mlsanityData to 14 Oct 2026

ChemicalX: A Deep Learning Library for Drug Pair Scoring

arXiv 2202.05240 ↗First posted 10 Feb 2022cs.LG cs.AIcited by 11

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Abstract (arXiv)

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.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 23 of its 55 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

23 of 55 references.

References · 23

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11 more
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Cited by year

Citing papers per year: 2022: 4; 2023: 4; 2024: 3.

11 papers.

  1. 2022Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakagecited by 61
  2. 2023Shift-Robust Molecular Relational Learning with Causal Substructurecited by 15
  3. 2023SynerGPT: In-Context Learning for Personalized Drug Synergy Prediction and Drug Designcited by 13
  4. 2022Collaboration-Aware Graph Convolutional Network for Recommender Systemscited by 12
  5. 2023A Topological Perspective on Demystifying GNN-Based Link Prediction Performancecited by 11
  6. 2024Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural Networkscited by 5
  7. 2023Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learningcited by 2
  8. 2024Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injectionscited by 2
  9. 2022Distributed representations of graphs for drug pair scoringcited by 1
  10. 2024GPS: Graph Contrastive Learning via Multi-scale Augmented Views from Adversarial Poolingcited by 1
  11. 2022TigerLily: Finding drug interactions in silico with the Graphcited by 0