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::LigandMpnn),
categories: &[
ToolCategory::ProteinDesign,
ToolCategory::SequencePrediction,
],
launch_type: LaunchType::PythonBasedApp,
license_type: LicenseCategory::Permissive,
expense: ProcessExpense::Moderate,
primary_output: Some(DataType::AaSequence),
primary_inputs: &[PrimaryInput::new("pdb_path", &[DataType::Pdb])],
top_choice: false,
spec: SpecData {
summary: "Protein sequence prediction, to conform with backbone coordinates. Takes external \
molecules into account; to some degree a superset of ProteinMPNN, but is a different model. \
A useful step after RFDiffusion in a protein design pipeline, and before validation \
with structure prediction.",
description: "A deep learning-based protein sequence design method that explicitly \
models all non-protein components of biomolecular systems. \
LigandMPNN generates not only sequences but also sidechain conformations to allow detailed \
evaluation of binding interactions. Experimental characterization demonstrates that LigandMPNN can \
generate small molecule and DNA-binding proteins with high affinity and specificity. \
It allows explicit modeling of small molecule, nucleotide, metal, and other atomic contexts.",
availability: "Installed by bio_tools with PyTorch, parser/packing dependencies and all official model variants; CPU or CUDA",
license_details: "MIT, weights included. Commercial use is unrestricted.",
repo_url: Some("https://github.com/dauparas/LigandMPNN"),
home_url: None,
docs_url: Some("https://github.com/dauparas/LigandMPNN#readme"),
input_params_url: Some("https://github.com/dauparas/LigandMPNN/blob/main/run.py"),
examples_url: Some("https://github.com/dauparas/LigandMPNN#design-examples"),
paper_url: Some("https://www.biorxiv.org/content/10.1101/2023.12.22.573103v1"),
license: License::Mit,
license_url: None,
tested: true,
},
};