{"id":"W3151536792","doi":"10.1088/2632-2153/abf5b7","title":"MPGVAE: improved generation of small organic molecules using message passing neural nets","year":2021,"lang":"en","type":"article","venue":"Machine Learning Science and Technology","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; Vector Institute; University of Toronto","funders":"","keywords":"Message passing; Computer science; Artificial neural network; Autoencoder; Matching (statistics); Theoretical computer science; Perceptron; Graph; Encoder; Artificial intelligence; Algorithm; Parallel computing; Mathematics","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":[],"consensus_categories":[],"category_scores_codex":[0.0003097115,0.000133577,0.0001854845,0.0003487627,0.0004649791,0.0001267088,0.0005819639,0.00009287021,0.000003158204],"category_scores_gemma":[0.0004233579,0.0001257682,0.00002230196,0.003008627,0.0005086157,0.0004050546,0.0006755311,0.0003913077,4.893504e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002952873,"about_ca_system_score_gemma":0.0001377752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000156337,"about_ca_topic_score_gemma":0.00003510675,"domain_scores_codex":[0.9985954,0.00005772772,0.0002118264,0.0005711137,0.0002029213,0.0003609719],"domain_scores_gemma":[0.9990928,0.00003667452,0.0001659636,0.0003764815,0.0002742156,0.00005385089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[7.337231e-7,0.00001105577,0.00342486,0.00000452648,0.000002723398,0.0000240914,0.00005146069,0.002921228,0.9103854,0.004913082,0.000001049907,0.07825983],"study_design_scores_gemma":[0.0001339819,0.00007118912,0.0003979192,0.00001100936,0.000004295707,0.0001574923,0.00002192187,0.8488579,0.148941,0.001197879,0.0000891361,0.0001163682],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6699941,0.0007364192,0.327443,0.001447333,0.0001308145,0.00005368919,2.467253e-7,0.000164169,0.00003026277],"genre_scores_gemma":[0.9190995,0.00003096558,0.0806755,0.0001416888,0.00001601387,0.000001931282,0.000001249659,0.000008359511,0.00002476174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8459367,"threshold_uncertainty_score":0.512868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01654811459114791,"score_gpt":0.2454807760947847,"score_spread":0.2289326615036368,"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."}}