maranasgroup · Vedasheersh · Mar 15, 2026 · Mar 15, 2026 · Mar 15, 2026 · Mar 15, 2026
diff --git a/README.md b/README.md
@@ -1,23 +1,23 @@
 # CatPred: A Comprehensive Framework for Deep Learning In Vitro Enzyme Kinetic Parameters
 
-[![DOI](https://img.shields.io/badge/DOI-10.1101/2024.03.10.584340-blue)](https://www.nature.com/articles/s41467-025-57215-9)
+[![Web App](https://img.shields.io/badge/Web_App-www.catpred.com-059669)](https://www.catpred.com)
+[![DOI](https://img.shields.io/badge/DOI-10.1038/s41467--025--57215--9-blue)](https://www.nature.com/articles/s41467-025-57215-9)
 [![Colab](https://img.shields.io/badge/GoogleColab-tiny.cc/catpred-red)](https://tiny.cc/catpred)
 [![License](https://img.shields.io/badge/License-MIT-green)](LICENSE)
 
 ---
 
 ## 🚨 Announcements 📢
 
+- ✅ **14th Mar 2026** - Web app live at [www.catpred.com](https://www.catpred.com) — predict kcat, Km, and Ki directly in your browser!
 - ✅ **28th Feb 2025** - Published in [_Nature Communications_](https://www.nature.com/articles/s41467-025-57215-9)
 - ✅ **27th Dec 2024** - Updated repository with scripts to reproduce results from the manuscript.
-- 🚧 **TODO**
-  - Add prediction codes for models using 3D-structural features.
-  - Add instructions to install CatPred using a Docker image.
 
 ---
 
 ## 📚 Table of Contents
 
+- [Web App](#web-app)
 - [Google Colab Interface](#colab-interface)
 - [Local Installation](#local-installation)
    - [System Requirements](#requirements)
@@ -26,12 +26,25 @@
    - [Web API (Optional)](#web-api-optional)
    - [Vercel Deployment (Optional)](#vercel-deployment-optional)
    - [Reproducibility](#reproduce)
+   - [Fine-Tuning On Custom Data](#-fine-tuning-on-custom-data)
+   - [Docker](#-docker)
 - [Acknowledgements](#acknw)
 - [License](#license)
 - [Citations](#citations)
 
 ---
 
+## 🌐 Web App <a name="web-app"></a>
+
+CatPred is live at **[www.catpred.com](https://www.catpred.com)** — no installation needed.
+
+- **Two prediction modes:** Substrate kinetics (kcat/Km) and Inhibition (Ki)
+- **Multi-substrate input** with primary substrate marker
+- **CSV import/export** for batch workflows
+- Powered by a [Modal](https://modal.com) serverless backend
+
+---
+
 ## 🌐 Google Colab Interface <a name="colab-interface"></a>
 
 For ease of use without any hardware requirements, a Google Colab interface is available here: [tiny.cc/catpred](http://tiny.cc/catpred). 
@@ -70,19 +83,28 @@ cd catpred_pipeline
 wget -c --tries=5 --timeout=30 https://catpred.s3.us-east-1.amazonaws.com/capsule_data_update.tar.gz || \
 wget -c --tries=5 --timeout=30 https://catpred.s3.amazonaws.com/capsule_data_update.tar.gz
 tar -xzf capsule_data_update.tar.gz
-git clone https://github.com/maranasgroup/catpred.git
+git clone https://github.com/maranasgroup/CatPred.git
 cd catpred
 conda env create -f environment.yml
 conda activate catpred
 pip install -e .
-````
+```
 
 `stride` is Linux-only and optional for the default demos. If needed for your workflow, install it separately on Linux:
 
 ```bash
 conda install -c kimlab stride
 ```
 
+### 🐳 Docker
+
+A `Dockerfile` is included for containerized usage (PyTorch 2.4, CUDA 12.4, Python 3.12.4 via Mambaforge).
+
+```bash
+docker build -t catpred .
+docker run --gpus all -it catpred
+```
+
 ### 🔮 Prediction <a name="predict"></a>
 
 The Jupyter Notebook `batch_demo.ipynb` and the Python script `demo_run.py` show the usage of pre-trained models for prediction.
@@ -104,20 +126,29 @@ For released benchmark datasets, the number of entries with 3D structure can be
 
 ### 🌍 Web API (Optional)
 
-CatPred also provides an optional FastAPI service for prediction workflows.
+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 `/`.
 
