Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R²) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.

数据库驱动的嫌疑人筛查已被证明是一种有用的工具，可用于检测目标筛查范围之外的新型精神活性物质 (NPS)；然而，缺乏液相色谱 (LC) 系统特有的保留时间会导致大量假阳性。使用 来自在线众包数据库 HighResNPS 的保留时间数据 ( n = 1281)，提出了一种基于单一流线型、定量结构保留关系 (QSRR) 的保留时间预测模型，该模型集成了具有不同洗脱条件的多个液相色谱系统。使用人工神经网络 (ANN)，特别是多层感知器 (MLP)，使用四个分子描述符和分类标签的单热编码进行建模。测试集预测的评估 ( n = 193) 产生的决定系数 (R ² ) 和平均绝对误差 (MAE) 值分别为 0.942 和 0.583 分钟。该模型成功区分了 LC 系统，分别在 ±0.5、±1 和 ±2 分钟内预测了 54%、81% 和 97% 的测试集。此外，对于每个 LC 系统，模型之前未观察到的分析物的保留时间预测在 ±1 分钟内。开发的模型可用于预测每个参与实验室的 LC 系统在 HighResNPS 上的所有分析物的保留时间，以进一步支持可疑物筛查。