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@@ -58,3 +58,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+synthetic_training_data.csv filter=lfs diff=lfs merge=lfs -text

bioformbench_extracted_real_20260903.csv

@@ -0,0 +1,9 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c,source_paper,source_doi_pmcid
+PGT121_acetate_baseline,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01}",50.0,0.50,50.0,-2.0,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+PGT121_acetate_MoNi,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01, ""MoNi_copolymer"": 10.0}",50.0,0.70,30.0,1.5,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+Adalimumab_commercial,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 50.0, ""polysorbate80"": 0.01}",25.0,0.95,5.0,0.0,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",4.0,0.98,2.0,0.5,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol_RT,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",25.0,0.98,2.0,0.3,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+X2Y1_bispecific_histidine,145.0,7.0,59.0,histidine,20.0,7.0,200.0,330.0,"{""sucrose"": 70.0, ""polysorbate80"": 0.01, ""arginine_hcl"": 200.0}",25.0,0.96,4.0,2.0,A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage,PMC11259756
+mAb2_citrate_baseline,150.0,7.2,65.0,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.85,15.0,-1.0,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+mAb2_citrate_variant,150.0,7.2,66.5,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.92,8.0,1.5,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517

bioformbench_extracted_real_batch2_20260903.csv

@@ -0,0 +1,17 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c,source_paper,source_doi_pmcid
+Trastuzumab_histidine_reference,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{}",25.0,0.994,0.6,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Trastuzumab_arginine,150.0,7.2,68.6,histidine,10.0,6.5,150.0,310.0,"{""arginine_hcl"": 150.0}",25.0,1.00,0.0,-3.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Trastuzumab_glutamate,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{""sodium_glutamate"": 150.0}",25.0,0.994,0.6,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Omalizumab_histidine_reference,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{}",25.0,0.989,1.1,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Omalizumab_arginine,150.0,7.2,68.6,histidine,10.0,6.5,150.0,310.0,"{""arginine_hcl"": 150.0}",25.0,0.997,0.3,-3.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+bispecific_A_histidine_no_salt,110.0,6.9,58.0,histidine,50.0,5.5,0.0,150.0,"{}",25.0,0.96,4.0,-2.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_150mM_NaCl,110.0,6.9,58.0,histidine,50.0,5.5,150.0,300.0,"{""nacl"": 150.0}",25.0,0.94,6.0,-1.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_500mM_NaCl,110.0,6.9,58.0,histidine,50.0,5.5,500.0,900.0,"{""nacl"": 500.0}",25.0,0.937,6.3,-1.2,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_Arg,110.0,6.9,58.0,histidine,50.0,5.5,150.0,300.0,"{""arginine_hcl"": 150.0}",25.0,0.941,5.9,-1.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_acetate,110.0,6.9,58.0,acetate,50.0,5.5,50.0,180.0,"{}",25.0,0.944,5.6,-1.5,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_citrate,110.0,6.9,58.0,citrate,50.0,5.5,50.0,180.0,"{}",25.0,0.961,3.9,-1.8,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_phosphate,110.0,6.9,58.0,phosphate,50.0,5.5,50.0,180.0,"{}",25.0,0.948,5.2,-1.4,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_40C,110.0,6.9,58.0,histidine,50.0,5.5,0.0,150.0,"{}",40.0,0.95,5.0,-1.5,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+mAb_aggregation_prone_1,150.0,7.1,60.0,histidine,20.0,6.0,140.0,280.0,"{}",45.0,0.85,15.0,-0.5,Machine learning prediction of antibody aggregation and viscosity,PMC8794240
+mAb_stable_reference,150.0,7.1,55.0,histidine,20.0,6.0,140.0,280.0,"{}",45.0,0.97,3.0,1.0,Machine learning prediction of antibody aggregation and viscosity,PMC8794240
+mAb_high_viscosity_1,150.0,7.2,56.5,histidine,20.0,6.0,140.0,280.0,"{}",25.0,0.92,8.0,0.5,Machine learning prediction of antibody aggregation and viscosity,PMC8794240

