Machine learning and AI-based approaches for bioactive ligand discovery and GPCR-ligand recognition

Abstract

In the last decade, machine learning and artificial intelligence applications have received a significant boost in performance and attention in both academic research and industry. The success behind most of the recent state-of-the-art methods can be attributed to the latest developments in deep learning. When applied to various scientific domains that are concerned with the processing of non-tabular data, for example, image or text, deep learning has been shown to outperform not only conventional machine learning but also highly specialized tools developed by domain experts. This review aims to summarize AI-based research for GPCR bioactive ligand discovery with a particular focus on the most recent achievements and research trends. To make this article accessible to a broad audience of computational scientists, we provide instructive explanations of the underlying methodology, including overviews of the most commonly used deep learning architectures and feature representations of molecular data. We highlight the latest AI-based research that has led to the successful discovery of GPCR bioactive ligands. However, an equal focus of this review is on the discussion of machine learning-based technology that has been applied to ligand discovery in general and has the potential to pave the way for successful GPCR bioactive ligand discovery in the future. This review concludes with a brief outlook highlighting the recent research trends in deep learning, such as active learning and semi-supervised learning, which have great potential for advancing bioactive ligand discovery. AbbreviationsAI Artificial intelligence CNN Convolutional neural network DNN Deep neural network ECFP Extended connectivity fingerprint GNN Graph neural network GPCR G protein-coupled receptor GPU Graphics processing unit GRU Gated recurrent unit MLP Multilayer perceptron NN Neural network QSAR Quantitative structure-activity relationship RNN Recurrent neural network

Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence

“…Deep learning for graphical data has become a growing area of interest, with graph convolutional neural networks now being actively applied in computational biology for modeling molecular structures [219]. Popular libraries in this area include the TensorFlow-based Graph Nets [220] library and PyTorch Geometric [221].…”