Protein Engineering

bgl3 · Graph CNN

reference_bgl3_gcn_s5_20261104-140838

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
test1.29910.56270.50490.31641036.00000.1768268.00001.00001.41312.08410.00000.00000.60430.70000.72482.62040.06000.00000.41850.70000.70432.62040.08000.00000.44320.74070.78872.08410.03700.00000.4186
train1.21470.62070.58650.38238299.00000.20034867.00001.00001.56733.11600.10000.54250.92001.28773.11600.02000.52090.93001.22823.11600.03000.53170.89811.14863.11600.05560.5392
validation1.38790.54590.49760.2979933.00000.07851447.00000.90000.96961.69460.00000.00000.44710.76000.79772.85480.04000.00000.44820.76000.77252.85480.08000.00000.47320.79170.86631.86900.00000.00000.4419

Loss per epoch

training validation

Config

name: reference_bgl3_gcn_s5
task: dms
data:
  dataset: bgl3
  encoding: one_hot,aa_index
  split_dir: bgl3/splits/main
  graph_threshold: 6
model:
  name: gcn
  filters:
  - 32
  hidden:
  - 100
  dropout: 0.2
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 5