# Drug Design Filters

Most common are drug filters in reducing a dataset of large SMILES down. These filters can be cumbersome to find, compare, and code. So we extended `GlobalChemExtensions` to cover that.  We use `RDKit` to fetch the parameters of each molecule. 

**Imports**

```
from global_chem_extensions import GlobalChemExtensions

cheminformatics = GlobalChemExtensions().cheminformatics()
```

**Filter a Drug by one of the Filters**

{% tabs %}
{% tab title="Code" %}

```
gc.build_global_chem_network()
smiles_list = list(gc.get_node_smiles('emerging_perfluoroalkyls').values())

filtered_smiles = cheminformatics.filter_smiles_by_criteria(
    smiles_list,
    lipinski_rule_of_5=True,
    ghose=False,
    veber=False,
    rule_of_3=False,
    reos=False,
    drug_like=False,
    pass_all_filters=False
)

print (filtered_smiles)
```

{% endtab %}

{% tab title="Output" %}

```
[
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'FC(F)(F)C1(F)OC1(F)F', 
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)F',
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F'
]

```

{% endtab %}
{% endtabs %}

**Algorithm** 

**Lipinski**

{% tabs %}
{% tab title="Rules" %}

```
Lipinski:
    Moleculer Weight <= 500
    LogP <= 5
    H-Bond Donor Count <= 5
    H-Bond Acceptor Count <= 10
```

{% endtab %}

{% tab title="Implementation" %}

```
if molecular_weight <= 500 and logp <= 5 and h_bond_donor <= 5 and h_bond_acceptors <= 10and rotatable_bonds <= 5:
    lipinski = True
    results["Lipinski Rule of 5"] += 1
```

{% endtab %}
{% endtabs %}

**Ghose**

{% tabs %}
{% tab title="Rules" %}

```
Ghose:
    Molecular weight between 160 and 480
    LogP between -0.4 and +5.6
    Atom count between 20 and 70
    Molar refractivity between 40 and 130
```

{% endtab %}

{% tab title="Implementation" %}

```
if molecular_weight >= 160 and molecular_weight <= 480 and logp >= -0.4 and logp <= 5.6 and number_of_atoms >= 20 and number_of_atoms <= 70 and molar_refractivity >= 40 and molar_refractivity <= 130:
    ghose_filter = True
    results["Ghose Filter"] += 1
```

{% endtab %}
{% endtabs %}

**Veber**

{% tabs %}
{% tab title="Rules" %}

```
Veber:
    Rotatable bonds <= 10
    Topological polar surface area <= 140
```

{% endtab %}

{% tab title="Implementation" %}

```
if rotatable_bonds <= 10 and topological_surface_area_mapping <= 140:
    veber_filter = True
    results["Veber Filter"] += 1
```

{% endtab %}
{% endtabs %}

**Rule of 3**

{% tabs %}
{% tab title="Rules" %}

```
Rule of 3:
    Molecular weight <= 300
    LogP <= 3
    H-bond donor <= 3
    H-bond acceptor count <= 3
    Rotatable bond count <= 3
```

{% endtab %}

{% tab title="Implementation" %}

```
if molecular_weight <= 300 and logp <= 3 and h_bond_donor <= 3 and h_bond_acceptors <= 3 and rotatable_bonds <= 3:
    rule_of_3 = True
    results["Rule of 3 Filter"] += 1
```

{% endtab %}
{% endtabs %}

**Drug-Like (QED)**

{% tabs %}
{% tab title="Rules" %}

```
mass < 400
ring count > 0
rotatable bond count < 5
h-bond donor count <= 5
h-bond acceptor count <= 10
logP < 5
```

{% endtab %}

{% tab title="Implementation" %}

```
if molecular_weight < 400 and num_of_rings > 0 and rotatable_bonds < 5 and h_bond_donor <= 5 and h_bond_acceptors <= 10 and logp < 5:
    drug_like_filter = True
    results["Drug-like Filter"] += 1
```

{% endtab %}
{% endtabs %}

For a more detailed look, please visit this blog for an application on the `WITHDRAWN` database:


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://globalchem.gitbook.io/globalchem-your-chemical-graph-network/cheminformatics/drug-design-filters.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.