A coherent guided generative model for chemically plausible reaction temperature modeling

Accurate prediction and generation of reaction temperature remain open challenges in data-driven chemical synthesis, owing to the continuous nature heterogeneous experimental protocols, and the need for strict physical plausibility. To address this, we introduce the Coherent Guided Generative Model (CGGM), a hybrid deep-learning architecture that integrates variational inference, adversarial learning, and smooth quadratic physical constraints to map reaction temperatures directly from molecular descriptors. Benchmarked on a rigorously curated dataset of 554,974 reactions from the Open Reaction Database (ORD), the CGGM exposes a fundamental precision–generalization trade-off in chemical AI. While a standalone Variational Autoencoder (VAE) minimizes pointwise error (MAE = 0.5105) through conservative mean-regression, the hybrid CGGM achieves superior global density estimation, yielding the highest log-likelihood (−5.2621) and accurately capturing rare cryogenic and high-temperature regimes. Crucially, the CGGM enforces thermodynamic boundaries, restricting out-of-range predictions to just 0.05%, nearly identical to the curated empirical ground truth (0.04%) and significantly outperforming a standalone GAN (0.16%). Latent space topology and manifold traversal analyses confirm a smooth, continuous embedding space with monotonic temperature encoding (ρ > 0.99, p < 0.001), allowing seamless interpolation between cryogenic (≈-78 °C and reflux (>100 °C) domains without structural degradation. Finally, stratified evaluations across Suzuki, Buchwald–Hartwig, and esterification reactions highlight that while classical regressors are optimal for static pointwise lookups, the primary merit of the CGGM lies in its exceptional distributional fidelity, tail-risk modeling, and physically bounded generation. This hybrid framework establishes a robust, uncertainty-aware computational engine for continuous chemical space exploration and closed-loop autonomous synthesis planning.