Multi-parameter optimization, the heart of drug design, is still an open challenge. Thus, improved methods for automated compounds design with multiple controlled properties are desired. Here, we present a significant extension to our previously described fragment-based reinforcement learning method (DeepFMPO) for the generation of novel molecules with optimal properties. As before, the generative process outputs optimized molecules similar to the input structures, now with the improved feature of replacing parts of these molecules with fragments of similar 3D-shape and electrostatics. By performing comparisons of 3D-fragments, we can simulate 3D properties while overcoming the notoriously difficult step of accurately describing bioactive conformations. We developed a new python package, ESP-Sim, for the comparison of electrostatic potential and molecular shape, allowing the calculation of state-of-the-art partial charges (e.g., RESP with B3LYP/6-31G**) obtained using the quantum chemistry program Psi4. The new improved generative (DeepFMPO v3D) method is demonstrated with a scaffold-hopping exercise identifying CDK2 bioisosteres. All code is open-source and freely available.

中文翻译：

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在深度生成方法中使用 3D 形状和静电相似性的价值

多参数优化是药物设计的核心，仍然是一个开放的挑战。因此，需要用于具有多个受控特性的自动化化合物设计的改进方法。在这里，我们对之前描述的基于片段的强化学习方法 (DeepFMPO) 进行了重大扩展，以生成具有最佳特性的新型分子。和以前一样，生成过程输出与输入结构相似的优化分子，现在具有改进的功能，即用类似 3D 形状和静电的片段替换这些分子的部分。通过对 3D 片段进行比较，我们可以模拟 3D 特性，同时克服众所周知的准确描述生物活性构象的困难步骤。我们开发了一个新的 python 包，ESP-Sim，用于比较静电势和分子形状，允许计算使用量子化学程序 Psi4 获得的最先进的部分电荷（例如，带有 B3LYP/6-31G** 的 RESP）。新的改进生成 (DeepFMPO v3D) 方法通过识别 CDK2 生物等排体的支架跳跃练习进行了演示。所有代码都是开源的，可以免费获得。