Protein Engineering

gb1 · Linear

reference_gb1_lr_contiguousf0_20261104-145256

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
train1.00990.90990.93440.827432275.00000.84921536.00001.00001.63331.93620.00000.89431.00001.63362.09420.12000.93061.00001.63412.09420.19000.94720.99951.39702.45390.76130.9861
validation2.4708-0.06600.0027-0.22445403.00000.10737577.00000.6000-0.37570.59630.00000.00000.27000.5200-1.03950.80680.00000.0000-0.01190.4800-0.98041.09090.01000.00000.01580.4179-0.93271.09090.01000.00000.0404
test3.55030.06910.1140-0.15661866.0000-0.06741270.00000.0000-3.9239-0.17810.00000.0000-0.62810.0200-3.34930.04310.00000.0000-0.47700.0300-3.15800.46130.00000.0000-0.42080.0498-2.94810.47200.00000.0000-0.3545

Loss per epoch

training validation

Config

name: reference_gb1_lr_contiguousf0
task: dms
data:
  dataset: gb1
  encoding: one_hot,aa_index
  split_dir: null
  split_scheme: contiguous
  split_fold: 0
model:
  name: lr
train:
  epochs: 300
  batch_size: 128
  learning_rate: 0.0001
  patience: 15
  min_delta: 1.0e-05
  seed: 7