Nitrification inhibitors are vital for increasing crop yield while mitigating nitrogen emissions from agriculture. However, current inhibitors are limited in effectiveness, harm aquatic ecosystems, and face regulatory challenges, creating an urgent need for novel, sustainable alternatives. The SMART-N project aims to revolutionize the discovery of small-molecule inhibitors through a self-driving, high-throughput screening lab that integrates computational chemistry, artificial intelligence, and robotics. Central to this effort is a digital assistant that facilitates candidate selection by employing a holistic virtual pre-screening pipeline trained on historical data. This pipeline evaluates potential inhibitors in silico, leveraging a deep learning-based scoring algorithm to rank molecules based on predicted bioactivities, physicochemical properties, and (eco)toxicological profiles. Selected candidates will then undergo in vitro validation in a high-throughput, closed-loop optimization process coordinated with the digital assistant to iteratively refine the scoring algorithm and outcomes. The methodology incorporates inhibition effectiveness, scale-up potential, and life-cycle assessments, enabling the design of environmentally friendly inhibitors with reduced experimental burden. By integrating ecological considerations from the earliest design stages, SMART-N accelerates small-molecule discovery while advancing sustainable agricultural practices.