{"id":"W4362697742","doi":"10.26434/chemrxiv-2023-1d5w8","title":"A community effort to discover small molecule SARS-CoV-2 inhibitors","year":2023,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"AXA Research Fund","keywords":"Virtual screening; In silico; Drug discovery; Small molecule; Computational biology; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Computer science; Biology; Bioinformatics; Medicine; Biochemistry; Infectious disease (medical specialty); Gene","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009901284,0.001575034,0.001130293,0.001197323,0.00148215,0.002201921,0.001977063,0.00193643,0.006101519],"category_scores_gemma":[0.007100791,0.0004104426,0.002277328,0.001089174,0.000897643,0.002169721,0.003793499,0.002780962,0.001681191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001206074,"about_ca_system_score_gemma":0.006687084,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001506835,"about_ca_topic_score_gemma":0.002120211,"domain_scores_codex":[0.9966289,0.00121431,0.00009906767,0.0004315956,0.001192191,0.0004338494],"domain_scores_gemma":[0.9950014,0.001406124,0.0002936903,0.0005256002,0.001558694,0.001214519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003820439,0.008588072,0.01250755,0.004120201,0.00130552,0.0009545815,0.00141511,0.102778,0.143351,0.03497563,0.08083942,0.6053446],"study_design_scores_gemma":[0.009278725,0.01714949,0.009098651,0.001033583,0.001455593,0.001070561,0.002400174,0.2626578,0.1923843,0.04944152,0.4535953,0.0004343347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6373806,0.01716338,0.2522792,0.0259546,0.00252512,0.00490406,0.003836994,0.004356538,0.05159954],"genre_scores_gemma":[0.5959013,0.009404267,0.3607115,0.005966957,0.0006111999,0.002678552,0.009595143,0.0007920036,0.01433906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009901284,"threshold_uncertainty_score":0.05236363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1042686417651441,"score_gpt":0.3499574327072325,"score_spread":0.2456887909420884,"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."}}