Sequence-based drug design as a concept in computational drug design

TLDR

Target‑protein–based drug development has succeeded, but the conventional structure‑based drug design pipeline is complex and manually engineered. The study proposes a sequence‑to‑drug concept for computational drug design that uses protein sequence data and end‑to‑end differentiable learning. The authors validate the concept by developing TransformerCPI2.0, interpreting its learned binding knowledge, and applying it to discover new hits and repurpose drugs. The proof‑of‑concept demonstrates that the sequence‑to‑drug concept offers a new perspective and can serve as an alternative to structure‑based drug design, especially for proteins lacking high‑quality 3D structures.

Abstract

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.