drug-lead-analysis

Drug Lead Analysis

This skill guides you through a comprehensive analysis of drug candidate molecules using OpenBioMed's molecular analysis tools.

When to Use This Skill

• User asks to analyze a molecule for drug potential
• User provides a molecule name or SMILES and wants an evaluation
• User asks about drug-likeness, ADMET, BBB penetration, or side effects
• User wants to compare multiple molecules for lead optimization

Analysis Workflow

Step 1: Get the Molecule

First, obtain the molecule object:

If user provides a molecule name (e.g., "aspirin", "ibuprofen"):

from open_biomed.tools import TOOLS

tool = TOOLS["molecule_name_request"]
result, message = tool.run(name="aspirin")
molecule = result["molecule"]
print(message)  # Shows retrieved info

If user provides a SMILES string:

from open_biomed.data import Molecule

molecule = Molecule.from_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")

If user provides a SDF file:

molecule = Molecule.from_sdf_file("path/to/molecule.sdf")

Step 2: Calculate Drug-likeness Scores

Run all drug-likeness metrics:

from open_biomed.tools import TOOLS

# QED (Quantitative Estimate of Drug-likeness) - 0 to 1, higher is better
qed_tool = TOOLS["molecule_qed"]
qed_result, qed_msg = qed_tool.run(molecule=molecule)

# SA (Synthetic Accessibility) - 1 to 10, lower is easier to synthesize
sa_tool = TOOLS["molecule_sa"]
sa_result, sa_msg = sa_tool.run(molecule=molecule)

# LogP (lipophilicity) - ideally between -0.4 and 5.6
logp_tool = TOOLS["molecule_logp"]
logp_result, logp_msg = logp_tool.run(molecule=molecule)

# Lipinski's Rule of Five - count violations (0 is ideal)
lipinski_tool = TOOLS["molecule_lipinski"]
lipinski_result, lipinski_msg = lipinski_tool.run(molecule=molecule)

Step 3: Predict ADMET Properties

Use the property prediction models:

# Blood-brain barrier penetration (binary: penetrates or not)
prop_tool = TOOLS["molecule_property_prediction"]
bbb_result, bbb_msg = prop_tool.run(
    molecule=molecule,
    dataset="bbbp",
    model="graphmvp"
)

# Side effects prediction (27 categories from SIDER dataset)
sidefx_result, sidefx_msg = prop_tool.run(
    molecule=molecule,
    dataset="sider",
    model="graphmvp"
)