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Merge pull request #36 from theproteinbot/feat/multi-substrate-ki-separation
docs(readme): add web app section and update citation
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‎README.md‎

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# CatPred: A Comprehensive Framework for Deep Learning In Vitro Enzyme Kinetic Parameters
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[![DOI](https://img.shields.io/badge/DOI-10.1101/2024.03.10.584340-blue)](https://www.nature.com/articles/s41467-025-57215-9)
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[![Web App](https://img.shields.io/badge/Web_App-www.catpred.com-059669)](https://www.catpred.com)
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[![DOI](https://img.shields.io/badge/DOI-10.1038/s41467--025--57215--9-blue)](https://www.nature.com/articles/s41467-025-57215-9)
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[![Colab](https://img.shields.io/badge/GoogleColab-tiny.cc/catpred-red)](https://tiny.cc/catpred)
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[![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)
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---
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## 🚨 Announcements 📢
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- ✅ **14th Mar 2026** - Web app live at [www.catpred.com](https://www.catpred.com) — predict kcat, Km, and Ki directly in your browser!
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- ✅ **28th Feb 2025** - Published in [_Nature Communications_](https://www.nature.com/articles/s41467-025-57215-9)
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- ✅ **27th Dec 2024** - Updated repository with scripts to reproduce results from the manuscript.
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- 🚧 **TODO**
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- Add prediction codes for models using 3D-structural features.
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- Add instructions to install CatPred using a Docker image.
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---
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## 📚 Table of Contents
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- [Web App](#web-app)
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- [Google Colab Interface](#colab-interface)
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- [Local Installation](#local-installation)
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- [System Requirements](#requirements)
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- [Web API (Optional)](#web-api-optional)
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- [Vercel Deployment (Optional)](#vercel-deployment-optional)
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- [Reproducibility](#reproduce)
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- [Fine-Tuning On Custom Data](#-fine-tuning-on-custom-data)
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- [Docker](#-docker)
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- [Acknowledgements](#acknw)
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- [License](#license)
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- [Citations](#citations)
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---
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## 🌐 Web App <a name="web-app"></a>
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CatPred is live at **[www.catpred.com](https://www.catpred.com)** — no installation needed.
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- **Two prediction modes:** Substrate kinetics (kcat/Km) and Inhibition (Ki)
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- **Multi-substrate input** with primary substrate marker
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- **CSV import/export** for batch workflows
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- Powered by a [Modal](https://modal.com) serverless backend
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---
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## 🌐 Google Colab Interface <a name="colab-interface"></a>
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For ease of use without any hardware requirements, a Google Colab interface is available here: [tiny.cc/catpred](http://tiny.cc/catpred).
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wget -c --tries=5 --timeout=30 https://catpred.s3.us-east-1.amazonaws.com/capsule_data_update.tar.gz || \
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wget -c --tries=5 --timeout=30 https://catpred.s3.amazonaws.com/capsule_data_update.tar.gz
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tar -xzf capsule_data_update.tar.gz
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git clone https://github.com/maranasgroup/catpred.git
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git clone https://github.com/maranasgroup/CatPred.git
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cd catpred
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conda env create -f environment.yml
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conda activate catpred
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pip install -e .
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````
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```
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`stride` is Linux-only and optional for the default demos. If needed for your workflow, install it separately on Linux:
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```bash
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conda install -c kimlab stride
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```
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### 🐳 Docker
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A `Dockerfile` is included for containerized usage (PyTorch 2.4, CUDA 12.4, Python 3.12.4 via Mambaforge).
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```bash
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docker build -t catpred .
