Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_s5_20261104-140307

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
test0.08530.96170.86970.9240490.00000.300435.00000.60003.77003.95800.00000.00000.82460.78003.80174.11360.26000.00000.89870.65003.76664.11360.28000.00000.90030.77783.79914.11360.25930.00000.8997
train0.06610.97060.88280.94124088.00000.2765293.00000.60003.79334.05640.00000.79540.80003.81014.05640.08000.84070.78003.81454.10790.10000.86310.72373.78384.11450.21690.8934
validation0.08540.96160.87160.9241466.00000.2492178.00000.80003.77863.88410.00000.00000.82530.76003.77873.93930.18000.00000.88480.65003.75764.00880.24000.00000.89810.75513.77993.93930.18370.00000.8850

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_s5
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  split_dir: avgfp/splits/main
  graph_threshold: 7
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: 5