{"id":"W4390064787","doi":"10.1016/j.jksus.2023.103076","title":"Comprehensive in silico discovery of c-Src tyrosine kinase inhibitors in cancer treatment: A unified approach combining pharmacophore modeling, 3D QSAR, DFT, and molecular dynamics simulation","year":2023,"lang":"en","type":"article","venue":"Journal of King Saud University - Science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"SickKids Foundation; Hospital for Sick Children","funders":"King Saud University","keywords":"Pharmacophore; Lipinski's rule of five; Virtual screening; Quantitative structure–activity relationship; Computational biology; Chemistry; Proto-oncogene tyrosine-protein kinase Src; Tyrosine kinase; In silico; Molecular dynamics; Receptor tyrosine kinase; Docking (animal); PubChem; Drug discovery; Stereochemistry; Kinase; Biochemistry; Biology; Computational chemistry; Signal transduction; Medicine","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":[0.0008179277,0.0001397564,0.0003046693,0.001282883,0.000121595,0.00009982342,0.0005901044,0.00003478415,2.862793e-7],"category_scores_gemma":[0.00007869356,0.0001428402,0.00006502022,0.003138022,0.0002088545,0.001750766,0.0003357989,0.0001969831,1.404463e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008771301,"about_ca_system_score_gemma":0.0007436008,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000468919,"about_ca_topic_score_gemma":0.00004214973,"domain_scores_codex":[0.9983345,0.000189116,0.0003506155,0.0003215161,0.0005505604,0.0002537445],"domain_scores_gemma":[0.9988762,0.0002951689,0.0003269519,0.000161047,0.0002534156,0.00008719607],"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":[0.00005280794,0.0001054846,0.004558617,0.00002467421,0.00001049863,0.000108483,0.002092806,0.9781582,0.007513447,0.001848002,7.646707e-7,0.005526159],"study_design_scores_gemma":[0.001269373,0.00008566791,0.00848533,0.0001665668,0.00001310839,0.000008320552,0.0008200874,0.9876532,0.0008121841,0.0005517723,0.000008779025,0.0001256063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8319567,0.000130421,0.1674266,0.0001436864,0.0001536487,0.00010839,0.000005778357,0.00001181481,0.00006299673],"genre_scores_gemma":[0.9878998,0.0001060596,0.0119347,0.00002866021,0.00001386145,2.43309e-7,0.000001850455,0.000005295386,0.000009549021],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1559431,"threshold_uncertainty_score":0.5824856,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03792830884240997,"score_gpt":0.3168431545727639,"score_spread":0.2789148457303539,"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."}}