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docker run --gpus all -it catpred
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```
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### 🔮 Prediction <a name="predict"></a>
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The Jupyter Notebook `batch_demo.ipynb` and the Python script `demo_run.py` show the usage of pre-trained models for prediction.
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### 🌍 Web API (Optional)
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CatPred also provides an optional FastAPI service for prediction workflows.
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CatPred also provides an optional FastAPI service for prediction workflows. The Vue 3 frontend lives in `catpred/web/frontend/` and is served by the API at `/`.
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Install web dependencies:
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```bash
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pip install -e ".[web]"
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```
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Run the API:
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Run the API (serves the built frontend at `/`):
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```bash
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catpred_web --host 0.0.0.0 --port 8000
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```
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To develop the frontend:
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```bash
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cd catpred/web/frontend
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npm install
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npm run dev # Vite dev server with HMR
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npm run build # Production build (vue-tsc + vite)
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```
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Endpoints:
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- `GET /health` — liveness check.
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- `GET /ready` — backend configuration/readiness.
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- `results_dir` is constrained under `CATPRED_API_RESULTS_ROOT`.
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- for local backend (and modal requests with fallback enabled), `checkpoint_dir` must resolve under `CATPRED_API_CHECKPOINT_ROOT`.
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Minimal `POST /predict` example for local inference using `input_rows`:
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Minimal `POST /predict` example for local inference using `input_rows` (human glucokinase + D-glucose):
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```bash
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curl -X POST http://127.0.0.1:8000/predict \
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"parameter": "kcat",
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"checkpoint_dir": "kcat",
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"input_rows": [
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{"SMILES": "CCO", "sequence": "ACDEFGHIK", "pdbpath": "seq_a"},
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{"SMILES": "CCN", "sequence": "LMNPQRSTV", "pdbpath": "seq_b"}
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{
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"SMILES": "C(C1C(C(C(C(O1)O)O)O)O)O",
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"sequence": "MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ",
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"pdbpath": "GCK_HUMAN"
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}
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],
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"results_dir": "batch1",
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"backend": "local"
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If you find the models useful in your research, we ask that you cite the relevant paper:
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```bibtex
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@article {Boorla2024.03.10.584340,
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author = {Veda Sheersh Boorla and Costas D. Maranas},
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title = {CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki},
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elocation-id = {2024.03.10.584340},
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year = {2024},
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doi = {10.1101/2024.03.10.584340},
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publisher = {Cold Spring Harbor Laboratory},
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URL = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340},
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eprint = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340.full.pdf},
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journal = {bioRxiv}
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@article{Boorla2025CatPred,
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author = {Boorla, Veda Sheersh and Maranas, Costas D.},
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title = {CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters},
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journal = {Nature Communications},
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year = {2025},
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volume = {16},
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number = {2072},
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doi = {10.1038/s41467-025-57215-9},
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URL = {https://www.nature.com/articles/s41467-025-57215-9}
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}
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```

