Protein Engineering

avgfp · Graph CNN

reference_avgfp_gcn_randomf0_20261104-141602

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
train0.06540.97050.88530.94162984.00000.29461497.00000.80003.79003.90810.00000.80990.78003.79744.10790.04000.84980.81003.79914.10790.10000.86820.72533.77904.10790.20370.8920
validation0.08350.96240.86960.92611025.00000.2214291.00000.70003.80204.08600.10000.00000.80910.62003.76544.08600.08000.00000.83970.62003.76344.09680.12000.00000.86710.64813.76524.09680.13890.00000.8718
test0.08620.96120.86740.92371035.00000.238214.00000.60003.76314.03110.10000.00000.79910.66003.77504.10680.14000.00000.84980.70003.77884.10680.19000.00000.88150.69443.77814.10680.19440.00000.8840

Loss per epoch

training validation

Config

name: reference_avgfp_gcn_randomf0
task: dms
data:
  dataset: avgfp
  encoding: one_hot,aa_index
  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