Aguti Riccardo, Gardini Erika, Bertazzo Martina, Decherchi Sergio, Cavalli Andrea
Computational and Chemical Biology, Fondazione Istituto Italiano di Tecnologia, Genoa, Italy.
Department of Pharmacy and Biotechnology, University of Bologna, Bologna, Italy.
Front Pharmacol. 2022 Jun 29;13:870479. doi: 10.3389/fphar.2022.870479. eCollection 2022.
The choice of target pocket is a key step in a drug discovery campaign. This step can be supported by druggability prediction. In the literature, druggability prediction is often approached as a two-class classification task that distinguishes between druggable and non-druggable (or less druggable) pockets (or voxels). Apart from obvious cases, however, the non-druggable class is conceptually ambiguous. This is because any pocket (or target) is only non-druggable until a drug is found for it. It is therefore more appropriate to adopt a one-class approach, which uses only unambiguous information, namely, druggable pockets. Here, we propose using the import vector domain description (IVDD) algorithm to support this task. IVDD is a one-class probabilistic kernel machine that we previously introduced. To feed the algorithm, we use customized DrugPred descriptors computed NanoShaper. Our results demonstrate the feasibility and effectiveness of the approach. In particular, we can remove or mitigate biases chiefly due to the labeling.
选择目标口袋是药物发现活动中的关键步骤。这一步骤可以通过成药可能性预测来支持。在文献中，成药可能性预测通常被视为一个两类分类任务，用于区分可成药和不可成药（或成药可能性较小）的口袋（或体素）。然而，除了明显的情况外，不可成药类别在概念上是模糊的。这是因为任何口袋（或靶点）在找到针对它的药物之前都只是不可成药的。因此，采用仅使用明确信息（即可成药口袋）的一类方法更为合适。在这里，我们建议使用导入向量域描述（IVDD）算法来支持这项任务。IVDD是我们之前介绍过的一类概率核机器。为了给该算法提供数据，我们使用通过NanoShaper计算的定制DrugPred描述符。我们的结果证明了该方法的可行性和有效性。特别是我们可以消除或减轻主要由于标记导致的偏差。