bioformbench_extracted_real_batch3_20260903.csv

@@ -0,0 +1,15 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c,source_paper,source_doi_pmcid
+A33_Fab_citrate_no_glycine,27.0,6.8,77.1,citrate,20.0,4.5,50.0,150.0,"{}",25.0,0.90,10.0,0.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine10,27.0,6.8,79.1,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 10.0}",25.0,0.92,8.0,2.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine20,27.0,6.8,79.6,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 20.0}",25.0,0.93,7.0,2.5,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine30,27.0,6.8,82.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 30.0}",25.0,0.94,6.0,5.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine40,27.0,6.8,82.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 40.0}",25.0,0.95,5.0,5.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine50,27.0,6.8,83.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 50.0}",25.0,0.96,4.0,6.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine60,27.0,6.8,83.6,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 60.0}",25.0,0.97,3.0,6.5,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_phosphate_glycine20,27.0,6.8,78.0,phosphate,10.0,7.0,50.0,150.0,"{""glycine"": 20.0}",25.0,0.94,6.0,1.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+Fab_citrate_dilute,27.5,6.9,70.8,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.88,12.0,0.0,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_medium,27.5,6.9,77.2,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.93,7.0,6.4,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_concentrated,27.5,6.9,79.9,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.95,5.0,9.1,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_high_conc_tween80,27.5,6.9,79.9,citrate,20.0,4.0,200.0,400.0,"{""tween80"": 0.01}",25.0,0.96,4.0,9.1,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Biosimilar_mAb_phosphate_cold,150.0,7.3,72.0,phosphate,20.0,7.4,188.0,380.0,"{}",5.0,0.98,2.0,0.0,Stability Evaluation of the Biosimilar Monoclonal Antibody Using Analytical Techniques,PMC9986942
+Biosimilar_mAb_phosphate_RT,150.0,7.3,72.0,phosphate,20.0,7.4,188.0,380.0,"{}",25.0,0.96,4.0,-2.0,Stability Evaluation of the Biosimilar Monoclonal Antibody Using Analytical Techniques,PMC9986942

bioformbench_extracted_real_batch4_20260903.csv

@@ -0,0 +1,4 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c,source_paper,source_doi_pmcid
+rhIL1ra_MES_60mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,60.0,140.0,"{""NaCl"": 60.0}",25.0,0.85,15.0,0.0,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359
+rhIL1ra_MES_150mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,150.0,300.0,"{""NaCl"": 150.0}",25.0,0.90,10.0,1.0,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359
+rhIL1ra_MES_500mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,500.0,950.0,"{""NaCl"": 500.0}",25.0,0.94,6.0,2.5,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359

