Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT.

ABSTRACT: Virtual screening of small molecules against protein targets can accelerate drug discovery and development by predicting drug-target interactions (DTIs). However, structure-based methods like molecular docking are too slow to allow for broad proteome-scale screens, limiting their application in screening for off-target effects or new molecular mechanisms. Recently, vector-based methods using protein language models (PLMs) have emerged as a complementary approach that bypasses explicit 3D structure modeling. Here, we develop SPRINT, a vector-based approach for screening entire chemical libraries against whole proteomes for DTIs and novel mechanisms of action. SPRINT improves on prior work by using a self-attention based architecture and structure-aware PLMs to learn a co-embedding space for

SUBMITTER: McNutt AT 

PROVIDER: S-EPMC11838698 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature