Department of Digital Technologies, Bayer AG, Berlin 13353, Germany.
Department of Mathematics and Computer Science, Freie Universität Berlin, Berlin 14195, Germany.
Bioinformatics. 2020 Jul 1;36(13):4093-4094. doi: 10.1093/bioinformatics/btaa271.
Optimizing small molecules in a drug discovery project is a notoriously difficult task as multiple molecular properties have to be considered and balanced at the same time. In this work, we present our novel interactive in silico compound optimization platform termed grünifai to support the ideation of the next generation of compounds under the constraints of a multiparameter objective. grünifai integrates adjustable in silico models, a continuous representation of the chemical space, a scalable particle swarm optimization algorithm and the possibility to actively steer the compound optimization through providing feedback on generated intermediate structures.
Source code and documentation are freely available under an MIT license and are openly available on GitHub (https://github.com/jrwnter/gruenifai). The backend, including the optimization method and distribution on multiple GPU nodes is written in Python 3. The frontend is written in ReactJS.
在药物发现项目中优化小分子是一项极具挑战性的任务，因为需要同时考虑和平衡多个分子特性。在这项工作中，我们提出了我们的新型交互式计算机化合物优化平台，称为 grünifai，以支持在多参数目标的约束下构思下一代化合物。grünifai 集成了可调节的计算机模型、化学空间的连续表示、可扩展的粒子群优化算法以及通过对生成的中间结构提供反馈来主动引导化合物优化的可能性。
源代码和文档可根据 MIT 许可证免费获得，并在 GitHub 上公开提供（https://github.com/jrwnter/gruenifai）。后端包括优化方法和在多个 GPU 节点上的分布，均使用 Python 3 编写。前端使用 ReactJS 编写。
