flowdock/configs/model/flowdock_fm.yaml

_target_: flowdock.models.flowdock_fm_module.FlowDockFMLitModule

net:
  _target_: flowdock.models.components.flowdock.FlowDock
  _partial_: true

optimizer:
  _target_: torch.optim.Adam
  _partial_: true
  lr: 2e-4
  weight_decay: 0.0

scheduler:
  _target_: torch.optim.lr_scheduler.CosineAnnealingWarmRestarts
  _partial_: true
  T_0: ${int_divide:${trainer.max_epochs},15}
  T_mult: 2
  eta_min: 1e-8
  verbose: true

# compile model for faster training with pytorch 2.0
compile: false

# model arguments
cfg:
  mol_encoder:
    node_channels: 512
    pair_channels: 64
    n_atom_encodings: 23
    n_bond_encodings: 4
    n_atom_pos_encodings: 6
    n_stereo_encodings: 14
    n_attention_heads: 8
    attention_head_dim: 8
    hidden_dim: 2048
    max_path_integral_length: 6
    n_transformer_stacks: 8
    n_heads: 8
    n_patches: ${data.n_lig_patches}
    checkpoint_file: ${oc.env:PROJECT_ROOT}/checkpoints/neuralplexermodels_downstream_datasets_predictions/models/complex_structure_prediction.ckpt
    megamolbart: null
    from_pretrained: true

  protein_encoder:
    use_esm_embedding: true
    esm_version: esm2_t33_650M_UR50D
    esm_repr_layer: 33
    residue_dim: 512
    plm_embed_dim: 1280
    n_aa_types: 21
    atom_padding_dim: 37
    n_atom_types: 4 # [C, N, O, S]
    n_patches: ${data.n_protein_patches}
    n_attention_heads: 8
    scalar_dim: 16
    point_dim: 4
    pair_dim: 64
    n_heads: 8
    head_dim: 8
    max_residue_degree: 32
    n_encoder_stacks: 2
    from_pretrained: true

  relational_reasoning:
    from_pretrained: true

  contact_predictor:
    n_stacks: 4
    dropout: 0.01
    from_pretrained: true

  score_head:
    fiber_dim: 64
    hidden_dim: 512
    n_stacks: 4
    max_atom_degree: 8
    from_pretrained: true

  confidence:
    enabled: true # whether the confidence prediction head is to be used e.g., during inference
    fiber_dim: ${..score_head.fiber_dim}
    hidden_dim: ${..score_head.hidden_dim}
    n_stacks: ${..score_head.n_stacks}
    from_pretrained: true

  affinity:
    enabled: true # whether the affinity prediction head is to be used e.g., during inference
    fiber_dim: ${..score_head.fiber_dim}
    hidden_dim: ${..score_head.hidden_dim}
    n_stacks: ${..score_head.n_stacks}
    ligand_pooling: sum # NOTE: must be a value in (`sum`, `mean`)
    dropout: 0.01
    from_pretrained: false

  latent_model: default
  prior_type: esmfold # NOTE: must be a value in (`gaussian`, `harmonic`, `esmfold`)

  task:
    pretrained: null
    ligands: true
    epoch_frac: ${data.epoch_frac}
    label_name: null
    sequence_crop_size: 1600
    edge_crop_size: ${data.edge_crop_size} # NOTE: for dynamic batching via `max_n_edges`
    max_masking_rate: 0.0
    n_modes: 8
    dropout: 0.01
    # pretraining: true
    freeze_mol_encoder: true
    freeze_protein_encoder: false
    freeze_relational_reasoning: false
    freeze_contact_predictor: true
    freeze_score_head: false
    freeze_confidence: true
    freeze_affinity: false
    use_template: true
    use_plddt: false
    block_contact_decoding_scheme: "beam"
    frozen_ligand_backbone: false
    frozen_protein_backbone: false
    single_protein_batch: true
    contact_loss_weight: 0.2
    global_score_loss_weight: 0.2
    ligand_score_loss_weight: 0.1
    clash_loss_weight: 10.0
    local_distgeom_loss_weight: 10.0
    drmsd_loss_weight: 2.0
    distogram_loss_weight: 0.05
    plddt_loss_weight: 1.0
    affinity_loss_weight: 0.1
    aux_batch_freq: 10 # NOTE: e.g., `10` means that auxiliary estimation losses will be calculated every 10th batch
    global_max_sigma: 5.0
    internal_max_sigma: 2.0
    detect_covalent: true
    # runtime configs
    float32_matmul_precision: highest
    # sampling configs
    constrained_inpainting: false
    visualize_generated_samples: true
    # testing configs
    loss_mode: auxiliary_estimation # NOTE: must be one of (`structure_prediction`, `auxiliary_estimation`, `auxiliary_estimation_without_structure_prediction`)
    num_steps: 20
    sampler: VDODE # NOTE: must be one of (`ODE`, `VDODE`)
    sampler_eta: 1.0 # NOTE: this corresponds to the variance diminishing factor for the `VDODE` sampler, which offers a trade-off between exploration (1.0) and exploitation (> 1.0)
    start_time: 1.0
    eval_structure_prediction: false # whether to evaluate structure prediction performance (`true`) or instead only binding affinity performance (`false`) when running a test epoch
    # overfitting configs
    overfitting_example_name: ${data.overfitting_example_name}