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.

中文翻译：

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grünifai：连续向量空间中分子的交互式多参数优化。

在药物开发项目中优化小分子是一项众所周知的难题，因为必须同时考虑和平衡多种分子特性。在这项工作中，我们提出了称为grünifai的新颖的交互式计算机模拟化合物优化平台，以支持在多参数目标的约束下下一代化合物的构想。grünifai集成了可调节的计算机模拟模型，化学空间的连续表示，可扩展的粒子群优化算法以及通过提供有关生成的中间结构的反馈来主动引导化合物优化的可能性。