{"id":"W4400951930","doi":"10.1016/j.mlwa.2024.100576","title":"Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects","year":2024,"lang":"en","type":"article","venue":"Machine Learning with Applications","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":98,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Drug discovery; Machine learning; Computer science; Artificial intelligence; Algorithm; Drug development; Development (topology); Drug; Mathematics; Bioinformatics; Medicine; Biology; Pharmacology","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007505615,0.0002829996,0.0002540941,0.0003597131,0.000373183,0.0005128635,0.0003782745,0.00005467819,0.00000388992],"category_scores_gemma":[0.00003224728,0.0002545614,0.00002402783,0.0007327954,0.00009104198,0.0007273005,0.0003858201,0.0007074667,0.00002227733],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000863192,"about_ca_system_score_gemma":0.0001937866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001019608,"about_ca_topic_score_gemma":0.0001806451,"domain_scores_codex":[0.9978734,0.0002007762,0.0003357492,0.0009569364,0.0003154918,0.0003176271],"domain_scores_gemma":[0.9988548,0.0005636826,0.00008318717,0.0003190143,0.00005025112,0.0001290749],"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":[0.00001012074,0.00009962473,0.007747022,0.0003088969,0.00004519597,0.00001294142,0.00335194,0.00916498,0.00005517414,0.1474706,0.000005556744,0.8317279],"study_design_scores_gemma":[0.0006292505,0.00006209864,0.02917104,0.0001552543,0.00001780344,0.00012104,0.0001568062,0.7258657,0.00008161409,0.005895191,0.2372658,0.000578397],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006732074,0.04708497,0.9410259,0.002477728,0.00002745888,0.001109469,0.00000775729,0.0006382162,0.0008964385],"genre_scores_gemma":[0.6674436,0.002810697,0.3257789,0.0001036841,0.0001082061,0.002361732,0.000181697,0.00007621328,0.001135165],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8311495,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01609116918542917,"score_gpt":0.2658343252966821,"score_spread":0.2497431561112529,"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."}}