{"id":"W4237752146","doi":"10.26434/chemrxiv.8299544.v4","title":"A De Novo Molecular Generation Method Using Latent Vector Based Generative Adversarial Network","year":2019,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"Autoencoder; Generative grammar; Artificial intelligence; Chemical space; Computer science; Adversarial system; Set (abstract data type); Generative adversarial network; Deep learning; Artificial neural network; Machine learning; Fraction (chemistry); Generative model; Pattern recognition (psychology); Drug discovery; Chemistry; Bioinformatics; Biology","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":[0.0009093271,0.000676624,0.000641931,0.0004116934,0.0002473661,0.0004523357,0.0009888889,0.001015917,0.002511701],"category_scores_gemma":[0.001345918,0.0004647147,0.0007516186,0.0003187833,0.0008045526,0.0008610193,0.001141804,0.001578923,0.0005493779],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006102034,"about_ca_system_score_gemma":0.0006483244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008480101,"about_ca_topic_score_gemma":0.001231095,"domain_scores_codex":[0.9996878,0.00009600542,0.00001056426,0.00006725694,0.0001098736,0.00002850887],"domain_scores_gemma":[0.9994242,0.0003016241,0.00006517806,0.0001148274,0.00006003591,0.00003416899],"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":[0.00008085489,0.00007989942,0.0004923115,0.00008430132,0.00006823466,0.0001324531,0.00003914424,0.874461,0.01427506,0.03971852,0.001866422,0.06870191],"study_design_scores_gemma":[0.000008069314,0.00002534687,0.00003074295,0.000003900373,0.000006300946,0.00003326792,0.000001553368,0.9924705,0.002723464,0.003676847,0.001014601,0.000005499138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00717359,0.0001523927,0.9900057,0.0001371502,0.00004019394,0.00004852403,0.00004600718,0.0004674139,0.00192908],"genre_scores_gemma":[0.4281187,0.0003571564,0.5616217,0.0003746357,0.00005749263,0.0002745034,0.0003003192,0.0002840496,0.008611477],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002511701,"threshold_uncertainty_score":0.008402526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06229740319530794,"score_gpt":0.3399937720826846,"score_spread":0.2776963688873766,"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."}}