Generative models are undergoing rapid research and application to de novo drug design. To facilitate their application and evaluation, we present MolScore. MolScore already contains many drug-design-relevant scoring functions commonly used in benchmarks such as, molecular similarity, molecular docking, predictive models, synthesizability, and more. In addition, providing performance metrics to evaluate generative model performance based on the chemistry generated. With this unification of functionality, MolScore re-implements commonly used benchmarks in the field (such as GuacaMol, MOSES, and MolOpt). Moreover, new benchmarks can be created trivially. We demonstrate this by testing a chemical language model with reinforcement learning on three new tasks of increasing complexity related to the design of 5-HT2a ligands that utilise either molecular descriptors, 266 pre-trained QSAR models, or dual molecular docking. Lastly, MolScore can be integrated into an existing Python script with just three lines of code. This framework is a step towards unifying generative model application and evaluation as applied to drug design for both practitioners and researchers. The framework can be found on GitHub and downloaded directly from the Python Package Index. Scientific Contribution MolScore is an open-source platform to facilitate generative molecular design and evaluation thereof for application in drug design. This platform takes important steps towards unifying existing benchmarks, providing a platform to share new benchmarks, and improves customisation, flexibility and usability for practitioners over existing solutions.

中文翻译：

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MolScore：从头药物设计中生成模型的评分、评估和基准测试框架


生成模型正在快速研究并应用于从头药物设计。为了方便他们的应用和评估，我们提出了 MolScore。MolScore 已经包含许多基准中常用的与药物设计相关的评分函数，例如分子相似性、分子对接、预测模型、可合成性等。此外，还提供性能指标，以根据生成的化学成分评估生成模型性能。通过这种功能的统一，MolScore 重新实现了该领域常用的基准测试（例如 GuacaMol、MOSES 和 MolOpt）。此外，可以很容易地创建新的基准测试。我们通过测试带有强化学习的化学语言模型来证明这一点，这些任务与利用分子描述符、266 个预训练的 QSAR 模型或双分子对接的 5-HT2a 配体设计相关，这些任务的复杂性不断增加。最后，只需三行代码即可将 MolScore 集成到现有的 Python 脚本中。该框架是将生成模型应用和评估统一为从业者和研究人员的药物设计迈出的一步。该框架可以在 GitHub 上找到，也可以直接从 Python Package Index 下载。Scientific Contribution MolScore 是一个开源平台，用于促进生成式分子设计和评估，以应用于药物设计。该平台在统一现有基准方面迈出了重要一步，提供了一个分享新基准的平台，并提高了从业者对现有解决方案的定制、灵活性和可用性。