论文标题
我们至少应该能够设计得很好的分子
We Should at Least Be Able to Design Molecules That Dock Well
论文作者
论文摘要
具有所需特性的化合物是药物发现过程的关键要素。但是，由于缺乏现实的回顾性基准以及前瞻性验证的巨大成本，衡量该领域的进度一直在挑战。为了缩小这一差距，我们提出了基于对接的基准，这是一种流行的计算方法，用于评估分子与蛋白质的结合。具体而言，目标是生成像受欢迎的对接软件Smina高度评分的药物样分子。我们观察到，当使用现实尺寸的训练集接受训练时，流行的基于图的生成模型无法生成具有高对接得分的分子。这表明了从头制药设计的当前模型的当前化身的局限性。最后，我们基于更简单的评分函数提出了基准的简化版本，并表明经过测试的模型能够部分解决它。我们在https://github.com/cieplinski-tobiasz/smina-docking-benchmark上发布基准作为易于使用的软件包。我们希望我们的基准将成为自动产生有前途的毒品的目标的垫脚石。
Designing compounds with desired properties is a key element of the drug discovery process. However, measuring progress in the field has been challenging due to the lack of realistic retrospective benchmarks, and the large cost of prospective validation. To close this gap, we propose a benchmark based on docking, a popular computational method for assessing molecule binding to a protein. Concretely, the goal is to generate drug-like molecules that are scored highly by SMINA, a popular docking software. We observe that popular graph-based generative models fail to generate molecules with a high docking score when trained using a realistically sized training set. This suggests a limitation of the current incarnation of models for de novo drug design. Finally, we propose a simplified version of the benchmark based on a simpler scoring function, and show that the tested models are able to partially solve it. We release the benchmark as an easy to use package available at https://github.com/cieplinski-tobiasz/smina-docking-benchmark. We hope that our benchmark will serve as a stepping stone towards the goal of automatically generating promising drug candidates.
