{"id":"W4405262384","doi":"10.21203/rs.3.rs-5538361/v1","title":"A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Computer science; Trajectory; Molecular dynamics; Speedup; Solver; Algorithm; Physics; Computational chemistry; Chemistry; Parallel computing","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":["metaepi_narrow","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008132018,0.0005360369,0.0006658313,0.001718336,0.0009922592,0.002437278,0.001471846,0.0005704617,0.0001764623],"category_scores_gemma":[0.006842385,0.0004845478,0.0002725235,0.00107604,0.0003266778,0.0001789153,0.004119908,0.002560419,0.0004869079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039717,"about_ca_system_score_gemma":0.0008775296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003280038,"about_ca_topic_score_gemma":0.00008544147,"domain_scores_codex":[0.9925246,0.001104929,0.0007791868,0.001842495,0.002255389,0.001493431],"domain_scores_gemma":[0.9969296,0.0007306022,0.0003495975,0.0008304922,0.0008760175,0.0002836928],"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":[0.0001503081,0.0001435844,0.0001605521,0.006155766,0.00002417662,0.0000153542,0.001130707,0.1995531,0.7856263,0.005861893,0.0001059453,0.00107226],"study_design_scores_gemma":[0.0004592085,0.0002119309,0.00008270503,0.001461415,0.00002938147,0.000002616164,0.0001561208,0.9692457,0.01768844,0.01013834,0.00003032243,0.000493847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7143862,0.000140712,0.2796484,0.0004281718,0.0009638422,0.003401225,0.0004024282,0.0004699028,0.000159162],"genre_scores_gemma":[0.9459574,0.00002148778,0.04371891,0.000005164971,0.0004405334,0.001891227,0.000463725,0.0001553542,0.007346232],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7696925,"threshold_uncertainty_score":0.9997606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1007589968755159,"score_gpt":0.4058766081345623,"score_spread":0.3051176112590464,"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."}}