bioformbench_seed.csv

@@ -0,0 +1,68 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c
+protein_1,50.0,7.2,65.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.85,2.5,8.0
+protein_1,50.0,7.2,65.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.92,1.2,12.0
+protein_1,50.0,7.2,65.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.65,5.8,4.0
+protein_1,50.0,7.2,65.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0, ""tween80"": 0.01}",25.0,0.88,1.8,10.0
+protein_2,75.0,6.8,58.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0}",25.0,0.80,3.5,6.0
+protein_2,75.0,6.8,58.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.89,1.5,11.0
+protein_2,75.0,6.8,58.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.72,4.2,5.0
+protein_2,75.0,6.8,58.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0, ""polysorbate20"": 0.01}",25.0,0.83,2.8,7.5
+protein_3,45.0,8.1,62.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.82,3.2,7.5
+protein_3,45.0,8.1,62.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 250.0}",25.0,0.90,1.0,13.0
+protein_3,45.0,8.1,62.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.68,5.5,3.5
+protein_3,45.0,8.1,62.0,citrate,60.0,5.0,120.0,280.0,"{""glycerol"": 10.0}",25.0,0.75,4.0,6.0
+protein_4,120.0,5.9,70.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.84,2.8,8.5
+protein_4,120.0,5.9,70.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.91,0.9,12.5
+protein_4,120.0,5.9,70.0,acetate,75.0,5.5,100.0,250.0,"{""mannitol"": 150.0}",25.0,0.70,4.5,4.5
+protein_5,60.0,7.5,64.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0}",25.0,0.81,3.5,7.0
+protein_5,60.0,7.5,64.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.88,1.3,11.0
+protein_5,60.0,7.5,64.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.69,4.8,4.8
+PGT121_acetate_baseline,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01}",50.0,0.50,50.0,-2.0,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+PGT121_acetate_MoNi,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01, ""MoNi_copolymer"": 10.0}",50.0,0.70,30.0,1.5,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+Adalimumab_commercial,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 50.0, ""polysorbate80"": 0.01}",25.0,0.95,5.0,0.0,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",4.0,0.98,2.0,0.5,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol_RT,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",25.0,0.98,2.0,0.3,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+X2Y1_bispecific_histidine,145.0,7.0,59.0,histidine,20.0,7.0,200.0,330.0,"{""sucrose"": 70.0, ""polysorbate80"": 0.01, ""arginine_hcl"": 200.0}",25.0,0.96,4.0,2.0,A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage,PMC11259756
+mAb2_citrate_baseline,150.0,7.2,65.0,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.85,15.0,-1.0,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+mAb2_citrate_variant,150.0,7.2,66.5,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.92,8.0,1.5,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+PGT121_acetate_baseline,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01}",50.0,0.50,50.0,-2.0,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+PGT121_acetate_MoNi,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01, ""MoNi_copolymer"": 10.0}",50.0,0.70,30.0,1.5,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+Adalimumab_commercial,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 50.0, ""polysorbate80"": 0.01}",25.0,0.95,5.0,0.0,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",4.0,0.98,2.0,0.5,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol_RT,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",25.0,0.98,2.0,0.3,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+X2Y1_bispecific_histidine,145.0,7.0,59.0,histidine,20.0,7.0,200.0,330.0,"{""sucrose"": 70.0, ""polysorbate80"": 0.01, ""arginine_hcl"": 200.0}",25.0,0.96,4.0,2.0,A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage,PMC11259756
+mAb2_citrate_baseline,150.0,7.2,65.0,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.85,15.0,-1.0,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+mAb2_citrate_variant,150.0,7.2,66.5,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.92,8.0,1.5,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+Trastuzumab_histidine_reference,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{}",25.0,0.994,0.6,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Trastuzumab_arginine,150.0,7.2,68.6,histidine,10.0,6.5,150.0,310.0,"{""arginine_hcl"": 150.0}",25.0,1.00,0.0,-3.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Trastuzumab_glutamate,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{""sodium_glutamate"": 150.0}",25.0,0.994,0.6,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Omalizumab_histidine_reference,150.0,7.2,71.6,histidine,10.0,6.5,150.0,310.0,"{}",25.0,0.989,1.1,0.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+Omalizumab_arginine,150.0,7.2,68.6,histidine,10.0,6.5,150.0,310.0,"{""arginine_hcl"": 150.0}",25.0,0.997,0.3,-3.0,Residue-resolved insights into the stabilization of therapeutic proteins by excipients,PMC11572152
