Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_plm_randomf0_20261104-174442

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
train0.08820.96010.87710.92122984.00000.2856481.00000.60003.78863.95800.00000.00000.80890.78003.80404.10790.02000.00000.85460.78003.80544.10790.10000.00000.87290.68833.77344.10790.20990.00000.8876
validation0.10270.95380.86750.90911025.00000.2166256.00000.90003.83364.08600.10000.00000.83080.70003.76344.08600.04000.00000.83820.68003.75334.08600.11000.00000.85940.64813.74744.08600.10190.00000.8582
test0.10430.95290.86580.90771035.00000.244420.00000.60003.76624.01930.00000.00000.80130.70003.78324.10680.12000.00000.85580.66003.76414.10680.15000.00000.87030.66673.76234.10680.14810.00000.8720

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_plm_randomf0
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index,plm,plm_sum
  split_dir: null
  graph_threshold: 7
  split_scheme: random
  split_fold: 0
model:
  name: gcn
  filters:
  - 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