Computational discovery of bifunctional organic semiconductors for energy and biosensing

TL;DR

This paper presents a high-throughput computational method combining machine learning, DFT, and molecular docking to identify organic semiconductors that are both high-performing in photovoltaics and effective in biosensing, focusing on synthetic accessibility.

Contribution

The study introduces a novel composite scoring approach for screening organic semiconductors, successfully identifying multifunctional candidates with high efficiency and biosensing potential.

Findings

Identified seven promising multifunctional organic semiconductors.

Molecule 4550 showed exceptional photovoltaic and biosensing properties.

The framework bridges theoretical predictions with practical synthesis feasibility.

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.