Synthetic Molecular Design, Integrated Drug Discovery, Sanofi-Aventis Deutschland GmbH, Industriepark Höchst, D-65926 Frankfurt am Main, Germany.
J Med Chem. 2020 Aug 27;63(16):8809-8823. doi: 10.1021/acs.jmedchem.9b02044. Epub 2020 Mar 20.
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.
人工智能为药物性质预测、化合物设计和逆合成规划提供了有前景的解决方案，有望显著加速具有药理相关性的分子的搜索。在这里，我们研究了基于人工智能的从头设计的各个方面，包括将其集成到实际工作流程中。首先，我们使用不同的化学空间作为强化学习 (RL) 的训练集，并结合不同的奖励函数。使用经过训练的神经网络可以再生不同的具有生物活性的分子。然而，从训练空间中排除包含五元环等结构部分的分子，仍然会产生含有这些部分的结果。此外，我们还研究了不同的 RL 评分函数，并产生了不同的设计组合。总之，这些设计方案中的一些在化学空间上与查询结果非常接近，因此支持了先导化合物的优化，而 3D 形状或 QSAR（定量构效关系）模型通过在更广泛的化学空间区域进行采样，产生了明显不同的方案，从而支持了先导化合物的生成。因此，RL 为不同发现阶段的定制设计方法提供了一个很好的框架。
