An issue with deep-TICA generating the dataset (COLVAR produced by metad).

[Jinmei Pan]

Dear Plumed users, 

I am conducting research on ligand binding systems using metadynamics (non-Well-tempered).I have successfully obtained dissociation trajectories and generated a COLVAR file for training the model. Following the deep-TICA method outlined in the paper "A unified framework for machine learning collective variables for enhanced sampling simulations: mlcolvar," I used the following code to create a dataset:

dataset = create_timelagged_dataset(X, t=t, lag_time=lag_time, logweights=logweight)

The resulting dataset is as follows:

DictDataset("data": [0], "data_lag": [0], "weights": [0], "weights_lag": [0])

We have a question: Is deep-TICA only suitable for handling the .bias in the COLVAR files of OPES (to convert biased data into unbiased)? Can deep-TICA be used for handling the .bias in COLVAR files generated by WT-metad or metad? I have attached the plumed.dat, COLVAR, descriptors.dat, and the deep-TICA training code for reference.

Can someone help me troubleshoot this problem or offer any advice? Thanks a lot! 

jinmei

[Luigi Bonati]

Dear Jinmei,
the TICA method has also been used with well-tempered methadynamics, see J. McCarty and M. Parrinello J. Chem. Phys. 147, 204109 (2017). In this case, the reweighting factor c(t) should also be taken into account (see Eq. 11 in the manuscript), and the .rbias column be used, similar to FES reweighting.

Nonetheless, my advice is to use OPES since it converges faster to a quasi-static bias, which is an important condition for trying to extract information about slow modes (in fact, I doubt that much information can be recovered from a plain metadynamics simulation)

Best, 

Luigi

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Luigi Bonati
Postdoctoral Researcher
Atomistic Simulations
Italian Institute of Technology, Genova, Italy
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[Jinmei Pan]

Dear Luigi,

Thank you for the valuable advice you provided earlier. We have successfully trained a deep-TICA model using the .rbias, which is suitable for our protein-ligand system. We selected 4536 descriptors, representing the interatomic distances between heavy atoms. However, during simulations with these 4536 descriptors, we observed lower computational efficiency. Similar to the Chignolin Folding case you mentioned in your article "Deep learning the slow modes for rare events sampling," we read your suggestions on reducing the descriptor set.

In your article, you mentioned reducing the number of descriptors to 210 by selecting the most relevant ones through sensitivity analysis of the primary CVs. We referred to your article and code https://colab.research.google.com/drive/1dG0ohT75R-UZAFMf_cbYPNQwBaOsVaAA#scrollTo=05ARhiNhSI_D and encountered some issues during testing. We hope you can provide assistance:

1. We faced issues in the variance calculation part and are unsure if the script is suitable for deep-TICA data. How should we modify it to adapt to deep-TICA data?

# compute std to correct for different magnitudes standardize_inputs = True #@param {type:"boolean"} if multiply_by_stddev: if standardize_inputs: dist2 = (dist - Mean) / Range else: dist2 = dist in_std = np.std(dist2, axis=0)

2. We encountered problems in the weight summation part of the function, specifically, we found that

model.nn[0].weight[:,i].abs().sum().item() throws an error: "TypeError: 'FeedForward' object is not subscriptable." Could you guide us on how to resolve this issue?

3. Could you share the code mentioned in the Chignolin Folding case in the article "Deep learning the slow modes for rare events sampling" applicable to reducing the descriptor set for deep-TICA data?

We appreciate your assistance and look forward to your guidance. Meanwhile, we are sharing our test code with you for a better understanding of our issues.I have attached the plumed.dat, COLVAR, descriptors.dat, and the deep-TICA training code for reference.

Thank you for your time and support. We eagerly await your response.

Best regards,

Jinmei