 Install web dependencies:
 
 ```bash
 pip install -e ".[web]"
 ```
 
-Run the API:
+Run the API (serves the built frontend at `/`):
 
 ```bash
 catpred_web --host 0.0.0.0 --port 8000
 ```
 
+To develop the frontend:
+
+```bash
+cd catpred/web/frontend
+npm install
+npm run dev          # Vite dev server with HMR
+npm run build        # Production build (vue-tsc + vite)
+```
+
 Endpoints:
 - `GET /health` — liveness check.
 - `GET /ready` — backend configuration/readiness.
@@ -129,7 +160,7 @@ By default, the API is hardened for service use:
 - `results_dir` is constrained under `CATPRED_API_RESULTS_ROOT`.
 - for local backend (and modal requests with fallback enabled), `checkpoint_dir` must resolve under `CATPRED_API_CHECKPOINT_ROOT`.
 
-Minimal `POST /predict` example for local inference using `input_rows`:
+Minimal `POST /predict` example for local inference using `input_rows` (human glucokinase + D-glucose):
 
 ```bash
 curl -X POST http://127.0.0.1:8000/predict \
@@ -138,8 +169,11 @@ curl -X POST http://127.0.0.1:8000/predict \
     "parameter": "kcat",
     "checkpoint_dir": "kcat",
     "input_rows": [
-      {"SMILES": "CCO", "sequence": "ACDEFGHIK", "pdbpath": "seq_a"},
-      {"SMILES": "CCN", "sequence": "LMNPQRSTV", "pdbpath": "seq_b"}
+      {
+        "SMILES": "C(C1C(C(C(C(O1)O)O)O)O)O",
+        "sequence": "MLDDRARMEAAKKEKVEQILAEFQLQEEDLKKVMRRMQKEMDRGLRLETHEEASVKMLPTYVRSTPEGSEVGDFLSLDLGGTNFRVMLVKVGEGEEGQWSVKTKHQMYSIPEDAMTGTAEMLFDYISECISDFLDKHQMKHKKLPLGFTFSFPVRHEDIDKGILLNWTKGFKASGAEGNNVVGLLRDAIKRRGDFEMDVVAMVNDTVATMISCYYEDHQCEVGMIVGTGCNACYMEEMQNVELVEGDEGRMCVNTEWGAFGDSGELDEFLLEYDRLVDESSANPGQQLYEKLIGGKYMGELVRLVLLRLVDENLLFHGEASEQLRTRGAFETRFVSQVESDTGDRKQIYNILSTLGLRPSTTDCDIVRRACESVSTRAAHMCSAGLAGVINRMRESRSEDVMRITVGVDGSVYKLHPSFKERFHASVRRLTPSCEITFIESEEGSGRGAALVSAVACKKACMLGQ",
+        "pdbpath": "GCK_HUMAN"
+      }
     ],
     "results_dir": "batch1",
     "backend": "local"
@@ -367,15 +401,14 @@ This source code is licensed under the MIT license found in the `LICENSE` file i
 If you find the models useful in your research, we ask that you cite the relevant paper:
 
