Comments (1)
Hi @paulrbuckley,
Indeed, the flexible_training.py
trains a TITAN model from scratch. Now it depends on how you use the flexible training script. You can either pass the TCR-eptope binding data to it, in that case, you omit the pretraining. Alternatively, you can use the model to pretrain a TITAN model on BindingDB and afterwards you can use the semi_frozen_finetuning.py
to finetune your model on TCR-epitope binding data. As per results in our paper, this should give the best results. Just keep in mind that we did not release the preprocessed BindingDB data for this paper. If you want to re-do this step, I recommend looking at our related paper in the Journal of Chemical Information & Modeling and its codebase: https://github.com/PaccMann/paccmann_kinase_binding_residues
If you follow the Box link there, you can access processed BindingDB data that should be quite straightforward to be fed to the flexible training script.
Depends on your needs, but it might be easier to start from the pretrained model that we provide rather than re-doing the pretraining on BindingDB. Also keep in mind to keep a low LR for finetuning otherwise you might induce catastrophic interference and overfit on your TCR data. Hope this helps!
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Related Issues (14)
- Data unavailable! HOT 1
- input output data size not match HOT 2
- Running/ training on own dataset HOT 4
- The logger give some advice, but I don't understand it. HOT 8
- Ligand with MHC HOT 1
- How do you visualize the epitope peptides based on the attention scores? HOT 1
- Bindingdb dataset HOT 1
- Running HOT 3
- shared dataset cannot be access HOT 3
- What to do when the TCR being targeted for prediction does not have the full length?
- how did you get the tcr_full from VDJdb? HOT 2
- Need help with running semifrozen_finetuning.py HOT 7
- pytoda version is not correct HOT 2
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