Dye-sensitized solar cells (DSSCs) are cost-effective, sustainable, and versatile electricity producers, allowing them to be incorporated into a variety of devices. In this work, we explore the usage of pharmacophore modeling to identify metal-free dyes for DSSCs by means of virtual screening. Pharmacophore models were built based on experimentally tested sensitizers. Virtual screening was performed against a large dataset of commercially available compounds taken from the ZINC15 library and identified multiple virtual hits. A subset of these hits was subjected to DFT and time-dependent-DFT calculations leading to the identification of two compounds, TSC6 and ASC5, with appropriate molecular orbitals energies, favorable localization, and reasonable absorption UV–vis spectra. These results suggest that pharmacophore models, traditionally used in drug discovery and lead optimization, successfully predicted electronic properties, which are in agreement with the theoretical requirements for sensitizers. Such models may therefore find additional usages as modeling tools in materials sciences.

染料敏化太阳能电池 (DSSC) 是具有成本效益、可持续且用途广泛的电力生产商，可以将它们整合到各种设备中。在这项工作中，我们探索了使用药效团模型通过虚拟筛选来识别 DSSC 的无金属染料。基于实验测试的敏化剂建立药效团模型。针对从 ZINC15 库中获取的大量商用化合物数据集进行虚拟筛选，并确定了多个虚拟命中。对这些命中的一个子集进行 DFT 和时间相关的 DFT 计算，从而鉴定出两种化合物TSC6和ASC5，具有适当的分子轨道能量、有利的定位和合理的吸收紫外-可见光谱。这些结果表明，传统上用于药物发现和先导优化的药效团模型成功地预测了电子特性，这与敏化剂的理论要求一致。因此，此类模型可能会在材料科学中作为建模工具找到其他用途。