Computational discovery of bifunctional organic semiconductors for energy and biosensing

Abstract

The discovery of synthetically accessible organic semiconductors with exceptional performance remains a critical bottleneck in materials science. While these materials offer compelling advantages - structural modularity, mechanical flexibility, and cost-effective solution processing - for applications in photovoltaics and biosensors, identifying candidates that balance high efficiency with practical synthesis presents significant challenges. To address this challenge, we developed a high-throughput screening approach using 17 458 molecules from the PubChemQC B3LYP/6-31G*//PM6 dataset. Our strategy employs a composite metric, PCESAScore = PCE - SAScore, which systematically balances power conversion efficiency (PCE) predictions from the Scharber model against synthetic accessibility scores. This approach successfully identified seven multi-functional candidates that demonstrate both exceptional photovoltaic performance (PCE up to 36.1 %) and strong protein-binding affinity for biosensing applications. Notably, molecule 4550 emerged as the optimal candidate, exhibiting a ligand efficiency of 0.340 kcal/mol/heavy atom with 100 % target promiscuity. Our computational framework integrates machine learning, density functional theory, and molecular docking to bridge the gap between theoretical performance and experimental feasibility. These findings establish a systematic pathway for discovering synthetically compatible organic semiconductors that can simultaneously address energy conversion and molecular recognition challenges.