671,0.00006437441,0.0001632634,0.0004582372,0.00006326795,0.00000645389],"category_scores_gemma":[0.0004639109,0.00008584841,0.0001213165,0.0005815907,0.000406109,0.002484045,0.0001517343,0.0001906062,0.000002076938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004255307,"about_ca_system_score_gemma":0.0004420976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000561153,"about_ca_topic_score_gemma":4.478824e-8,"domain_scores_codex":[0.998164,0.00005992927,0.0008034786,0.0001394927,0.0006704564,0.0001626469],"domain_scores_gemma":[0.9978792,0.000789845,0.0004719329,0.0001070998,0.0006080571,0.0001438685],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006341658,0.001134411,0.0007885011,0.0001460704,0.0001821545,0.00001417184,0.0019517,0.1886178,0.4830669,0.2887167,0.001243191,0.03350428],"study_design_scores_gemma":[0.0006448725,0.0004259867,0.00140075,0.00005476999,0.00002650552,0.0001242547,0.00001174753,0.1916775,0.7901207,0.01536189,0.00004288942,0.0001081972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6069574,0.00001368406,0.3913494,0.001433624,0.0001565601,0.00004096422,9.273255e-7,0.000009859781,0.00003763995],"genre_scores_gemma":[0.8404832,0.000005180078,0.1587997,0.0006603644,0.00004749394,9.44522e-7,0.000001351907,0.000001270811,4.150671e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3070538,"threshold_uncertainty_score":0.3500796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739974045223304,"score_gpt":0.3456817049560347,"score_spread":0.3082819645038016,"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."}}