BbbpPredictor (predict_bbbp)

Example

Input:

smiles: 'CCNC(=O)/C=C/C1=CC=CC(Br)=C1'

Text Input (used for the run_text function in the Python calling mode):

smiles: 'CCNC(=O)/C=C/C1=CC=CC(Br)=C1'

Output:

"""The probability of the compound to penetrate the blood-brain barrier is 99.90%, which means it's likely to happen.
Note that the result is predicted by a neural network model and may not be accurate. You may use other tools or resources to obtain more reliable results if needed."""

Usage

The tool supports both MCP mode and Python calling mode.

MCP Mode

Configure your MCP client following its instructions with something like:

{
    "command": "/ABSTRACT/PATH/TO/uv",  // Use `which uv` to get its path
    "args": ["--directory", "/ABSTRACT/PATH/TO/ChemMCP", "run", "--tools", "BbbpPredictor"],
    "toolCallTimeoutMillis": 300000,
    "env": {}
}

Python Calling Mode

import os
from chemmcp.tools import BbbpPredictor

# Initialize the tool
tool = BbbpPredictor()

# The tool has two alternative ways to run:
# 1. Run with separate input domains (recommended)
output = tool.run_code(
    smiles='CCNC(=O)/C=C/C1=CC=CC(Br)=C1'
)
# 2. Run with text-only input
output = tool.run_text(
    smiles='CCNC(=O)/C=C/C1=CC=CC(Br)=C1'
)

Each tool in ChemMCP has two ways to run:

• run_code (recommended): The inputs contain one or more domains, each of which can be a str, an int, a float, etc.
• run_text: The inputs are a single string in a specific format. The tool will parse the string to extract the input domains. This is useful in scenarios where an agent framework calls tools only with text input. The output is the same in both cases.

For the input and output domains, please refer to the tool’s signature.

Tool Signature

Input

Used in the MCP mode, as well as the run_code function in the Python calling mode.

Text Input

Used in the run_text function in the Python calling mode.

Output

The output is the same in both input cases.

Envs

No required environment variables for this tool.

Implementation Details

• Implementation Description: Use the Uni-Mol model fine-tuned on SmolInstruct PP-BBBP data to predict the BBBP probability, and use a text template to construct textual output.
• Open-source dependencies (code source or required libraries):
  • Uni-Mol (MIT)
  • Uni-Core (MIT)
• Hosted services and software (required for running the tool): None