论文标题
深度学习基于结构的药物发现
Structure-based drug discovery with deep learning
论文作者
论文摘要
深度学习形式的人工智能（AI）承诺对药物发现和化学生物学的承诺，$ \ textit {e.g。} $，以预测蛋白质结构和分子生物活性，计划有机合成和设计分子$ \ textit {de novo} $。尽管药物发现中的大多数深度学习工作都集中在基于配体的方法上，但基于结构的药物发现具有应对未解决的挑战的潜力，例如对未探索的蛋白质靶标的亲和力预测，阐明结合机制以及相关化学动力学的合理化。深度学习方法论的进步以及蛋白质三级结构的准确预测倡导在基于结构的药物发现方法中，$ \ textit {Renaissance} $在AI指导的基于结构的方法中。这篇综述总结了基于结构的深度学习的最突出的算法概念，并预测了未来的机遇，应用和挑战。
Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and chemical biology, $\textit{e.g.}$, to predict protein structure and molecular bioactivity, plan organic synthesis, and design molecules $\textit{de novo}$. While most of the deep learning efforts in drug discovery have focused on ligand-based approaches, structure-based drug discovery has the potential to tackle unsolved challenges, such as affinity prediction for unexplored protein targets, binding-mechanism elucidation, and the rationalization of related chemical kinetic properties. Advances in deep learning methodologies and the availability of accurate predictions for protein tertiary structure advocate for a $\textit{renaissance}$ in structure-based approaches for drug discovery guided by AI. This review summarizes the most prominent algorithmic concepts in structure-based deep learning for drug discovery, and forecasts opportunities, applications, and challenges ahead.
