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ChemicalX: A Deep Learning Library for Drug Pair Scoring

Authors:

Benedek Rozemberczki,

Charles Tapley Hoyt,

Piotr Grabowski,

Andriy Nikolov,

Sebastian Nilsson,

Michael Ughetto,

Benjamin M. GyoriAuthors Info & Claims

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Pages 3819 - 3828

https://doi.org/10.1145/3534678.3539023

Published: 14 August 2022 Publication History

Abstract

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners in a streamlined framework. The design of ChemicalX reuses existing high level model training utilities, geometric deep learning, and deep chemistry layers from the PyTorch ecosystem. Our system provides neural network layers, custom pair scoring architectures, data loaders, and batch iterators for end users. We showcase these features with example code snippets and case studies to highlight the characteristics of ChemicalX. A range of experiments on real world drug-drug interaction, polypharmacy side effect, and combination synergy prediction tasks demonstrate that the models available in ChemicalX are effective at solving the pair scoring task. Finally, we show that ChemicalX could be used to train and score machine learning models on large drug pair datasets with hundreds of thousands of compounds on commodity hardware.

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Index Terms

ChemicalX: A Deep Learning Library for Drug Pair Scoring
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 2022

5033 pages

ISBN:9781450393850

DOI:10.1145/3534678

General Chairs:
Aidong Zhang
University of Virginia
,
Huzefa Rangwala
Amazon/George Mason University

Copyright © 2022 ACM.

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Published: 14 August 2022

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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 14 - 18, 2022

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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https://doi.org/10.1109/IJCNN60899.2024.10649939
Barrett JOskoui SRussell SBorens A(2023)Digital Research Environment(DRE)-enabled Artificial Intelligence (AI) to facilitate early stage drug developmentFrontiers in Pharmacology10.3389/fphar.2023.111535614Online publication date: 24-Mar-2023
https://doi.org/10.3389/fphar.2023.1115356
Zhang HMa XLin Z(2023)SAE-SV: A Stacked-AutoEncoder and Soft Voting Joint Approach Based on Small Dataset with High Dimensions for Inhibitory Potency PredictionProceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science10.1145/3644116.3644315(1170-1175)Online publication date: 20-Oct-2023
https://dl.acm.org/doi/10.1145/3644116.3644315
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Saifi IBhat BHamdani SBhat ULobato-Tapia CMir MDar TGanie S(2023)Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical scienceJournal of Biomolecular Structure and Dynamics10.1080/07391102.2023.223403942:12(6523-6541)Online publication date: 11-Jul-2023
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Wang YZhao YDong YChen HLi JDerr TZhang ARangwala H(2022)Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute LeakageProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539404(1938-1948)Online publication date: 14-Aug-2022
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