Protein Engineering

gb1 · Graph CNN

reference_gb1_gcn_contiguousf3_20261104-161838

Metrics

Setmsepearsonrspearmanrr2above_wt_nabove_wt_spearmanrbudget_to_maxhit_rate_at_10mean_at_10max_at_10precision_at_10captured_at_10hit_rate_at_50mean_at_50max_at_50precision_at_50captured_at_50hit_rate_at_100mean_at_100max_at_100precision_at_100captured_at_100hit_rate_at_1pctmean_at_1pctmax_at_1pctprecision_at_1pctcaptured_at_1pct
train0.60550.93790.94090.875830012.00000.5203275.00001.00001.35371.60970.00000.81771.00001.32121.68780.00000.85641.00001.31911.78740.02000.87560.99631.15802.52950.52960.9364
validation3.07120.63080.54920.30755856.00000.17007021.00000.0000-1.5644-1.06170.00000.0000-0.00060.0200-1.55660.21710.00000.00000.00210.0100-1.53610.21710.00000.00000.00970.0846-1.34120.82300.00000.00000.0841
test10.45460.43660.4359-0.90622473.00000.14912207.00000.0000-0.3586-0.04570.00000.00000.61320.2800-0.29011.53880.04000.00000.63790.2200-0.72291.53880.03000.00000.55720.2650-0.89881.53880.01500.00000.5314

Loss per epoch

training validation

Config

name: reference_gb1_gcn_contiguousf3
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: contiguous
  split_fold: 3
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: 7