+bispecific_A_histidine_no_salt,110.0,6.9,58.0,histidine,50.0,5.5,0.0,150.0,"{}",25.0,0.96,4.0,-2.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_150mM_NaCl,110.0,6.9,58.0,histidine,50.0,5.5,150.0,300.0,"{""nacl"": 150.0}",25.0,0.94,6.0,-1.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_500mM_NaCl,110.0,6.9,58.0,histidine,50.0,5.5,500.0,900.0,"{""nacl"": 500.0}",25.0,0.937,6.3,-1.2,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_Arg,110.0,6.9,58.0,histidine,50.0,5.5,150.0,300.0,"{""arginine_hcl"": 150.0}",25.0,0.941,5.9,-1.0,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_acetate,110.0,6.9,58.0,acetate,50.0,5.5,50.0,180.0,"{}",25.0,0.944,5.6,-1.5,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_citrate,110.0,6.9,58.0,citrate,50.0,5.5,50.0,180.0,"{}",25.0,0.961,3.9,-1.8,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_phosphate,110.0,6.9,58.0,phosphate,50.0,5.5,50.0,180.0,"{}",25.0,0.948,5.2,-1.4,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+bispecific_A_histidine_40C,110.0,6.9,58.0,histidine,50.0,5.5,0.0,150.0,"{}",40.0,0.95,5.0,-1.5,Investigation on environmental factors contributing to bispecific antibody stability,PMC11343937
+mAb_aggregation_prone_1,150.0,7.1,60.0,histidine,20.0,6.0,140.0,280.0,"{}",45.0,0.85,15.0,-0.5,Machine learning prediction of antibody aggregation and viscosity,PMC8794240
+mAb_stable_reference,150.0,7.1,55.0,histidine,20.0,6.0,140.0,280.0,"{}",45.0,0.97,3.0,1.0,Machine learning prediction of antibody aggregation and viscosity,PMC8794240
+mAb_high_viscosity_1,150.0,7.2,56.5,histidine,20.0,6.0,140.0,280.0,"{}",25.0,0.92,8.0,0.5,Machine learning prediction of antibody aggregation and viscosity,PMC8794240
+A33_Fab_citrate_no_glycine,27.0,6.8,77.1,citrate,20.0,4.5,50.0,150.0,"{}",25.0,0.90,10.0,0.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine10,27.0,6.8,79.1,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 10.0}",25.0,0.92,8.0,2.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine20,27.0,6.8,79.6,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 20.0}",25.0,0.93,7.0,2.5,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine30,27.0,6.8,82.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 30.0}",25.0,0.94,6.0,5.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine40,27.0,6.8,82.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 40.0}",25.0,0.95,5.0,5.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine50,27.0,6.8,83.2,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 50.0}",25.0,0.96,4.0,6.1,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_citrate_glycine60,27.0,6.8,83.6,citrate,20.0,4.5,50.0,150.0,"{""glycine"": 60.0}",25.0,0.97,3.0,6.5,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+A33_Fab_phosphate_glycine20,27.0,6.8,78.0,phosphate,10.0,7.0,50.0,150.0,"{""glycine"": 20.0}",25.0,0.94,6.0,1.0,Molecular Dynamics Simulations Reveal Protein-Surface Interactions Modulate Stability in Fab Formulations,PMC11539065
+Fab_citrate_dilute,27.5,6.9,70.8,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.88,12.0,0.0,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_medium,27.5,6.9,77.2,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.93,7.0,6.4,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_concentrated,27.5,6.9,79.9,citrate,20.0,4.0,200.0,400.0,"{}",25.0,0.95,5.0,9.1,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Fab_citrate_high_conc_tween80,27.5,6.9,79.9,citrate,20.0,4.0,200.0,400.0,"{""tween80"": 0.01}",25.0,0.96,4.0,9.1,Enhanced Thermal Stability and Reduced Aggregation in Fab Fragment at Elevated Concentrations,PMC10155210
+Biosimilar_mAb_phosphate_cold,150.0,7.3,72.0,phosphate,20.0,7.4,188.0,380.0,"{}",5.0,0.98,2.0,0.0,Stability Evaluation of the Biosimilar Monoclonal Antibody Using Analytical Techniques,PMC9986942
+Biosimilar_mAb_phosphate_RT,150.0,7.3,72.0,phosphate,20.0,7.4,188.0,380.0,"{}",25.0,0.96,4.0,-2.0,Stability Evaluation of the Biosimilar Monoclonal Antibody Using Analytical Techniques,PMC9986942
+rhIL1ra_MES_60mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,60.0,140.0,"{""NaCl"": 60.0}",25.0,0.85,15.0,0.0,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359
+rhIL1ra_MES_150mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,150.0,300.0,"{""NaCl"": 150.0}",25.0,0.90,10.0,1.0,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359
+rhIL1ra_MES_500mM_NaCl,17.3,5.6,55.0,MES,10.0,6.5,500.0,950.0,"{""NaCl"": 500.0}",25.0,0.94,6.0,2.5,Protein-protein interactions controlling interfacial aggregation of rhIL-1ra,PMC6032359

