{"id":"W4387974303","doi":"10.21203/rs.3.rs-3388359/v1","title":"Interaction-aware 3D Molecular Generative Framework for Generalizable Structure-based Drug Design","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea; National Research Foundation","keywords":"Generative grammar; Drug; Computer science; Generative Design; Human–computer interaction; Artificial intelligence; Business; Pharmacology; Medicine; Marketing","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":[0.0006524035,0.0006023642,0.0007442219,0.0008018818,0.0003277917,0.000715777,0.00151726,0.001064841,0.002815166],"category_scores_gemma":[0.0009609995,0.0005217084,0.001601744,0.0005758557,0.001005477,0.0005404431,0.00125114,0.001146102,0.0005265315],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001194995,"about_ca_system_score_gemma":0.0009587553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006255145,"about_ca_topic_score_gemma":0.006718962,"domain_scores_codex":[0.9997578,0.00007051147,0.000008747606,0.00004706344,0.0000847765,0.00003110448],"domain_scores_gemma":[0.9996623,0.0001686993,0.00004144304,0.00004935532,0.00004153895,0.00003658469],"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":[0.00001403941,0.00001469626,0.0002633267,0.00002217443,0.00001847508,0.00003401559,0.00001505247,0.9733192,0.001518822,0.01566667,0.0005918025,0.008521707],"study_design_scores_gemma":[0.000002120092,0.000004036986,0.00001583243,0.000001296832,0.000001876496,0.000005562998,8.005154e-7,0.996711,0.0001555734,0.002856282,0.0002439008,0.00000170978],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01515441,0.0003371685,0.9801364,0.000279348,0.00003027796,0.00003752299,0.0002243742,0.001140521,0.00266007],"genre_scores_gemma":[0.7290087,0.0006346367,0.2638388,0.0005205363,0.00007148046,0.0002884034,0.0008510575,0.0004321198,0.004354249],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006255145,"threshold_uncertainty_score":0.01243746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1810785961348111,"score_gpt":0.475415233750865,"score_spread":0.2943366376160539,"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."}}