table formatting

app.py

@@ -130,20 +130,20 @@ def gradio_interface():
 
         The training set contains the following parameters:
         
-        | Column                       |    Unit   | data type |  Description |
-        |:-----------------------------|-----------|-----------|:-------------|
-        | Molecule Name                |           |    str    | Identifier for the molecule |
-        | Smiles                       |           |    str    | Text representation of the 2D molecular structure |
-        | LogD                         |           |   float   | LogD calculation |
-        | KSol                         |    uM     |   float   | Kinetic Solubility |
-        | MLM CLint                    | mL/min/kg |   float   | Mouse Liver Microsomal |
-        | HLM CLint                    | mL/min/kg |   float   | Human Liver Microsomal |
-        | Caco-2 Permeability Efflux   |           |   float   | Caco-2 Permeability Efflux |
-        | Caco-2 Permeability Papp A>B | 10^-6 cm/s|   float   | Caco-2 Permeability Papp A>B |
-        | MPPB                         | % Unbound |   float   | Mouse Plasma Protein Binding |
-        | MBPB                         | % Unbound |   float   | Mouse Brain Protein Binding |
-        | RLM CLint                    | mL/min/kg |   float   | Rat Liver Microsomal Stability |
-        | MGMB.                        | % Unbound |   float   | Mouse Gastrocnemius Muscle Binding |
+        | Column                       | Unit        | Type      | Description                                   |
+        |:---------------------------- |:----------: |:--------: |:----------------------------------------------|
+        | Molecule Name                |             |    str    | Identifier for the molecule |
+        | Smiles                       |             |    str    | Text representation of the 2D molecular structure |
+        | LogD                         |             |   float   | LogD calculation |
+        | KSol                         |    uM       |   float   | Kinetic Solubility |
+        | MLM CLint                    | mL/min/kg   |   float   | Mouse Liver Microsomal |
+        | HLM CLint                    | mL/min/kg   |   float   | Human Liver Microsomal |
+        | Caco-2 Permeability Efflux   |             |   float   | Caco-2 Permeability Efflux |
+        | Caco-2 Permeability Papp A>B | 10^-6 cm/s  |   float   | Caco-2 Permeability Papp A>B |
+        | MPPB                         | % Unbound   |   float   | Mouse Plasma Protein Binding |
+        | MBPB                         | % Unbound   |   float   | Mouse Brain Protein Binding |
+        | RLM CLint                    | mL/min/kg   |   float   | Rat Liver Microsomal Stability |
+        | MGMB.                        | % Unbound   |   float   | Mouse Gastrocnemius Muscle Binding |
 
         You can download the training data from the [Hugging Face dataset](https://huggingface.co/datasets/OpenADMET/openadmet-challenge-training-set).
         The test set will remained blinded until the challenge submission deadline. You will be tasked with predicting the same set of ADMET endpoints for the test set molecules.
@@ -180,12 +180,40 @@ def gradio_interface():
                         font-weight: 700 !important;
                     }
                 </style>
+                <style>
+                #welcome-md table {
+                    width: 60%;
+                    border-collapse: collapse;
+                    font-size: 0.95rem;     
+                    line-height: 1.2;       /* tighter vertical spacing */
+                }
+                #welcome-md th, #welcome-md td {
+                    padding: 6px 10px;      /* reduce cell padding */
+                    border: 1px solid rgba(0,0,0,0.9);
+                    vertical-align: middle; 
+                }
+                #welcome-md thead th {
+                    background: var(--panel-background-fill, #f5f5f7);
+                    font-weight: 1000;
+                }
+                /* Header shading */
+                #welcome-md thead th:nth-child(2),
+                #welcome-md thead th:nth-child(3) {
+                    text-align: center;
+                }
+                /* Zebra striping */
+                #welcome-md tbody tr:nth-child(odd)  { background: rgba(0,0,0,0.03); }
+                #welcome-md tbody tr:hover          { background: rgba(0,0,0,0.06); }
+                /* Align columns */
+                #welcome-md td:nth-child(2),
+                #welcome-md td:nth-child(3) { text-align: center; white-space: nowrap; }
+                </style>
                 """)
         with gr.Tabs(elem_classes="tab-buttons"):
             lboard_dict = {}
 
             with gr.TabItem("📖 About"):
-                gr.Markdown(welcome_md)
+                gr.Markdown(welcome_md, elem_id="welcome-md")
 
             with gr.TabItem("🚀 Leaderboard", elem_id="lb_subtabs"):
                 gr.Markdown("View the leaderboard for each ADMET endpoint by selecting the appropiate tab.")