Advancing drug discovery with deep attention neural networks

• Published in: Drug discovery today 2024-08, Vol.29 (8), p.104067, Article 104067
• Main Author: Lavecchia, Antonio
• Format: Article
• Language: English
• Subjects: artificial intelligence; attention-based networks; de novo drug design; Drug discovery; drug–target interactions; molecular property prediction
• ISSN: 1359-6446; 1878-5832; 1878-5832
• DOI: 10.1016/j.drudis.2024.104067

•Revolutionizing drug discovery with deep attention neural networks.•Exploring attention mechanism and extended architectures like GATs, transformers, BERT, GPTs and BART for complex data.•Uncovering a pivotal role in catalyzing de novo drug design, predicting molecular properties and deciphering elusive drug–target interactions.•Addressing challenges to deepen understanding for pharmaceutical breakthroughs. In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug–target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.