{"id":"W2912357598","doi":"10.1021/acsomega.8b03328","title":"Predicting Blood–Brain Partitioning of Small Molecules Using a Novel Minimalistic Descriptor-Based Approach via the 3D-RISM-KH Molecular Solvation Theory","year":2019,"lang":"en","type":"article","venue":"ACS Omega","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Solvation; Blood–brain barrier; Implicit solvation; Chemistry; Compartmentalization (fire protection); Molecule; Computational chemistry; Chemical physics; Neuroscience; Psychology; Organic chemistry","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":[0.0003127455,0.0004808228,0.0007135968,0.0006858233,0.0003197534,0.0005654581,0.0007261989,0.0007599164,0.0007409073],"category_scores_gemma":[0.0009369053,0.0003513289,0.0008472218,0.0003486406,0.0004173993,0.0005409092,0.0005524581,0.0005607874,0.0001525912],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000638104,"about_ca_system_score_gemma":0.001347916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005157607,"about_ca_topic_score_gemma":0.004159328,"domain_scores_codex":[0.9999107,0.00003006144,0.000005691526,0.00001379248,0.00002803566,0.00001168057],"domain_scores_gemma":[0.9996823,0.0002062423,0.00003499863,0.0000163306,0.00003605252,0.00002396255],"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":[0.00002668799,0.00002083997,0.0002781483,0.00002945767,0.00001779255,0.00002789818,0.000008110028,0.9914339,0.00225142,0.001756075,0.0001420895,0.004007556],"study_design_scores_gemma":[0.000002308897,0.000005774888,0.00002948142,4.657061e-7,0.000001637044,0.000002583535,9.801381e-7,0.9992945,0.0002201052,0.0004044775,0.0000361909,0.000001459778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1914523,0.000518765,0.8040896,0.0003648584,0.00003017182,0.0001129314,0.0003574404,0.0005856286,0.002488248],"genre_scores_gemma":[0.9091377,0.0002944151,0.08829749,0.0001125972,0.00003314138,0.0002435566,0.0004012577,0.0001129861,0.001366805],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005157607,"threshold_uncertainty_score":0.01025522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03287914523985677,"score_gpt":0.2626851092766793,"score_spread":0.2298059640368225,"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."}}