Expediting the discovery of promising photothermal cyanine molecules through a transfer learning approach.

Abstract

Cyanine-based molecules have gained significant attention in photothermal therapy due to their unique fluorescence brightness and tunable spectral properties. However, the development of new photothermal agents is often constrained by the complexity of the chemical landscape and the need for biocompatibility. To address these challenges, we present an innovative transfer learning approach for rapidly identifying promising photothermal agent candidates with excellent photothermal properties, high synthetic feasibility, and superior biocompatibility. Using natural language processing, our pretrained model generated a molecular library based on cyanine scaffolds. The most promising candidates were screened rigorously through a weighted analysis of chemical indicators, such as photothermal performance and synthetic accessibility and biological indicators, including bio-toxicity. From these, three molecules were selected for retrosynthetic analysis. This artificial intelligence-driven approach provides a robust solution to the traditional challenges in photothermal agent design, significantly enhancing their potential applications in cancer bioimaging, mitochondrial phototherapy, and image-guided surgery.