mlsanityData to 7 Oct 2026

FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning

arXiv 2410.15010 ↗First posted 19 Oct 2024cs.LG cs.AIcited by 3

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Abstract (arXiv)

Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, the large feasible model space of MRL poses significant challenges to benchmarking, and existing MRL frameworks face limitations in flexibility and scope. To address these challenges, avoid repetitive coding efforts, and ensure fair comparison of models, we introduce FlexMol, a comprehensive toolkit designed to facilitate the construction and evaluation of diverse model architectures across various datasets and performance metrics. FlexMol offers a robust suite of preset model components, including 16 drug encoders, 13 protein sequence encoders, 9 protein structure encoders, and 7 interaction layers. With its easy-to-use API and flexibility, FlexMol supports the dynamic construction of over 70, 000 distinct combinations of model architectures. Additionally, we provide detailed benchmark results and code examples to demonstrate FlexMol's effectiveness in simplifying and standardizing MRL model development and comparison.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 26 of its 71 references that are in the map. First posted on arXiv 19 Oct 2024.

Builds on

26 of 71 references.

References · 26

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14 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024DrugAgent: Automating AI-aided Drug Discovery Programming through LLM Multi-Agent Collaborationcited by 40
  2. 2025ModuLM: Enabling Modular and Multimodal Molecular Relational Learning with Large Language Modelscited by 0
  3. 2026FlexMS: A Unified Public Benchmark for Molecule Tandem Mass Spectrum Predictioncited by 0