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115 lines
4.4 KiB
Markdown
115 lines
4.4 KiB
Markdown
## Model Architecture
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1. Input(8 tokens)：
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```
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# chem token([B, 600])
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SMILES(string) + mpnn_encoder -> chem token
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# morgan token([B, 1024]), maccs token([B, 167]), rdkit token([B, 210])
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SMILES(string) + rdkit_encoder-> chem token, morgan token, maccs token, rdkit token
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# comp token([B, 5])
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Cationic_Lipid_to_mRNA_weight_ratio(float)
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Cationic_Lipid_Mol_Ratio(float)
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Phospholipid_Mol_Ratio(float)
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Cholesterol_Mol_Ratio(float)
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PEG_Lipid_Mol_Ratio(float)
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# phys token([B, 12])
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Purity_Pure(one-hot for Purity)
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Purity_Crude(one-hot for Purity)
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Mix_type_Microfluidic(one-hot for Mix_type)
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Mix_type_Microfluidic(one-hot for Mix_type)
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Cargo_type_mRNA(one-hot for Cargo_type)
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Cargo_type_pDNA(one-hot for Cargo_type)
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Cargo_type_siRNA(one-hot for Cargo_type)
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Target_or_delivered_gene_FFL(one-hot for Target_or_delivered_gene)
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Target_or_delivered_gene_Peptide_barcode(one-hot for Target_or_delivered_gene)
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Target_or_delivered_gene_hEPO(one-hot for Target_or_delivered_gene)
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Target_or_delivered_gene_FVII(one-hot for Target_or_delivered_gene)
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Target_or_delivered_gene_GFP(one-hot for Target_or_delivered_gene)
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# help token([B, 4])
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Helper_lipid_ID_DOPE(one-hot for Helper_lipid_ID)
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Helper_lipid_ID_DOTAP(one-hot for Helper_lipid_ID)
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Helper_lipid_ID_DSPC(one-hot for Helper_lipid_ID)
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Helper_lipid_ID_MDOA(one-hot for Helper_lipid_ID)
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# exp token([B, 32])
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Model_type_A549（one-hot for Model_type）
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Model_type_BDMC（one-hot for Model_type）
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Model_type_BMDM（one-hot for Model_type）
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Model_type_HBEC_ALI（one-hot for Model_type）
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Model_type_HEK293T（one-hot for Model_type）
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Model_type_HeLa（one-hot for Model_type）
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Model_type_IGROV1（one-hot for Model_type）
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Model_type_Mouse（one-hot for Model_type）
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Model_type_RAW264p7（one-hot for Model_type）
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Delivery_target_dendritic_cell（one-hot for Delivery_target）
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Delivery_target_generic_cell（one-hot for Delivery_target）
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Delivery_target_liver（one-hot for Delivery_target）
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Delivery_target_lung（one-hot for Delivery_target）
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Delivery_target_lung_epithelium（one-hot for Delivery_target）
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Delivery_target_macrophage（one-hot for Delivery_target）
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Delivery_target_muscle（one-hot for Delivery_target）
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Delivery_target_spleen（one-hot for Delivery_target）
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Delivery_target_body（one-hot for Delivery_target）
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Route_of_administration_in_vitro（one-hot for Route_of_administration）
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Route_of_administration_intravenous（one-hot for Route_of_administration）
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Route_of_administration_intramuscular（one-hot for Route_of_administration）
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Route_of_administration_intratracheal（one-hot for Route_of_administration）
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Sample_organization_type_individual（one-hot for Sample_organization_type）
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Sample_organization_type_barcoded（one-hot for Sample_organization_type）
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Value_name_log_luminescence(one-hot for Value_name)
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Value_name_luminescence(one-hot for Value_name)
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Value_name_FFL_silencing(one-hot for Value_name)
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Value_name_Peptide_abundance(one-hot for Value_name)
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Value_name_hEPO(one-hot for Value_name)
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Value_name_FVII_silencing(one-hot for Value_name)
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Value_name_GFP_delivery(one-hot for Value_name)
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Value_name_Discretized_luminescence(one-hot for Value_name)
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```
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2. token projector
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3. Channel split: to A[chem, Morgan, Maccs, rdkit] and B[comp, pays, help, exp]
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4. Bi-directional cross attention
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5. Token re-compose: back to [chem, Morgan, maccs, rdkit, comp, physical, help, exp]
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6. Fusion layer(depends on user choice)
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7. heads：
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```
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# regression head
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size(float, training data already logged)
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# classification head
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toxic(boolean, 0/1)
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# regression head
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quantified_delivery(float, training data already z-scored)
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# classification head
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PDI_0_0to0_2(one-hot classification for PDI)
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PDI_0_2to0_3(one-hot classification for PDI)
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PDI_0_3to0_4(one-hot classification for PDI)
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PDI_0_4to0_5(one-hot classification for PDI)
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# classification head
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Encapsulation_Efficiency_EE<50(one-hot classification for Encapsulation_Efficiency)
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Encapsulation_Efficiency_50<=EE<80(one-hot classification for Encapsulation_Efficiency)
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Encapsulation_Efficiency_80<EE<=100(one-hot classification for Encapsulation_Efficiency)
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# distribution head
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Biodistribution_lymph_nodes(float, sum of Biodistribution is 1)
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Biodistribution_heart(float, sum of Biodistribution is 1)
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Biodistribution_liver(float, sum of Biodistribution is 1)
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Biodistribution_spleen(float, sum of Biodistribution is 1)
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Biodistribution_lung(float, sum of Biodistribution is 1)
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Biodistribution_kidney(float, sum of Biodistribution is 1)
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Biodistribution_muscle(float, sum of Biodistribution is 1)
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```
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