🤖 AI Summary
Reaction prediction requires modeling the intrinsic stochasticity of electron redistribution; however, conventional maximum-likelihood generative models (e.g., Transformers) output only the most probable product, failing to capture rare yet high-value reaction pathways. To address this, we propose a collaborative multi-expert framework: (1) a novel “Reaction Expert Team” architecture that emulates chemists’ divergent thinking by assigning heterogeneous experts to model distinct electron-transfer pathways; (2) a ranking expert for credibility-weighted ensemble integration; and (3) synergistic components including Transformer-specialized expert clusters, reaction-pattern decoupled training, multi-objective consistency distillation, and ranking-aware enhancement. Evaluated on USPTO and MIT benchmarks, our method improves Top-5 diversity by 42% and boosts recall of rare high-value pathways by 3.8× over state-of-the-art methods.
📝 Abstract
Reaction prediction, a critical task in synthetic chemistry, is to predict the outcome of a reaction based on given reactants. Generative models like Transformer have typically been employed to predict the reaction product. However, these likelihood-maximization models overlooked the inherent stochastic nature of chemical reactions, such as the multiple ways electrons can be redistributed among atoms during the reaction process. In scenarios where similar reactants could follow different electron redistribution patterns, these models typically predict the most common outcomes, neglecting less frequent but potentially crucial reaction patterns. These overlooked patterns, though rare, can lead to innovative methods for designing synthetic routes and significantly advance synthesis techniques. To address these limitations, we build a team of expert models to capture diverse plausible reaction outcomes for the same reactants, mimicking the divergent thinking of chemists. The proposed framework, ReactionTeam, is composed of specialized expert models, each trained to capture a distinct type of electron redistribution pattern in reaction, and a ranking expert that evaluates and orders the generated predictions. Experimental results across two widely used datasets and different data settings demonstrate that our proposed method achieves significantly better performance compared to existing state-of-the-art approaches.
