FlexMol工具箱让分子关系学习模型构建更灵活高效
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
- 提供70,000种模型组合的动态搭建能力
- 内置16种药物编码器、13种蛋白序列编码器等组件
- 适合药物发现领域研究者快速对比不同模型性能
分子关系学习（MRL）对理解分子对之间的相互作用行为至关重要，是药物研发的关键环节。然而，MRL模型空间庞大，现有框架在灵活性和覆盖范围上存在局限。为解决这些问题，避免重复编码，并确保模型公平比较，我们提出FlexMol——一个全面的工具箱，支持在多种数据集和评估指标下构建与评测多样化模型架构。该工具箱提供16种药物编码器、13种蛋白序列编码器、9种蛋白结构编码器和7种交互层等预置组件，通过简洁API实现超过70,000种模型组合的动态构建。我们还提供了详细的基准测试结果与代码示例，验证了FlexMol在简化与标准化MRL模型开发与比较方面的有效性。
原文摘要 · Abstract (English)
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.
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