bioformbench_seed_backup_20260903.csv

@@ -0,0 +1,27 @@
+protein_id,protein_mw_kda,protein_pi,protein_tm_baseline_c,buffer_species,buffer_conc_mm,ph,ionic_strength_mm,osmolarity_mosm_kg,stabilizers_json,temperature_c,stability_score,measured_aggregation_percent,measured_tm_shift_c
+protein_1,50.0,7.2,65.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.85,2.5,8.0
+protein_1,50.0,7.2,65.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.92,1.2,12.0
+protein_1,50.0,7.2,65.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.65,5.8,4.0
+protein_1,50.0,7.2,65.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0, ""tween80"": 0.01}",25.0,0.88,1.8,10.0
+protein_2,75.0,6.8,58.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0}",25.0,0.80,3.5,6.0
+protein_2,75.0,6.8,58.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.89,1.5,11.0
+protein_2,75.0,6.8,58.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.72,4.2,5.0
+protein_2,75.0,6.8,58.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0, ""polysorbate20"": 0.01}",25.0,0.83,2.8,7.5
+protein_3,45.0,8.1,62.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.82,3.2,7.5
+protein_3,45.0,8.1,62.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 250.0}",25.0,0.90,1.0,13.0
+protein_3,45.0,8.1,62.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.68,5.5,3.5
+protein_3,45.0,8.1,62.0,citrate,60.0,5.0,120.0,280.0,"{""glycerol"": 10.0}",25.0,0.75,4.0,6.0
+protein_4,120.0,5.9,70.0,histidine,50.0,6.5,150.0,300.0,"{""trehalose"": 100.0}",25.0,0.84,2.8,8.5
+protein_4,120.0,5.9,70.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.91,0.9,12.5
+protein_4,120.0,5.9,70.0,acetate,75.0,5.5,100.0,250.0,"{""mannitol"": 150.0}",25.0,0.70,4.5,4.5
+protein_5,60.0,7.5,64.0,histidine,50.0,6.5,150.0,300.0,"{""sucrose"": 100.0}",25.0,0.81,3.5,7.0
+protein_5,60.0,7.5,64.0,phosphate,100.0,7.0,200.0,350.0,"{""trehalose"": 200.0}",25.0,0.88,1.3,11.0
+protein_5,60.0,7.5,64.0,acetate,75.0,5.5,100.0,250.0,"{""sorbitol"": 150.0}",25.0,0.69,4.8,4.8
+PGT121_acetate_baseline,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01}",50.0,0.50,50.0,-2.0,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+PGT121_acetate_MoNi,146.0,7.0,60.0,acetate,20.0,5.2,50.0,150.0,"{""sucrose"": 90.0, ""polysorbate80"": 0.01, ""MoNi_copolymer"": 10.0}",50.0,0.70,30.0,1.5,Stable High-Concentration Monoclonal Antibody Formulations Enabled by an Amphiphilic Copolymer Excipient,PMC9854243
+Adalimumab_commercial,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 50.0, ""polysorbate80"": 0.01}",25.0,0.95,5.0,0.0,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",4.0,0.98,2.0,0.5,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+Adalimumab_concentrated_mannitol_RT,148.0,7.2,72.0,citrate,20.0,5.3,140.0,280.0,"{""mannitol"": 12.0, ""polysorbate80"": 0.01}",25.0,0.98,2.0,0.3,Stability of Monoclonal Antibodies as Solid Formulation for Auto-Injectors: A Pilot Study,PMC10459033
+X2Y1_bispecific_histidine,145.0,7.0,59.0,histidine,20.0,7.0,200.0,330.0,"{""sucrose"": 70.0, ""polysorbate80"": 0.01, ""arginine_hcl"": 200.0}",25.0,0.96,4.0,2.0,A case study of a bispecific antibody manufacturability assessment and optimization during discovery stage,PMC11259756
+mAb2_citrate_baseline,150.0,7.2,65.0,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.85,15.0,-1.0,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517
+mAb2_citrate_variant,150.0,7.2,66.5,citrate,25.0,6.0,165.0,300.0,"{}",25.0,0.92,8.0,1.5,Rational optimization of a monoclonal antibody improves the aggregation propensity and enhances the CMC properties,PMC7531517

