configs/budget_efficiency_benchmark.yaml

run:
  name: budget_efficiency_benchmark
  output_dir: results/budget_efficiency_benchmark
  random_seed: 20260416
  batch_size: 64
  allow_resume: true
  max_batches: 500
  budgets: [100, 500, 2500, 5000, 10000]
  time_importance_values: [0.0, 0.5, 1.0]

backend:
  type: rdock
  require_real_backend: true
  n_runs: 1
  mapper_radius: 6.0
  command_timeout_seconds: 240
  parallel_jobs: auto-minus-4
  allow_skip_failed_ligands: true
  max_failed_ligands: 200

encoding:
  fingerprint_radius: 2
  fingerprint_bits: 1024
  generate_3d: false

feature_extraction:
  compute_partial_charges: false
  compute_sasa: false

clustering:
  butina_cutoff: 0.35
  n_hyperclusters: 40

scheduler:
  init_coverage_fraction: 0.35
  conservative_deprioritize: true
  surrogate:
    prefer_xgboost: true
    n_estimators: 180
    min_train_samples: 20
    max_depth_small: 3
    max_depth_large: 6
  model_weight_schedule:
    sample_knots: [20, 50, 100, 200, 500]
    weight_knots: [0.1, 0.3, 0.5, 0.7, 0.9]
    max_weight: 0.9
    min_weight: 0.05
    instability_threshold: 2.0
    instability_decay: 0.25

early_stop:
  min_evaluated: 600
  patience_rounds: 8
  min_model_weight: 0.35
  quantile: 0.15
  uncertainty_weight: 0.2
  min_improvement: 0.02
  exploration_margin: 0.45
  exploitation_margin: 0.1
  reference_evaluations: 10000

policies:
  adaptive_hard_stop: { enabled: true }
  adaptive_soft_stop:
    enabled: true
    soft_recovery_batches: 16
    soft_threshold_relax: 0.35
    soft_model_weight_cap: 0.45
    soft_exploration_boost: 0.25
    max_soft_recoveries: 1
  adaptive_no_stop: { enabled: true }
  adaptive_cluster_bootstrap:
    enabled: true
    cluster_bootstrap_rounds: 24
  adaptive_soft_stop_cluster_floor:
    enabled: true
    cluster_bootstrap_rounds: 20
    min_cluster_coverage: 0.60
    min_hypercluster_coverage: 0.55
    soft_recovery_batches: 20
    soft_threshold_relax: 0.40
    soft_model_weight_cap: 0.40
    soft_exploration_boost: 0.30
    pre_floor_model_weight_cap: 0.35
    max_soft_recoveries: 2

matrix:
  naive_random_seeds: [11, 22, 33]
  cluster_naive_seeds: [101, 202, 303]

disk_guard:
  min_free_gb: 10.0
  projected_output_gb: 3.0
  keep_raw_batches: 6

dataset_A:
  name: dataset_A
  protein_name: EGFR
  target_id: egfr_budget
  docking_reference_pdb: "4WKQ"
  docking_target_path: data/targets/budget_efficiency_benchmark_A/egfr_4wkq.pdb
  reference_id: ref_gefitinib
  pdb_id: "4WKQ"
  ligand_comp_id: "IRE"
  reference_name: Gefitinib
  reference_smiles: "COC1=C(C=C2C(=C1)N=CN=C2NC3=CC(=C(C=C3)F)Cl)OCCCN4CCOCC4"
  benchmark_dataset:
    output_dir: data/ligands/budget_efficiency_benchmark_A
    target_size: 10000
    shuffle_seed: 334455
    reuse_existing: true
    min_similarity_keep: 0.00
    pubchem_max_records: 12000
    pubchem_thresholds: [80]
    chembl_target_id: CHEMBL203
    chembl_max_rows: 0
    allow_generated_fallback: true

dataset_B:
  name: dataset_B
  protein_name: ABL1
  target_id: abl1_budget
  docking_reference_pdb: "1IEP"
  docking_target_path: data/targets/budget_efficiency_benchmark_B/abl1_1iep.pdb
  reference_id: ref_imatinib
  pdb_id: "1IEP"
  ligand_comp_id: "STI"
  reference_name: Imatinib
  reference_smiles: "CC1=C(C=C(C=C1)NC(=O)C2=CC=C(C=C2)CN3CCN(CC3)C)NC4=NC=CC(=N4)C5=CN=CC=C5"
  benchmark_dataset:
    output_dir: data/ligands/budget_efficiency_benchmark_B
    target_size: 10000
    shuffle_seed: 556677
    reuse_existing: true
    min_similarity_keep: 0.00
    pubchem_max_records: 12000
    pubchem_thresholds: [80]
    chembl_target_id: CHEMBL1862
    chembl_max_rows: 0
    allow_generated_fallback: true