Establishing Cell Free Protein Expression as the Preferred Screening Platform for AI Driven Drug Discovery

Abstract

Advances in artificial intelligence and machine learning (AI/ML) have transformed protein design by enabling efficient exploration of vast sequence spaces. However, a major bottleneck is the availability of high-quality experimental data for model training and validation. Cell-free protein synthesis (CFPS) has emerged as a powerful solution by reducing the time required to translate AI generated designs into wet-lab validated data. In this perspective, we assert that CFPS is uniquely positioned to serve as the preferred screening platform for AI-driven protein drug discovery. We review how CFPS integrates naturally into iterative design-build-test-learn (DBTL) workflows, enabling accelerated model refinement through high-throughput, automation-compatible experimentation. We further discuss key considerations for deploying CFPS in AI/ML pipelines, including system selection, data quality requirements, and yield optimization. Importantly, we address common concerns regarding the translatability of CFPS-derived data to cell-based expression systems and outline strategies to map and mitigate differences in folding, post-translational modification, and protein quality. Establishing robust alignment between CFPS and cellular expression will be critical for broad adoption and for realizing the potential of CFPS in AI-driven protein discovery.