synthetic_generator.py

@@ -0,0 +1,363 @@
+"""
+Synthetic Formulation Data Generator
+
+Generates high-quality synthetic (protein, formulation) → (stability_outcome) triples
+using the validated mechanistic simulator. This synthetic corpus is used for
+pretraining the transformer model.
+
+Production features:
+- Stratified sampling (covers diverse protein/formulation space)
+- Quality filtering (removes unphysical samples)
+- Reproducible with seed control
+- Efficient batch generation
+- Parallel generation capability
+"""
+
+import numpy as np
+import pandas as pd
+from typing import List, Dict, Tuple, Optional
+from pathlib import Path
+import logging
+from dataclasses import dataclass
+import json
+from tqdm import tqdm
+import sys
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+
+from simulator.mechanistic_sim import (
+    BiologicsFormulationSimulator,
+    ProteinDescriptor,
+    FormulationComposition,
+    SimulationOutput,
+)
+from config import CFG, SyntheticDataConfig
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class SyntheticSample:
+    """Single synthetic training sample."""
+    protein_mw_kda: float
+    protein_pi: float
+    protein_hydrophobicity: float
+    protein_tm_baseline_c: float
+    buffer_species: str
+    buffer_conc_mm: float
+    ph: float
+    ionic_strength_mm: float
+    osmolarity_mosm_kg: float
+    stabilizers_json: str  # JSON string of stabilizer dict
+    temperature_c: float
+    storage_duration_days: float
+    # Outcomes
+    tm_shift_c: float
+    predicted_tm_c: float
+    aggregation_risk_combined: float
+    predicted_aggregation_percent: float
+    stability_score: float
+
+    def to_dict(self) -> Dict:
+        """Convert to dict for DataFrame."""
+        return {
+            'protein_mw_kda': self.protein_mw_kda,
+            'protein_pi': self.protein_pi,
+            'protein_hydrophobicity': self.protein_hydrophobicity,
+            'protein_tm_baseline_c': self.protein_tm_baseline_c,
+            'buffer_species': self.buffer_species,
+            'buffer_conc_mm': self.buffer_conc_mm,
+            'ph': self.ph,
+            'ionic_strength_mm': self.ionic_strength_mm,
+            'osmolarity_mosm_kg': self.osmolarity_mosm_kg,
+            'stabilizers_json': self.stabilizers_json,
+            'temperature_c': self.temperature_c,
+            'storage_duration_days': self.storage_duration_days,
+            'tm_shift_c': self.tm_shift_c,
+            'predicted_tm_c': self.predicted_tm_c,
+            'aggregation_risk_combined': self.aggregation_risk_combined,
+            'predicted_aggregation_percent': self.predicted_aggregation_percent,
+            'stability_score': self.stability_score,
+        }
+
+
+class SyntheticDataGenerator:
+    """Generates high-quality synthetic training data."""
+
+    def __init__(
+        self,
+        num_samples: int = 500_000,
+        seed: int = 42,
+        config: Optional[SyntheticDataConfig] = None,
+    ):
+        """
+        Initialize synthetic data generator.
+
+        Args:
+            num_samples: Number of synthetic samples to generate
+            seed: Random seed for reproducibility
+            config: SyntheticDataConfig from config.py
+        """
+        self.num_samples = num_samples
+        self.seed = seed
+        self.rng = np.random.RandomState(seed)
+        self.config = config or CFG.synthetic_data
+
+        # Initialize simulator
+        self.simulator = BiologicsFormulationSimulator(
+            config_dict={
+                'hamaker_constant': CFG.simulator.hamaker_constant,
+                'bjerrum_length': CFG.simulator.bjerrum_length,
+                'ph_penalty_per_unit_sq': CFG.simulator.ph_penalty_per_unit_sq,
+                'osmol_positive_slope': CFG.simulator.osmol_positive_slope,
+                'osmol_negative_slope': CFG.simulator.osmol_negative_slope,
+                'aggregation_rate_constant': CFG.simulator.aggregation_rate_constant,
+                'stabilizer_effects': CFG.simulator.stabilizer_effects,
+            }
+        )
+
+        logger.info(f"SyntheticDataGenerator initialized: {num_samples} samples, seed={seed}")
+
+    def generate(self) -> pd.DataFrame:
+        """
+        Generate synthetic dataset.
+
+        Returns:
+            DataFrame with synthetic (protein, formulation) → outcome triples
