profinity

A program for predicting unknown proton affinities of small molecules and metabolites

https://github.com/mitkeng/profinity

Science Score: 44.0%

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Repository

A program for predicting unknown proton affinities of small molecules and metabolites

Basic Info

Host: GitHub
Owner: mitkeng
License: mit
Default Branch: main
Homepage:
Size: 852 KB

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Created over 1 year ago · Last pushed over 1 year ago

Metadata Files

Readme License Citation

ProFinity: proton affinity prediction

Introduction

ProFinity is a machine learning program for predicting proton affinities (PA) of various small molecules and metabolites. Since PA is a gas phase property, the amount of diverse experimental PA measurements available is limited, thus, ProFinity can be practical for accurately predicting PA values for unknown chemicals within an interpolation limit.

Overall, ProFinity uses two neural network models: 1) a model for predicting PA values and 2) a model for error correction. Ultimately, both models synergistically deliver error attenuated results.

Performance

focus focus

Functionality

The program supports single PA query or batch PA queries. For single query, only a canonical SMILE is required as input string. For batch queries, mirror the below input csv data layout for applicability:

| SMILES|
|-------------|
|Cc1cccc(C)c1|
|... |

Upon completion of a task a tabulated result like the table below is saved in a csv file.

|SMILES| PA (kcal/mol)| |-----|----| |Cc1cccc(C)c1 |189.36111| |...|...|

Requirement

Google account needed to access Google Colab notebook.

Support

To create a small batch query csv input file ad hoc: ```twig import pandas as pd

try: !touch small_batch.csv except: pass

columnnames=["SMILES"] smallbatch=pd.readcsv("smallbatch.csv", names=columnnames) complist = #example: ["C(=O)=O", "O"] smallbatch['SMILES'] = complist smallbatch.tocsv("small_batch.csv", index=False) ```

Limitations

ProFinity currently only supports chemicals containing the following atom types: H, He, B, C, N, O, F, P, S, Cl, Fe, As, Br, I, and Xe. The models have been trained on small molecules and metabolites, therefore, it may significantly underperform when applied to sizeable biomolecules. Also, training did not explicitly account for temperature or electric field effect.

Accessibility

to access the ProFinity platform. focus

Owner

Name: Mithony Keng
Login: mitkeng
Kind: user
Company: Michigan State University

Repositories: 1
Profile: https://github.com/mitkeng

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: ProFinity
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Mithony
    family-names: Keng
    email: kengmith@msu.edu
    affiliation: Michigan State Unviersity
    orcid: 'https://orcid.org/0000-0002-9850-0120'
repository-code: >-
  https://github.com/mitkeng/ProFinity/blob/main/CITATION.cff
url: 'https://github.com/mitkeng/ProFinity'
abstract: 'Program for predicting proton affinity. '
keywords:
  - Gas phase
  - Proton affinity
  - Protonation
license: MIT
version: '1.0'
date-released: '2024-07-15'

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