Department of Informatics, Ionian University, Corfu, Greece.
Adv Exp Med Biol. 2020;1194:115-125. doi: 10.1007/978-3-030-32622-7_10.
Computer-aided drug design (CADD) is the framework in which the huge amount of data accumulated by high-throughput experimental methods used in drug design is quantitatively studied. Its objectives include pattern recognition, biomarker identification and/or classification, etc. In order to achieve these objectives, machine learning algorithms and especially artificial neural networks (ANNs) have been used over ADMET factor testing and QSAR modeling evaluation. This paper provides an overview of the current trends in CADD-applied ANNs, since their use was re-boosted over a decade ago.
计算机辅助药物设计 (CADD) 是一个框架，其中定量研究了药物设计中高通量实验方法积累的大量数据。其目标包括模式识别、生物标志物识别和/或分类等。为了实现这些目标，机器学习算法，特别是人工神经网络 (ANNs)，已被用于 ADMET 因素测试和 QSAR 建模评估。本文概述了十年来重新兴起的 CADD 应用 ANN 的最新趋势。
