Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) models predict biological activity and molecular property based on the numerical relationship between chemical structures and activity (property) values. Molecular representations are of importance in QSAR/QSPR analysis. Topological information of molecular structures is usually utilized (2D representations) for this purpose. However, conformational information seems important because molecules are in the three-dimensional space. As a three-dimensional molecular representation applicable to diverse compounds, similarity between a test molecule and a set of reference molecules has been previously proposed. This 3D representation was found to be effective on virtual screening for early enrichment of active compounds. In this study, we introduced the 3D representation into QSAR/QSPR modeling (regression tasks). Furthermore, we investigated relative merits of 3D representations over 2D in terms of the diversity of training data sets. For the prediction task of quantum mechanics-based properties, the 3D representations were superior to 2D. For predicting activity of small molecules against specific biological targets, no consistent trend was observed in the difference of performance using the two types of representations, irrespective of the diversity of training data sets.

定量结构-活性关系 (QSAR) 和定量结构-特性关系 (QSPR) 模型根据化学结构和活性（特性）值之间的数值关系预测生物活性和分子特性。分子表征在 QSAR / QSPR 分析中很重要。为此，通常使用分子结构的拓扑信息（二维表示）。然而，构象信息似乎很重要，因为分子位于三维空间中。作为适用于各种化合物的三维分子表示，先前已提出测试分子与一组参考分子之间的相似性。发现这种 3D 表示对于早期富集活性化合物的虚拟筛选是有效的。在这项研究中，我们将 3D 表示引入 QSAR/QSPR 建模（回归任务）。此外，我们在训练数据集的多样性方面研究了 3D 表示相对于 2D 的相对优点。对于基于量子力学的特性的预测任务，3D 表示优于 2D。对于预测小分子针对特定生物目标的活性，无论训练数据集的多样性如何，使用两种类型的表示的性能差异都没有观察到一致的趋势。