Protein Engineering

gb1 · Graph CNN

reference_gb1_gcn_s2_20261104-120827

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10tie_share_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50tie_share_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100tie_share_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pcttie_share_at_1pctcaptured_at_1pct
test0.19500.98120.97950.96279438.00000.864388.00001.00001.68141.93620.20000.00000.93551.00001.55631.93620.34000.00000.95681.00001.54412.08880.58000.00000.97441.00001.28912.08880.75000.00000.9840
train0.15540.98500.98400.970176104.00000.87430.00000.86500.08000.91480.16000.93040.77500.9856
validation0.19980.98060.98000.96158494.00000.871818.00001.00001.63451.79800.10000.00000.94771.00001.55561.93520.42000.00000.96031.00001.51861.93520.52000.00000.97171.00001.30631.93520.78010.00000.9873

Loss per epoch

training validation

Config

name: reference_gb1_gcn_s2
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index
  split_dir: gb1/splits/main
  graph_threshold: 7
model:
  name: gcn
  filters:
  - 128
  - 128
  - 128
  - 128
  - 128
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 32
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 2