Drug development based on target proteins has been a successful approach in recent decades. However, the conventional structure-based drug design (SBDD) pipeline is a complex, human-engineered process with multiple independently optimized steps. Here, we propose a sequence-to-drug concept for computational drug design based on protein sequence information by end-to-end differentiable learning. We validate this concept in three stages. First, we design TransformerCPI2.0 as a core tool for the concept, which demonstrates generalization ability across proteins and compounds. Second, we interpret the binding knowledge that TransformerCPI2.0 learned. Finally, we use TransformerCPI2.0 to discover new hits for challenging drug targets, and identify new target for an existing drug based on an inverse application of the concept. Overall, this proof-of-concept study shows that the sequence-to-drug concept adds a perspective on drug design. It can serve as an alternative method to SBDD, particularly for proteins that do not yet have high-quality 3D structures available.

近几十年来，基于靶蛋白的药物开发已成为一种成功的方法。然而，传统的基于结构的药物设计（SBDD）流程是一个复杂的、人为设计的过程，具有多个独立优化的步骤。在这里，我们通过端到端可微学习提出了基于蛋白质序列信息的计算药物设计的序列到药物概念。我们分三个阶段验证这个概念。首先，我们将 TransformerCPI2.0 设计为该概念的核心工具，它展示了跨蛋白质和化合物的泛化能力。其次，我们解读TransformerCPI2.0学到的绑定知识。最后，我们使用 TransformerCPI2.0 来发现具有挑战性的药物靶点的新靶标，并基于该概念的逆向应用来识别现有药物的新靶点。总体而言，这项概念验证研究表明，序列到药物的概念增加了药物设计的视角。它可以作为 SBDD 的替代方法，特别是对于尚不具有高质量 3D 结构的蛋白质。