Biomolecular sequence models are increasingly reused outside the studies in which they were introduced, but public checkpoints rarely preserve the execution context needed to inspect source-defined behavior, adapt models to new assays, compare models under shared task definitions or deploy biological predictions. MultiMolecule is an open-source Python ecosystem that turns heterogeneous RNA, DNA and protein sequence-model releases into complete, source-checked model-family implementations with shared loading, workflow and prediction interfaces. The Resource state reported here includes 53 complete model-family implementations with 112 standardized model checkpoints, together with 16 curated dataset resources released through 39 public dataset repositories and 10 user-facing prediction pipelines. Standardized components are linked to source provenance, conversion or preparation code, source-reference checks, Extended Data summaries and public documentation, allowing users to inspect what was standardized, what behavior was checked and how each component enters training, evaluation, inference or deployment. By shifting reuse from repository-specific checkpoints to executable implementations connected to standardized checkpoints, curated datasets, Runner workflows and biological prediction pipelines, MultiMolecule provides common infrastructure for preserving source-defined model behavior, adapting models to new assays, enabling controlled evaluation and deploying biomolecular predictions.

翻译：生物分子序列模型正越来越多地被用于其原始研究之外，但公共检查点很少保留检查源代码定义行为、将模型适配到新实验、在共享任务定义下比较模型或部署生物学预测所需的执行上下文。MultiMolecule是一个开源Python生态系统，它将异质的RNA、DNA和蛋白质序列模型发布物转化为完整且经过源代码核验的模型族实现，并提供共享的加载、工作流和预测接口。本报告涉及的资源状态包括53个完整的模型族实现（包含112个标准化模型检查点），以及通过39个公共数据集仓库发布的16个精选数据集资源和10个面向用户的预测管道。标准化组件与源代码出处、转换或制备代码、源代码参考核验、扩展数据摘要和公共文档相关联，使用户能够检查哪些内容被标准化、哪些行为被核验，以及每个组件如何进入训练、评估、推理或部署流程。通过将复用从特定仓库的检查点转向与标准化检查点、精选数据集、Runner工作流和生物学预测管道相连的可执行实现，MultiMolecule为保留源代码定义的模型行为、将模型适配到新实验、实现受控评估以及部署生物分子预测提供了通用基础设施。