- name
- deeppurpose
- description
- Help install, inspect, run, troubleshoot, and adapt the DeepPurpose molecular modeling library for drug-target interaction prediction, compound property prediction, DDI, PPI, protein function prediction, drug repurposing, and virtual screening. Use when the user mentions DeepPurpose,
from DeepPurpose import,DTI,CompoundPred,DDI,PPI,ProteinPred,oneliner,data_process,generate_config, DeepPurpose datasets, encodings, pretrained models, toy data, or demo notebooks. - license
- BSD-3-Clause
DeepPurpose
This skill is adapted from DeepPurpose, copyright (c) 2020 Kexin Huang, Tianfan Fu, licensed under BSD 3-Clause.
Prefer a local DeepPurpose checkout over web summaries. Treat a directory as the repo root when it contains setup.py, requirements.txt, DeepPurpose/, DEMO/, and toy_data/.
Workflow
- Classify the request: environment/install, task pipeline, dataset format,
pretrained model, notebook/demo adaptation, or troubleshooting.
- Read only the relevant reference file:
- installation, dependency sanity, or smoke tests: references/install-and-dependencies.md - task/module selection, encodings, splits, and core APIs: references/tasks-and-entrypoints.md - dataset loaders, custom text formats, pretrained downloads, and result outputs: references/data-and-pretrained.md
- Verify advice against local files before answering. Prefer
README.md,
DeepPurpose/utils.py, DeepPurpose/dataset.py, and the task module the user actually needs.
- Reuse the upstream API shape instead of inventing wrappers. The maintained
paths are: - DTI: DeepPurpose/DTI.py - compound property prediction: DeepPurpose/CompoundPred.py - DDI: DeepPurpose/DDI.py - PPI: DeepPurpose/PPI.py - protein function prediction: DeepPurpose/ProteinPred.py - one-line repurposing and virtual screening: DeepPurpose/oneliner.py
- Prefer the closest notebook in
DEMO/when the user wants an example or a
starting point.
Execution Rules
- Build datasets with
DeepPurpose.datasethelpers or local text files in the
expected format.
- Encode and split with
data_process(...), then build a config with
generate_config(...), then call model_initialize(**config) or model_pretrained(...).
- Keep the task/module aligned:
- DTI uses both drug and target inputs - compound property uses drug-only inputs - DDI uses X_drug plus X_drug_ - PPI uses X_target plus X_target_ - protein function uses target-only inputs
- For repurposing or screening, prefer the existing helpers:
DTI.repurpose, DTI.virtual_screening, CompoundPred.repurpose, and oneliner.repurpose or oneliner.virtual_screening.
- Warn when a step triggers network downloads. Dataset helpers and pretrained
model helpers fetch remote files.
- Distinguish static validation from runtime validation.
DeepPurpose/utils.py
imports heavy dependencies immediately, so a real import needs RDKit, PyTorch, Descriptastorus, and related packages installed first.
Source Files
Use these local files as the primary source of truth when present:
README.mdrequirements.txtenvironment.ymlsetup.pyDeepPurpose/utils.pyDeepPurpose/dataset.pyDeepPurpose/oneliner.pyDeepPurpose/DTI.pyDeepPurpose/CompoundPred.pyDeepPurpose/DDI.pyDeepPurpose/PPI.pyDeepPurpose/ProteinPred.pytoy_data/DEMO/