{"id":"W4415208579","doi":"10.1038/s43588-025-00886-7","title":"ECloudGen: leveraging electron clouds as a latent variable to scale up structure-based molecular design","year":2025,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"National Key Research and Development Program of China; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Latent variable; Benchmark (surveying); Chemical space; Latent variable model; Generative grammar; Variable (mathematics); Scale (ratio); Generative model","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":[0.001164788,0.0004805253,0.000676076,0.0004078915,0.0003378786,0.0009833346,0.001214663,0.0008221427,0.003150897],"category_scores_gemma":[0.003385829,0.000305853,0.0007813236,0.0004767628,0.0005652442,0.001551204,0.00137808,0.001377913,0.0006557751],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005413234,"about_ca_system_score_gemma":0.0008148433,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001782031,"about_ca_topic_score_gemma":0.003891008,"domain_scores_codex":[0.9995096,0.0002002571,0.00001474552,0.00008566347,0.000147104,0.00004256865],"domain_scores_gemma":[0.9988049,0.0007139994,0.00007330906,0.0002725885,0.00008285232,0.00005224374],"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":[0.0004775277,0.0004402595,0.003719305,0.0003222074,0.0002550785,0.0001488878,0.000115067,0.7490629,0.01102007,0.08632953,0.007507212,0.1406019],"study_design_scores_gemma":[0.00004155806,0.00004638139,0.0000951512,0.000006510713,0.00001365819,0.000008474564,0.000005581637,0.9799411,0.002358198,0.0159525,0.001523658,0.000007330593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06192056,0.0005372121,0.9265871,0.0005361367,0.0001351753,0.0001133508,0.0007396298,0.00570294,0.003727847],"genre_scores_gemma":[0.6600547,0.0004097417,0.334093,0.0003455036,0.00005373391,0.0001971533,0.001072105,0.0006477996,0.003126228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003150897,"threshold_uncertainty_score":0.01054084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007204081071840983,"score_gpt":0.2855321472125076,"score_spread":0.2783280661406666,"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."}}