use crate::{
LaunchType, License, LicenseCategory, ProcessExpense, SpecData, ToolCategory,
tool_definitions::{
Tool,
catalog::{CatalogEntry, DataType, Identity, PrimaryInput},
},
};
pub const ENTRY: CatalogEntry = CatalogEntry {
identity: Identity::Installed(Tool::DlkCat),
categories: &[
ToolCategory::PropertyPrediction,
ToolCategory::Cheminformatics,
],
launch_type: LaunchType::PythonBasedApp,
license_type: LicenseCategory::Permissive,
expense: ProcessExpense::Moderate,
primary_output: None,
primary_inputs: &[PrimaryInput::residues(
"protein_sequence",
&[DataType::AaSequence],
)],
top_choice: false,
spec: SpecData {
summary: "Predict an enzyme turnover number from its sequence and a substrate structure.",
description: "Runs the deep-learning half of the DLKcat toolbox, which pairs a graph neural network over the substrate with a convolutional network over the enzyme sequence to predict kcat. It is trained on wild-type and mutant enzymes across many organisms, and is meant for parameterising models rather than for ranking closely related variants.",
availability: "Installed by setup_system.sh into its own uv environment; the trained model ships with the checkout",
license_details: "MIT (SysBioChalmers), weights included. Commercial use is unrestricted.",
repo_url: Some("https://github.com/SysBioChalmers/DLKcat"),
home_url: None,
docs_url: Some("https://www.nature.com/articles/s41929-022-00798-z"),
input_params_url: None,
examples_url: None,
paper_url: Some("https://www.nature.com/articles/s41929-022-00798-z"),
license: License::Mit,
license_url: None,
tested: false,
},
};