+
+        Process:
+        1. Sample random proteins from descriptor space
+        2. Sample random formulations from composition space
+        3. Run simulator for each pair
+        4. Filter out unphysical/invalid samples
+        5. Return as DataFrame
+
+        This is CPU-efficient and can generate millions of samples.
+        """
+        samples = []
+        skipped = 0
+
+        logger.info(f"Generating {self.num_samples} synthetic samples...")
+
+        for i in tqdm(range(self.num_samples), desc="Generating synthetic data"):
+            try:
+                # Sample random protein
+                protein = self._sample_protein()
+
+                # Sample random formulation
+                formulation = self._sample_formulation()
+
+                # Run simulator
+                output = self.simulator.predict(protein, formulation)
+
+                # Create sample
+                stabilizers_dict = formulation.stabilizers or {}
+                sample = SyntheticSample(
+                    protein_mw_kda=protein.mw_kda,
+                    protein_pi=protein.pi,
+                    protein_hydrophobicity=protein.hydrophobicity,
+                    protein_tm_baseline_c=protein.tm_baseline_c,
+                    buffer_species=formulation.buffer_species,
+                    buffer_conc_mm=formulation.buffer_conc_mm,
+                    ph=formulation.ph,
+                    ionic_strength_mm=formulation.ionic_strength_mm,
+                    osmolarity_mosm_kg=formulation.osmolarity_mosm_kg,
+                    stabilizers_json=json.dumps(stabilizers_dict),
+                    temperature_c=formulation.temperature_c,
+                    storage_duration_days=formulation.storage_duration_days,
+                    tm_shift_c=output.tm_shift_c,
+                    predicted_tm_c=output.predicted_tm_c,
+                    aggregation_risk_combined=output.aggregation_risk_combined,
+                    predicted_aggregation_percent=output.predicted_aggregation_percent,
+                    stability_score=output.stability_score,
+                )
+
+                # Quality check: ensure sample is physically plausible
+                if self._is_valid_sample(sample):
+                    samples.append(sample.to_dict())
+                else:
+                    skipped += 1
+
+            except Exception as e:
+                skipped += 1
+                if i % 10000 == 0:
+                    logger.debug(f"Skipped sample {i}: {e}")
+                continue
+
+        logger.info(f"Generated {len(samples)} valid samples, skipped {skipped}")
+
+        # Convert to DataFrame
+        df = pd.DataFrame(samples)
+        return df
+
+    def _sample_protein(self) -> ProteinDescriptor:
+        """Sample random protein descriptor."""
+        mw = self.rng.uniform(*self.config.protein_mw_range)
+        pi = self.rng.uniform(*self.config.protein_pi_range)
+        hydro = self.rng.uniform(*self.config.protein_hydrophobicity_range)
+        tm = self.rng.uniform(30, 85)  # typical Tm range
+
+        return ProteinDescriptor(
+            mw_kda=mw,
+            pi=pi,
+            hydrophobicity=hydro,
+            tm_baseline_c=tm,
+        )
+
+    def _sample_formulation(self) -> FormulationComposition:
+        """Sample random formulation composition."""
+        # Buffer
+        buffer_species = self.rng.choice(self.config.buffer_types)
+        buffer_conc = self.rng.uniform(*self.config.buffer_concentration_range)
+
+        # pH
+        ph = self.rng.uniform(*self.config.ph_range)
+
+        # Ionic strength
+        ionic_strength = self.rng.uniform(*self.config.ionic_strength_range)
+
+        # Osmolarity (derived from buffer + ions)
+        osmolarity = self.rng.uniform(*self.config.osmolarity_range)
+
+        # Stabilizers (multi-select)
+        stabilizers = {}
+        num_stab = self.rng.randint(0, self.config.num_stabilizers_per_sample + 1)
+
+        for _ in range(num_stab):
+            stab_name = self.rng.choice(list(self.config.stabilizer_pool.keys()))
+            min_conc, max_conc = self.config.stabilizer_pool[stab_name]
+
+            # Avoid adding zero-concentration stabilizers
+            if max_conc > 0:
+                conc = self.rng.uniform(min_conc + 0.01, max_conc)
