rdkit/README.md

# Molecular Fingerprinting

*author: shiyu*

<br />

## Desription

Molecular Fingerprinting encodes a Simplified Molecular Input Line Entry Specification (SMILES) as a fingerprint. The fingerprint can represent elements, atom pairs, or functional groups, etc., and are often used for substructure searches and similarity searches in drug discovery. 

This operator uses [RDKit](https://www.rdkit.org/docs/index.html) to generate the molecular fingerprint.



<br />



## Code Example

An example that use the Morgan algorithm to generate a fingerprint of the molecular formula 'Cc1ccc(cc1)S(=O)(=O)N'.

*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
from towhee.dc2 import pipe, ops, DataCollection

p = (
    pipe.input('smiles')
        .map('smiles', 'fingerprint', ops.molecular_fingerprinting.rdkit())
        .output('smiles', 'fingerprint')
)

DataCollection(p('Cc1ccc(cc1)S(=O)(=O)N')).show()
```
<img src="./result2.png" height="75px"/>



<br />



## Factory Constructor

Create the operator via the following factory method:

***molecular_fingerprinting.rdkit( algorithm: str = 'morgan', size: int = 2048)***



**Parameters:**

***algorithm:*** *str*

Which algorithm to use for fingerprinting, including 'morgan', 'daylight', 'ap', 'maccs', defaluts to 'morgan', and there is the [list of available fingerprints](https://www.rdkit.org/docs/GettingStartedInPython.html#list-of-available-fingerprints).



***size:*** *int*

The bit vector size just for morgan and daylight algorithm, defaults to 2048.



<br />



## Interface

An molecular fingerprinting operator takes a SMILES as input.
It uses the RDKit specified by algorithm name to generate a SMILES fingerprint.

**Parameters:**

***smiles:*** *str*

A Simplified Molecular Input Line Entry Specification (SMILES).



**Returns:** *bytes*

The molecular fingerprint.


    # More Resources

    - [DNA Sequence Classification based on Milvus - Zilliz blog](https://zilliz.com/blog/dna-sequence-classification-based-on-milvus): Use Milvus, an open-source vector database, to recognize gene families of DNA sequences. Less space but higher accuracy.
- [Accelerating New Drug Discovery - Zilliz blog](https://zilliz.com/blog/molecular-structure-similarity-with-milvus): How to run molecular structure similarity analysis in Milvus
- [Introduction to Milvus Vector Database - Zilliz blog](https://zilliz.com/learn/introduction-to-milvus-vector-database): Zilliz tells the story about building the world's very first open-source vector database, Milvus. The start, the pivotal, and the future.
- [The 2024 Playbook: Top Use Cases for Vector Search - Zilliz blog](https://zilliz.com/learn/top-use-cases-for-vector-search): An exploration of vector search technologies and their most popular use cases.
- [Emerging Trends in Vector Database Research and Development - Zilliz blog](https://zilliz.com/blog/emerging-trends-in-vector-database-research-and-development): This post discusses the development and anticipated future of vector databases from both technical and practical perspectives, focusing on cost-efficiency and business requirements.