2026/04/17 by Leonard Galustian, Johannes Karwounopoulos, Tori Demuth +6 · 1 voice
Chemistry · Computer Science · Materials Science · #Computational Drug Discovery Methods #Cyclization and Aryne Chemistry #Machine Learning in Materials Science
paper · pdf · doi:10.26434/chemrxiv.15002135/v1
openalex publication_date 2026/04/17 · openalex created_date 2026/04/18 · openalex updated_date 2026/07/14
Transition states (TSs) are first-order saddle points on the potential energy surface, and thus the highestenergy structure on the (multi-step) minimum-energy reaction pathway that corresponds to a chemical reaction, determining the rate at which it proceeds. Locating TSs and thus elucidating reaction mechanisms is a computationally demanding and expertise-driven task. Recent automation strategies leverage heuristic rules or deep learning models to directly arrive at TS geometries from textual SMILES representations. However, standalone TS predictors lack robustness, whereas heuristic-based approaches lack scalability, limiting their applicability to reliable high-throughput reaction discovery under distribution shifts. Here, we present a generative machine learning framework embedded within a fully automated TS search pipeline and demonstrate accelerated high-throughput screening of bio-orthogonal click reactions as a representative case study. The automated framework enables scalable exploration of large reaction spaces and progressively learns, reducing the number of required quantum mechanical evaluations as additional reactions are processed. In our case study, continuous learning decreased the required TS optimization cycles by nearly 50% after processing only 540 reactions. By coupling generative modeling with physics-based validation in a scalable workflow, our work establishes a framework for data-driven reaction mechanism exploration and advances the development of autonomous computational discovery of chemical reactivity.