Life Cycle Thinking in Early Drug Discovery: Reducing Environmental Impact and Animal Testing via Interpretable Machine Learning- and Chemometrics-Based Oral Acute Toxicity Screening

The integration of life cycle thinking into the earliest stages of drug discovery is imperative to mitigate the significant environmental footprint of pharmaceutical research and development. Conventional approaches, which rely heavily on resource-intensive synthesis and animal testing of numerous compounds, are inherently unsustainable. Herein, we demonstrate how the adoption of in silico strategies can serve as a powerful green chemistry lever to address this challenge. We present a high-throughput computational workflow employing interpretable quantitative structure–activity relationship (QSAR), quantitative read-across (q-RASAR), and machine learning (ML) models to accurately predict the acute oral toxicity of six types of drug scaffolds. The State-of-the-Art (SOTA) algorithms including several deep learning (DL) methods were also explored for global modeling of all scaffolds. The developed models achieved robust predictive performance, with external validation coefficients (Rtest2) ranging from 0. 7674 to 0. 8980 across different scaffolds. This framework, rigorously developed and validated on a data set of 1150 compounds, was applied to virtually screen over 23, 000 untested molecules. By enabling the prioritization of low-toxicity leads and the elimination of hazardous candidates prior to synthesis, our approach directly minimizes the demand for chemical reagents, solvents, and energy-intensive laboratory processes, thereby reducing waste generation and carbon emissions at the source. Concurrently, it substantially reduces the reliance on animal testing, aligning with the 3Rs principles. The analysis of structure–toxicity relationships further guides the de novo design of safer chemicals. Notably, a life cycle-oriented estimate indicates that this predictive strategy could potentially yield approximately 238 million in direct R&D cost avoidance and prevent 5000–10, 000 t of CO2-equivalent emissions. This work establishes a paradigm shift toward a more sustainable drug discovery ecosystem, where computational prediction proactively circumvents the environmental and ethical costs embedded in the traditional research life cycle.