Custom QSAR model integration with REINVENT (Staged Learning) not achieving the goal

[filvaleriia]

Dear REINVENT Team,

I am experiencing difficulties integrating my custom QSAR model with REINVENT in staged learning mode. Despite configuring the scoring function and external process, the generated molecules are not achieving the intended optimization goal of predicted pChEMBL values ≥ 7.

Goal:
To create a model that will generate molecules with predicted pChEMBL values are ≥ 7 using my custom QSAR model.

Details:

• Output format for external QSAR scoring function: {"version": 1, "payload": {"predictions": np.reshape(scores, len(smilies)).tolist()}}
• Issue: Only approximately 8% of the generated molecules comply with the condition of pChEMBL ≥ 7, which is significantly lower than expected.

Question:
Could additional adjustments to staged learning configurations help improve alignment with the optimization goal for pChEMBL values?

Below is the code I used for the staged learning setup:

run_type = "staged_learning"
use_cuda = "cuda:0" 
json_out_config = "_staged_learning.json"
output_file = "cOS_REINVENT_staged_learning.csv" 

[parameters]
model_file = "reinvent.prior"

use_checkpoint = false
summary_csv_prefix = "RL"
agent_file = "TL_reinvent.model" #my own model after transfer_learning
prior_file = "reinvent.prior"

batch_size = 512
unique_molecules = true  
randomize_smiles = true  

[learning_strategy]
type = "dap"
sigma = 128
rate = 0.0001

[[stage]]
chkpt_file = "StL.chkpt"

max_score = 1.0
max_steps = 300


[stage.scoring]
type = "geometric_mean"

[[stage.scoring.component]]
[stage.scoring.component.ExternalProcess]

[[stage.scoring.component.ExternalProcess.endpoint]]
name = "QSAR"

min_value = 7
max_value = 10

params.executable = "/usr/bin/conda"
params.args = "run -n env bash -c 'python prediction.py'"

Thank you for your assistance, and please let me know if additional details are needed.

Best regards,
Valeriia Fil

[halx]

Hi,

welcome to the community and many thanks for your interest in REINVENT!

What I note is that min_value and max_value are not valid parameters. In newer versions of REINVENT that would actually be an error. What you probably meant to do here is to provide a transformation function, e.g.

[[stage.scoring.component.ExternalProcess.endpoint]]
name = "QSAR"

params.executable = "/usr/bin/conda"
params.args = "run -n env bash -c 'python prediction.py'"

# Adjust parameters as needed
transform.type = "sigmoid"
transform.high = 10
transform.low = 7
transform.k = 0.4

A step function would probably be too harsh.

Cheers,
Hannes.

[filvaleriia]

Hi,

Thank you very much for your response and for pointing this out! I see now that min_value and max_value are not valid parameters, and it is necessary to use a transformation function instead.

I was wondering if there is a way to guide the model more effectively to generate compounds with a predicted pChEMBL value > 7 (QSAR raw score) or, alternatively, with a transformed score closer to 1? Currently, the majority of generated molecules have predicted values below 7(QSAR (raw)), which is not aligned with the optimization goal.

Looking forward to your advice!

Best regards,
Valeriia Fil

[halx]

Only the transformed score is used in the RL algorithm and needs to be between 0 and 1. You need to make sure to set the transform correctly such that higher pChEMBL values are close to 1 and lower ones closer to 0. That works usually very effectively.