‎catpred/web/static/catpred-favicon.svg‎

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‎catpred/web/static/index.html‎

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href="https://fonts.googleapis.com/css2?family=Newsreader:ital,opsz,wght@0,6..72,300;0,6..72,400;1,6..72,300;1,6..72,400&family=Outfit:wght@200;300;400;500&family=IBM+Plex+Mono:wght@300;400&display=swap"
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rel="stylesheet"
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/>
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<link rel="icon" href="/static/catpred-favicon.svg" type="image/svg+xml" />
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<link rel="stylesheet" href="/static/catpred.css?v=20260309a" />
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<link rel="stylesheet" href="/static/catpred.css?v=20260314a" />
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</head>
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<body>
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<div class="texture" aria-hidden="true"></div>
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</header>
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<main>
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<section id="predictor" class="studio container reveal snap-section">
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<section class="hero reveal show">
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<div class="container hero-content">
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<span class="hero-badge">Published in Nature Communications</span>
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<h1>Predict Enzyme<br><em>Kinetic Parameters</em></h1>
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<p class="hero-lead">Deep learning ensemble predictions for kcat, Km, and Ki with built-in uncertainty quantification.</p>
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<a href="#predictor" class="btn btn-dark hero-cta">Start predicting →</a>
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</div>
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</section>
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<section id="predictor" class="studio container reveal show snap-section">
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<div class="studio-head">
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<div class="studio-titleblock">
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<h1>Enzyme Kinetics Prediction</h1>
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<h2>Enzyme Kinetics Prediction</h2>
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<p class="studio-subtitle">kcat, Km, and Ki with uncertainty estimates</p>
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</div>
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<div id="resultCards" class="result-cards">
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<article class="empty-result">
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<p>Run a prediction to see kinetic estimates here.</p>
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<div class="empty-result-icon" aria-hidden="true">
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<svg viewBox="0 0 48 48" width="48" height="48" fill="none" xmlns="http://www.w3.org/2000/svg">
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<circle cx="24" cy="24" r="18" stroke="currentColor" stroke-width="1" stroke-dasharray="4 3" opacity="0.4"/>
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<path d="M17 30c2-4.5 4-8 7-10 3 2 5 5.5 7 10" stroke="currentColor" stroke-width="1.2" stroke-linecap="round" opacity="0.35"/>
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<circle cx="24" cy="18" r="2.5" stroke="currentColor" stroke-width="1" opacity="0.3"/>
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</svg>
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</div>
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<p>Run a prediction to see kinetic<br>estimates here.</p>
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</article>
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</div>
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</div>
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</section>
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<section class="section how-it-works reveal">
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<div class="container">
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<div class="section-head centered">
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<h2>How It Works</h2>
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<p class="muted">Three steps to enzyme kinetics predictions</p>
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</div>
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<div class="step-cards">
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<article class="step-card">
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<span class="step-number">01</span>
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<h3>Enter Data</h3>
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<p>Provide substrate SMILES notation and enzyme amino acid sequence for each entry.</p>
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</article>
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<article class="step-card">
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<span class="step-number">02</span>
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<h3>Run Prediction</h3>
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<p>Our deep learning ensemble processes inputs through multiple model checkpoints.</p>
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</article>
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<article class="step-card">
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<span class="step-number">03</span>
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<h3>Get Results</h3>
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<p>Receive predicted kinetic parameters with uncertainty estimates and detailed metrics.</p>
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</article>
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</div>
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</div>
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</section>
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<section class="section section-alt citation-section reveal">
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<div class="container">
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<div class="section-head centered">
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<h2>Peer-Reviewed Research</h2>
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<p class="muted">CatPred is backed by rigorous scientific methodology</p>
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</div>
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<div class="citation-card">
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<div class="citation-icon" aria-hidden="true">
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<svg viewBox="0 0 24 24" width="24" height="24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round">
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<path d="M4 19.5v-15A2.5 2.5 0 0 1 6.5 2H20v20H6.5a2.5 2.5 0 0 1 0-5H20"/>
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</svg>
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</div>
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<div class="citation-body">
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<h3>CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki</h3>
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<p>Nature Communications, 2025 &mdash; Veda Sheersh Boorla, Costas D. Maranas</p>
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<a class="citation-link" href="https://www.nature.com/articles/s41467-025-57215-9" target="_blank" rel="noreferrer">Read the paper →</a>
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</div>
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</div>
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</div>
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</section>
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</main>
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<footer class="site-footer">
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<div class="container">
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<div class="footer-inner">
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<div class="footer-brand">
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<span class="brand-word">CatPred</span>
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<p>Maranas Group &middot; Penn State University</p>
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</div>
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<nav class="footer-nav" aria-label="Footer navigation">
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<a href="https://www.nature.com/articles/s41467-025-57215-9" target="_blank" rel="noreferrer">Paper</a>
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<a href="https://github.com/maranasgroup/catpred/" target="_blank" rel="noreferrer">GitHub</a>
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</nav>
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</div>
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<p class="footer-copy">&copy; 2025 Maranas Group, Penn State University. All rights reserved.</p>
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</div>
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</footer>
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<a class="mobile-stick" href="#predictor">Run prediction</a>
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<script src="/static/catpred.js?v=20260301a"></script>

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