checkpoints/backbone.yml

train:
    home_dir: ''
    seed: 0
    checkpoint: ['', 0]
    batch_size: 32
    max_epochs: 10000
    eval_freq: 7200  # seconds
    checkpoint_freq: 50
    checkpoints: []
    lr:  0.0001
    warmup_steps: 1000
    decay_steps: 2_000_000
    clip_grad_norm: True
    grad_clip_val: 1.0
    weight_decay: 0.0
    n_eval_samples: 8
    sample_length_range: [50, 512]
    sc_num_seqs: 4
    eval_loss_t: [0.1, 0.3, 0.5, 0.7, 0.9]
    self_cond_train_prob: 0.9
    subsample_eval_set: 0.05
    crop_conditional: False

data:
    pdb_path: 'datasets/ingraham_cath_dataset'
    fixed_size: 384
    n_aatype_tokens: 21
    se3_data_augment: True
    sigma_data: 10.0

diffusion:
    training:
        function: 'lognormal'
        psigma_mean: -1.2
        psigma_std: 1.2
    sampling:
        function: 'uniform'
        s_min: 0.001
        s_max: 80

model:
    task: 'backbone'  # 'backbone', 'allatom', 'seqdes', 'codesign'
    pretrained_modules: []  # 'struct_model', 'mpnn_model'
    struct_model_checkpoint: ''
    mpnn_model_checkpoint: ''
    crop_conditional: False
    dummy_fill_masked_atoms: False
    struct_model:
        arch: 'uvit'
        n_atoms: 37  # keep same shapes, just zero out sidechains
        n_channel: 256
        noise_cond_mult: 4
        uvit:
            patch_size: 1
            n_layers: 6
            n_heads: 8
            dim_head: 32
            n_filt_per_layer: []
            n_blocks_per_layer: 2
            cat_pwd_to_conv: False
            conv_skip_connection: False
            position_embedding_type: 'absolute_residx'
    mpnn_model:
        use_self_conditioning: True
        label_smoothing: 0.1
        n_channel: 128
        n_layers: 3
        n_neighbors: 32
        noise_cond_mult: 4