Protein Engineering

gb1 · Graph CNN

reference_gb1_gcn_contiguousf1_20261104-154104

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
train0.58350.94230.94480.883233066.00000.511419713.00001.00001.41601.62090.00000.92361.00001.43421.82200.24000.95941.00001.35711.82200.34000.95430.99320.91121.82200.44450.9324
validation11.99640.14900.0286-1.22392092.00000.24632055.00000.0000-5.6779-4.65820.00000.0000-0.35700.0000-5.4516-3.49180.00000.0000-0.32500.0000-5.5098-2.55480.00000.0000-0.34210.0000-5.5028-2.55480.00000.0000-0.3487
test2.10270.28860.3667-0.04205403.00000.18563140.00000.4000-1.01650.72810.00000.0000-0.00120.5000-0.61140.72810.00000.00000.18400.6100-0.28161.34260.01000.00000.34840.6020-0.30421.34260.04480.00000.3540

Loss per epoch

training validation

Config

name: reference_gb1_gcn_contiguousf1
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: contiguous
  split_fold: 1
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