A Python library for efficient computation of molecular fingerprints

• URL: http://arxiv.org/abs/2403.19718v1
• Date: Wed, 27 Mar 2024 19:02:09 GMT
• Title: A Python library for efficient computation of molecular fingerprints
• Authors: Michał Szafarczyk, Piotr Ludynia, Przemysław Kukla,
• Abstract summary: We create a Python library that computes molecular fingerprints efficiently and delivers an interface that is comprehensive. The library enables the user to perform computation on large datasets using parallelism. We show that using molecular fingerprints we can achieve results comparable to state-of-the-art ML solutions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning solutions are very popular in the field of chemoinformatics, where they have numerous applications, such as novel drug discovery or molecular property prediction. Molecular fingerprints are algorithms commonly used for vectorizing chemical molecules as a part of preprocessing in this kind of solution. However, despite their popularity, there are no libraries that implement them efficiently for large datasets, utilizing modern, multicore architectures. On top of that, most of them do not provide the user with an intuitive interface, or one that would be compatible with other machine learning tools. In this project, we created a Python library that computes molecular fingerprints efficiently and delivers an interface that is comprehensive and enables the user to easily incorporate the library into their existing machine learning workflow. The library enables the user to perform computation on large datasets using parallelism. Because of that, it is possible to perform such tasks as hyperparameter tuning in a reasonable time. We describe tools used in implementation of the library and asses its time performance on example benchmark datasets. Additionally, we show that using molecular fingerprints we can achieve results comparable to state-of-the-art ML solutions even with very simple models.

Related papers

• Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping [0.0]
  The Deep Fast Machine Learning Utils (DFMLU) library provides tools designed to automate and enhance aspects of machine learning processes. DFMLU offers functionalities that support model development and data handling. This manuscript presents an overview of DFMLU's functionalities, providing Python examples for each tool.
  arXiv  Detail & Related papers  (2024-09-14T21:39:17Z)
• Scikit-fingerprints: easy and efficient computation of molecular fingerprints in Python [0.0]
  skfp is a Python package for computation of molecular fingerprints for applications in chemoinformatics. skfp offers an industry-standard scikit-learn interface, allowing intuitive usage and easy integration with machine learning pipelines. It is also flexible, highly efficient, and fully open source.
  arXiv  Detail & Related papers  (2024-07-18T08:45:14Z)
• Benchmarking Predictive Coding Networks -- Made Simple [48.652114040426625]
  We first propose a library called PCX, whose focus lies on performance and simplicity. We use PCX to implement a large set of benchmarks for the community to use for their experiments.
  arXiv  Detail & Related papers  (2024-07-01T10:33:44Z)
• PARTIME: Scalable and Parallel Processing Over Time with Deep Neural Networks [68.96484488899901]
  We present PARTIME, a library designed to speed up neural networks whenever data is continuously streamed over time. PARTIME starts processing each data sample at the time in which it becomes available from the stream. Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning.
  arXiv  Detail & Related papers  (2022-10-17T14:49:14Z)
• A Library for Representing Python Programs as Graphs for Machine Learning [39.483608364770824]
  We introduce an open source Python library python_graphs that applies static analysis to construct graph representations of Python programs. We present the capabilities and limitations of the library, perform a case study applying the library to millions of competitive programming submissions, and showcase the library's utility for machine learning research.
  arXiv  Detail & Related papers  (2022-08-15T22:36:17Z)
• PyGOD: A Python Library for Graph Outlier Detection [56.33769221859135]
  PyGOD is an open-source library for detecting outliers in graph data. It supports a wide array of leading graph-based methods for outlier detection. PyGOD is released under a BSD 2-Clause license at https://pygod.org and at the Python Package Index (PyPI)
  arXiv  Detail & Related papers  (2022-04-26T06:15:21Z)
• SparseChem: Fast and accurate machine learning model for small molecules [6.88204255655161]
  SparseChem provides fast and accurate machine learning models for biochemical applications. It is possible to train classification, regression and censored regression models, or combination of them from command line. Source code and documentation is freely available under MIT License on GitHub.
  arXiv  Detail & Related papers  (2022-03-09T12:40:35Z)
• Solo-learn: A Library of Self-supervised Methods for Visual Representation Learning [83.02597612195966]
  solo-learn is a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs.
  arXiv  Detail & Related papers  (2021-08-03T22:19:55Z)
• PyGlove: Symbolic Programming for Automated Machine Learning [88.15565138144042]
  We introduce a new way of programming AutoML based on symbolic programming. Under this paradigm, ML programs are mutable, thus can be manipulated easily by another program. We show that PyGlove users can easily convert a static program into a search space, quickly iterate on the search spaces and search algorithms, and craft complex search flows.
  arXiv  Detail & Related papers  (2021-01-21T19:05:44Z)
• Captum: A unified and generic model interpretability library for PyTorch [49.72749684393332]
  We introduce a novel, unified, open-source model interpretability library for PyTorch. The library contains generic implementations of a number of gradient and perturbation-based attribution algorithms. It can be used for both classification and non-classification models.
  arXiv  Detail & Related papers  (2020-09-16T18:57:57Z)
• Kernel methods library for pattern analysis and machine learning in python [0.0]
  The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion. The library provides a number of well-defined classes to make various kernel-based operations efficient.
  arXiv  Detail & Related papers  (2020-05-27T16:44:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.