Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure–activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.

中文翻译：

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药物化学中的自动化从头设计：生成性神经网络可以学习哪些化学类型？

人工智能为性能预测，化合物设计和逆合成规划提供了有前途的解决方案，这些解决方案有望显着加快对药理相关分子的搜索。在这里，我们研究基于人工智能的从头设计，涉及将其集成到现实工作流程中的各个方面。首先，结合不同的奖励功能，将不同的化学空间用作强化学习（RL）的训练集。利用训练有素的神经元网络，可以再生出不同的生物活性分子。但是，从训练空间中排除具有亚结构（例如五元环）的分子仍会产生包含这些部分的结果。此外，研究了RL中不同的评分功能，并产生了不同的设计作品。综上所述，这些设计建议中的一些在化学空间中接近查询，从而支持铅优化，而3D形状或QSAR（定量结构-活性关系）模型通过对化学空间的更广泛区域进行采样产生了截然不同的建议，从而支持铅代。因此，RL为针对不同的发现阶段量身定制的设计方法提供了一个良好的框架。