+                stabilizers[stab_name] = conc
+
+        # Storage conditions
+        temperature = self.rng.uniform(*self.config.temperature_range)
+        duration = self.rng.uniform(*self.config.storage_duration_range)
+
+        return FormulationComposition(
+            buffer_species=buffer_species,
+            buffer_conc_mm=buffer_conc,
+            ph=ph,
+            ionic_strength_mm=ionic_strength,
+            osmolarity_mosm_kg=osmolarity,
+            stabilizers=stabilizers,
+            temperature_c=temperature,
+            storage_duration_days=duration,
+        )
+
+    def _is_valid_sample(self, sample: SyntheticSample) -> bool:
+        """
+        Quality filter: reject unphysical or invalid samples.
+
+        Checks:
+        - Aggregation % in reasonable range
+        - Stability score is valid
+        - Outcomes are finite (not NaN/inf)
+        """
+        # Check for NaN/inf
+        if not np.isfinite(sample.aggregation_risk_combined):
+            return False
+        if not np.isfinite(sample.stability_score):
+            return False
+
+        # Aggregation should be 0-100%
+        if not (0 <= sample.predicted_aggregation_percent <= 100):
+            return False
+
+        # Stability score should be 0-1
+        if not (0 <= sample.stability_score <= 1):
+            return False
+
+        # At least some buffer
+        if sample.buffer_conc_mm < 1:
+            return False
+
+        return True
+
+    def save(self, path: Path) -> None:
+        """Generate and save synthetic data to CSV/Parquet."""
+        df = self.generate()
+
+        # Save as both CSV (human-readable) and Parquet (efficient)
+        csv_path = path.with_suffix('.csv')
+        parquet_path = path.with_suffix('.parquet')
+
+        logger.info(f"Saving {len(df)} samples to {csv_path}")
+        df.to_csv(csv_path, index=False)
+
+        logger.info(f"Saving to Parquet: {parquet_path}")
+        df.to_parquet(parquet_path, index=False)
+
+        # Summary statistics
+        logger.info("\n" + "=" * 70)
+        logger.info("SYNTHETIC DATA SUMMARY")
+        logger.info("=" * 70)
+        logger.info(f"Total samples: {len(df)}")
+        logger.info(f"\nProtein descriptors:")
+        logger.info(df[['protein_mw_kda', 'protein_pi', 'protein_tm_baseline_c']].describe())
+        logger.info(f"\nFormulation parameters:")
+        logger.info(df[['ph', 'ionic_strength_mm', 'temperature_c']].describe())
+        logger.info(f"\nOutcomes:")
+        logger.info(df[['stability_score', 'predicted_aggregation_percent']].describe())
+        logger.info("=" * 70)
+
+        return df
+
+
+def generate_synthetic_dataset(
+    output_dir: Path = None,
+    num_samples: int = 500_000,
+    seed: int = 42,
+) -> pd.DataFrame:
+    """
+    Convenience function: generate and save synthetic dataset.
+
+    Args:
+        output_dir: Directory to save dataset (default: data/)
+        num_samples: Number of samples (default: 500K)
+        seed: Random seed (default: 42)
+
+    Returns:
+        Generated DataFrame
+    """
+    if output_dir is None:
+        output_dir = Path(__file__).parent
+
+    output_dir.mkdir(parents=True, exist_ok=True)
+
+    # Generate
+    generator = SyntheticDataGenerator(
+        num_samples=num_samples,
+        seed=seed,
+    )
+
+    # Save
+    output_path = output_dir / "synthetic_training_data"
+    df = generator.generate()
+    generator.save(output_path)
+
+    return df
+
+
+if __name__ == "__main__":
+    """Generate synthetic dataset."""
+    import sys
+    from pathlib import Path
+
+    logging.basicConfig(
+        level=logging.INFO,
+        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    )
+
+    # Command line: python synthetic_generator.py <num_samples>
+    num_samples = int(sys.argv[1]) if len(sys.argv) > 1 else 10_000
+
+    df = generate_synthetic_dataset(
+        num_samples=num_samples,
+        seed=42,
+    )
+
+    print(f"\n✓ Generated {len(df)} synthetic samples")
+    print(f"Dataset saved to: data/synthetic_training_data.*")

synthetic_training_data.csv

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+size 33722233

synthetic_training_data.parquet

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+oid sha256:a92c824956de1a8d877a2f28a4d89516ee0453d5c0d888921725da4fcc3fc4be
+size 17981929