Charité - Universitätsmedizin Berlin, Institute of Physiology and Science IT, Corporate Member of Freie Universität Berlin, Berlin Institute of Health, Humboldt-Universität zu Berlin, 10117 Berlin, Germany.
Nucleic Acids Res. 2022 Jul 5;50(W1):W726-W731. doi: 10.1093/nar/gkac297.
Since the last published update in 2014, the SuperPred webserver has been continuously developed to offer state-of-the-art models for drug classification according to ATC classes and target prediction. For the first time, a thoroughly filtered ATC dataset, that is suitable for accurate predictions, is provided along with detailed information on the achieved predictions. This aims to overcome the challenges in comparing different published prediction methods, since performance can vary greatly depending on the training dataset used. Additionally, both ATC and target prediction have been reworked and are now based on machine learning models instead of overall structural similarity, stressing the importance of functional groups for the mechanism of action of small molecule substances. Additionally, the dataset for the target prediction has been extensively filtered and is no longer only based on confirmed binders but also includes non-binding substances to reduce false positives. Using these methods, accuracy for the ATC prediction could be increased by almost 5% to 80.5% compared to the previous version, and additionally the scoring function now offers values which are easily assessable at first glance. SuperPred 3.0 is publicly available without the need for registration at: https://prediction.charite.de/index.php.
自 2014 年最后一次更新以来，SuperPred 网络服务器一直在不断发展，以提供根据 ATC 类别和目标预测进行药物分类的最先进模型。首次提供了一个经过彻底过滤的 ATC 数据集，该数据集适合进行准确预测，并提供了有关所实现预测的详细信息。这旨在克服在比较不同已发布的预测方法时所面临的挑战，因为性能可能因所使用的训练数据集而有很大差异。此外，ATC 和目标预测都经过了重新设计，现在基于机器学习模型，而不是整体结构相似性，强调了功能基团对小分子物质作用机制的重要性。此外，目标预测的数据集已被广泛过滤，不再仅基于已确认的结合物，还包括非结合物，以减少假阳性。使用这些方法，与上一版本相比，ATC 预测的准确性可提高近 5%至 80.5%，此外，评分函数现在提供了易于一眼评估的值。SuperPred 3.0 可在无需注册的情况下在以下网址公开使用：https://prediction.charite.de/index.php。
