Target identification plays a critical role in preclinical drug development. The in silico approach has been developed and widely applied to assist medicinal chemists and pharmacologists in drug target identification. There are many target prediction web servers available today that have revealed both advantages and shortcomings in practical applications. Here, we present 3DSTarPred, a web server for three-dimensional (3D) shape similarity-based target prediction of small molecules. A benchmark study showed that 3DSTarPred achieved a target prediction success rate of 76.27%, which was higher than that of existing target prediction web servers. In addition, the performance of 3DSTarPred in the target prediction of diverse substructures/superstructures was also better than that of the existing target prediction web servers. In case studies, 3DSTarPred was used to identify the potential targets of two small molecules, one being kaempferol, a natural lead compound for the treatment of Alzheimer’s disease (AD), and the other being sildenafil, a candidate for drug repurposing in AD. The case studies further demonstrated the reliability and success of 3DSTarPred in practice. The 3DSTarPred server is freely available at http://3dstarpred.pumc.wecomput.com.

中文翻译：

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3DSTarPred：基于 3D 形状相似性生物活性小分子靶点预测的 Web 服务器


靶点识别在临床前药物开发中起着关键作用。计算机模拟方法已被开发并广泛应用，以帮助药物化学家和药理学家进行药物靶标识别。目前有许多目标预测 Web 服务器在实际应用中既揭示了优点，也揭示了缺点。在这里，我们介绍了 3DSTarPred，这是一个用于基于三维 （3D） 形状相似性的小分子目标预测的 Web 服务器。一项基准测试显示，3DSTarPred 实现了 76.27% 的目标预测成功率，高于现有的目标预测 Web 服务器。此外，3DSTarPred 在各种子结构/上部结构的目标预测中的性能也优于现有的目标预测 Web 服务器。在案例研究中，3DSTarPred 用于确定两个小分子的潜在靶点，一个是山奈酚，一种用于治疗阿尔茨海默病 （AD） 的天然先导化合物，另一个是西地那非，一种用于 AD 药物再利用的候选药物。案例研究进一步证明了 3DSTarPred 在实践中的可靠性和成功。3DSTarPred 服务器可在 http://3dstarpred.pumc.wecomput.com 免费获得。