 ```bibtex
-@article {Boorla2024.03.10.584340,
-	author = {Veda Sheersh Boorla and Costas D. Maranas},
-	title = {CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki},
-	elocation-id = {2024.03.10.584340},
-	year = {2024},
-	doi = {10.1101/2024.03.10.584340},
-	publisher = {Cold Spring Harbor Laboratory},
-	URL = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340},
-	eprint = {https://www.biorxiv.org/content/early/2024/03/26/2024.03.10.584340.full.pdf},
-	journal = {bioRxiv}
+@article{Boorla2025CatPred,
+	author = {Boorla, Veda Sheersh and Maranas, Costas D.},
+	title = {CatPred: a comprehensive framework for deep learning in vitro enzyme kinetic parameters},
+	journal = {Nature Communications},
+	year = {2025},
+	volume = {16},
+	number = {2072},
+	doi = {10.1038/s41467-025-57215-9},
+	URL = {https://www.nature.com/articles/s41467-025-57215-9}
 }
 ```
diff --git a/catpred/web/static/catpred-favicon.svg b/catpred/web/static/catpred-favicon.svg
diff --git a/catpred/web/static/index.html b/catpred/web/static/index.html
@@ -14,8 +14,8 @@
       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"
       rel="stylesheet"
     />
-    <link rel="icon" href="/static/catpred-favicon.svg" type="image/svg+xml" />
-    <link rel="stylesheet" href="/static/catpred.css?v=20260309a" />
+
+    <link rel="stylesheet" href="/static/catpred.css?v=20260314a" />
   </head>
   <body>
     <div class="texture" aria-hidden="true"></div>
@@ -40,10 +40,19 @@
     </header>
 
     <main>
-      <section id="predictor" class="studio container reveal snap-section">
+      <section class="hero reveal show">
+        <div class="container hero-content">
+          <span class="hero-badge">Published in Nature Communications</span>
+          <h1>Predict Enzyme<br><em>Kinetic Parameters</em></h1>
+          <p class="hero-lead">Deep learning ensemble predictions for kcat, Km, and Ki with built-in uncertainty quantification.</p>
+          <a href="#predictor" class="btn btn-dark hero-cta">Start predicting →</a>
+        </div>
+      </section>
+
+      <section id="predictor" class="studio container reveal show snap-section">
         <div class="studio-head">
           <div class="studio-titleblock">
-            <h1>Enzyme Kinetics Prediction</h1>
+            <h2>Enzyme Kinetics Prediction</h2>
             <p class="studio-subtitle">kcat, Km, and Ki with uncertainty estimates</p>
           </div>
 
@@ -107,7 +116,14 @@ <h3>Results</h3>
 
             <div id="resultCards" class="result-cards">
               <article class="empty-result">
-                <p>Run a prediction to see kinetic estimates here.</p>
+                <div class="empty-result-icon" aria-hidden="true">
+                  <svg viewBox="0 0 48 48" width="48" height="48" fill="none" xmlns="http://www.w3.org/2000/svg">
+                    <circle cx="24" cy="24" r="18" stroke="currentColor" stroke-width="1" stroke-dasharray="4 3" opacity="0.4"/>
+                    <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"/>
+                    <circle cx="24" cy="18" r="2.5" stroke="currentColor" stroke-width="1" opacity="0.3"/>
+                  </svg>
+                </div>
+                <p>Run a prediction to see kinetic<br>estimates here.</p>
               </article>
             </div>
 
@@ -121,8 +137,71 @@ <h3>Results</h3>
         </div>
       </section>
 
+      <section class="section how-it-works reveal">
+        <div class="container">
+          <div class="section-head centered">
+            <h2>How It Works</h2>
+            <p class="muted">Three steps to enzyme kinetics predictions</p>
+          </div>
+          <div class="step-cards">
+            <article class="step-card">
+              <span class="step-number">01</span>
+              <h3>Enter Data</h3>
+              <p>Provide substrate SMILES notation and enzyme amino acid sequence for each entry.</p>
+            </article>
+            <article class="step-card">
+              <span class="step-number">02</span>
+              <h3>Run Prediction</h3>
+              <p>Our deep learning ensemble processes inputs through multiple model checkpoints.</p>
+            </article>
+            <article class="step-card">
+              <span class="step-number">03</span>
+              <h3>Get Results</h3>
+              <p>Receive predicted kinetic parameters with uncertainty estimates and detailed metrics.</p>
+            </article>
+          </div>
+        </div>
+      </section>
+
+      <section class="section section-alt citation-section reveal">
+        <div class="container">
+          <div class="section-head centered">
+            <h2>Peer-Reviewed Research</h2>
+            <p class="muted">CatPred is backed by rigorous scientific methodology</p>
+          </div>
+          <div class="citation-card">
+            <div class="citation-icon" aria-hidden="true">
+              <svg viewBox="0 0 24 24" width="24" height="24" fill="none" stroke="currentColor" stroke-width="1.5" stroke-linecap="round" stroke-linejoin="round">
+                <path d="M4 19.5v-15A2.5 2.5 0 0 1 6.5 2H20v20H6.5a2.5 2.5 0 0 1 0-5H20"/>
+              </svg>
+            </div>
+            <div class="citation-body">
+              <h3>CatPred: A comprehensive framework for deep learning in vitro enzyme kinetic parameters kcat, Km and Ki</h3>
+              <p>Nature Communications, 2025 &mdash; Veda Sheersh Boorla, Costas D. Maranas</p>
+              <a class="citation-link" href="https://www.nature.com/articles/s41467-025-57215-9" target="_blank" rel="noreferrer">Read the paper →</a>
+            </div>
+          </div>
+        </div>
+      </section>
+
     </main>
 
+    <footer class="site-footer">
+      <div class="container">
+        <div class="footer-inner">
+          <div class="footer-brand">
+            <span class="brand-word">CatPred</span>
+            <p>Maranas Group &middot; Penn State University</p>
+          </div>
+          <nav class="footer-nav" aria-label="Footer navigation">
+            <a href="https://www.nature.com/articles/s41467-025-57215-9" target="_blank" rel="noreferrer">Paper</a>
+            <a href="https://github.com/maranasgroup/catpred/" target="_blank" rel="noreferrer">GitHub</a>
+          </nav>
+        </div>
+        <p class="footer-copy">&copy; 2025 Maranas Group, Penn State University. All rights reserved.</p>
+      </div>
+    </footer>
+
     <a class="mobile-stick" href="#predictor">Run prediction</a>
 
     <script src="/static/catpred.js?v=20260301a"></script>