kermt-infer

To run high-throughput molecular property predictions from SMILES strings, you can use an automated inference pipeline that cleans raw CSV inputs, generates RDKit 2D features, and executes containerized deep learning models on chemical datasets.

Automated SMILES data preparation for molecular docking works by validating input CSV formats, cleaning chemical strings, and generating the required RDKit 2D features needed for deep learning model inference.

Yes, you need a CUDA-capable environment with Docker and the NVIDIA container toolkit to execute high-performance containerized model inference for molecular property prediction.

No, you cannot use a finetuned checkpoint with missing task FFN heads for molecular predictions because automated validation ensures checkpoints have the required task FFN heads before execution proceeds.

The best way to validate input CSV formats for cheminformatics model inference is using an automated validation step that checks checkpoint structures and SMILES data formats before executing containerized workflows.

Your KERMT inference workflow may fail to generate RDKit 2D features if the input SMILES strings are not properly cleaned or if the required Python RDKit dependency is missing from your environment.