Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties
Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties
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积极探索：同时优化性能的新型分子的逆向设计
DOI：
10.1021/acs.jpca.1c08191
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Savoie, Brett M.
中科院分区：
文献类型：
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作者：
Iovanac, Nicolae C.;MacKnight, Robert;Savoie, Brett M.
将量子化学表征与生成式机器学习模型相结合，有可能加速分子发现。在这种范式中，量子化学作为评估特定分子性质的相对经济有效的预言器，而生成模型提供了一种基于学习到的结构-功能关系的化学空间采样方法。在实际应用中，必须在发现工作流程中对多个可能正交的特性进行串联优化。这带来了额外的困难，与目标的特异性和模型同时协调所有属性的能力有关。在这里，我们展示了一种主动学习方法来提高多目标生成化学模型的性能。我们首先展示了一组在单一属性预测任务上训练的基线模型在生成具有各种属性目标的新化合物（即不存在于训练数据中）方面的有效性，包括内插和外推生成场景。对于难以精确定位的属性范围，使用量子化学对模型提出的新化合物进行表征，并将最接近表达所需属性的新分子反馈到生成模型中进行额外训练。这逐渐提高了生成模型对化学空间目标区域的理解，并将生成的化合物的分布向目标值转移。然后，我们证明了这种主动学习方法在生成具有多种化学约束的化合物方面的有效性，包括垂直电离势、电子亲和和偶极矩目标，并在ωB97X-D3/def2-TZVP水平上验证了结果。该方法不需要修改现有的生成方法，而是利用其固有的生成和预测方面进行自细化，并且可以应用于必须同时优化具有不同程度相关性的任意数量的属性的情况。
Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure–function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with the specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here, we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds (i.e., not present in the training data) with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry and the new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models’ understanding of targeted areas of chemical space and shifts the distribution of the generated compounds toward the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the ωB97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.