{"id":"W4233151585","doi":"10.26434/chemrxiv.8299544","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":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"AstraZeneca (Canada)","funders":"","keywords":"Autoencoder; Chemical space; Generative grammar; Artificial intelligence; Generative adversarial network; Deep learning; Set (abstract data type); Artificial neural network; Adversarial system; Computer science; Machine learning; Fraction (chemistry); Generative model; Drug discovery; Pattern recognition (psychology); Chemistry; Biochemistry; Chromatography","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":[0.0008537532,0.0007387333,0.0007902262,0.0004920759,0.0002750629,0.000517925,0.001109055,0.001190949,0.004119021],"category_scores_gemma":[0.001409961,0.0005434422,0.0008427299,0.0003736759,0.0007878859,0.0008450542,0.001215972,0.001640773,0.0008883258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007162876,"about_ca_system_score_gemma":0.0007484811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001301272,"about_ca_topic_score_gemma":0.001935701,"domain_scores_codex":[0.9997024,0.00008791895,0.00001064857,0.00006765517,0.0001010863,0.00003038577],"domain_scores_gemma":[0.9993246,0.0003773216,0.00006770697,0.0001148395,0.00007690842,0.00003868758],"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":[0.00008347069,0.00006054672,0.0004011399,0.00007096818,0.00006173096,0.0001239806,0.00003286069,0.8863541,0.007448731,0.03248262,0.002839586,0.07004027],"study_design_scores_gemma":[0.000007265645,0.00001360645,0.00001860512,0.000003026401,0.000004381513,0.00001910087,0.000001266814,0.9952555,0.001087998,0.002876371,0.0007094549,0.00000342331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005871677,0.0001657904,0.9906099,0.000168084,0.00004595815,0.00005217508,0.00006135603,0.0006467976,0.002378125],"genre_scores_gemma":[0.3785473,0.0003165404,0.6062415,0.0005059818,0.00007556158,0.0003272129,0.0004435879,0.0004360794,0.01310627],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004119021,"threshold_uncertainty_score":0.01377946,"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."}}