Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_randomf1_20261104-145831

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
train0.06830.96930.88190.93923013.00000.2905653.00000.70003.83604.10680.20000.83790.76003.82094.10680.10000.85860.68003.79774.10680.10000.86130.73153.78724.10680.22530.8948
validation0.08620.96080.87170.92301006.00000.258245.00000.60003.52953.89900.00000.00000.64440.74003.75304.10790.16000.00000.84820.72003.76034.10790.22000.00000.87810.72223.75884.10790.22220.00000.8801
test0.08440.96200.86950.92531025.00000.2336662.00000.80003.83084.08600.10000.00000.82890.70003.76874.08600.06000.00000.84210.70003.77974.11360.15000.00000.87950.69443.77944.11360.16670.00000.8826

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_randomf1
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: null
  graph_threshold: 7
  split_scheme: random
  split_fold: 1
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