Jul 2026· Journal of the American Chemical Society· Vol 148, pp. 31593 - 31603· 0 citations· 34 references
Medicine
TL;DR
Compared to conventional expectations, it is found that simpler models with interpretable features can achieve competitive performance under rigorous validation protocols and should become a requirement for future ML studies for reaction-yield prediction.
Abstract
Reaction yield prediction is a longstanding challenge in synthetic chemistry, with broad implications for route planning, scalability, and high-throughput experimentation (HTE). While recent machine learning (ML) approaches have demonstrated promise in modeling reactivity, they often use complex descriptors or deep architectures that are computationally expensive and limit interpretability and scalability. Here, we assess how much information is stored in simpler descriptors and whether model accuracy is improved by increasing the complexity of the descriptors. Using classical ML models trained on descriptors with different complexity levels, we benchmark predictive performance on four publicly available HTE data sets covering three diverse reaction data sets: Buchwald–Hartwig (BH) amination, Suzuki–Miyaura (SM) coupling, and the silicon–amine protocol (SLAP). Our evaluation furthermore discusses (1) generalization via component-wise data splitting, (2) robustness through external validation across data sets, and (3) performance across asymmetric yield distributions characteristic of HTE data. Contrary to conventional expectations, we find that simpler models with interpretable features can achieve competitive performance under rigorous validation protocols. Based on our findings, we formulate good practices for future studies in this area. For example, comparison to low-cost baseline models should become a requirement for future ML studies for reaction-yield prediction.
A framework that systematically standardizes and integrates multiple reaction datasets into a high-quality, unique-structure-per-entity dataset, coupled with active learning to strategically expand chemical space is introduced, establishing a blueprint for robust machine learning in synthetic chemistry.
Paulo Neves, Bo Hao, Santeri Aikonen et al.· Nature Computational Science· 1 citation
The results highlight the promise of CRG-based RxnCLF as a scalable reaction foundation model, with the potential to generalize across broader reaction spaces and support diverse downstream reaction informatics tasks, including regioselectivity prediction, enantioselectivity prediction, and reaction condition optimization.
Yiting Zheng, Cheng Fang, Anthony Donofrio et al.· 0 citations
This tutorial provides a comprehensive, end-to-end workflow from raw data to deployed models,icitly designed for environmental chemists with limited prior experience in ML modeling while also providing practical guidance for other users seeking to strengthen their modeling workflows.
Kai Zhang, Yushu Cheng, Hai-Ping Ai et al.· ACS Environmental Au· 0 citations
Reaction optimization is a very time- and resource-intensive process, as optimal reaction conditions depend highly on specific electronic and steric properties of the substrate identity and require extensive fine-tuning of synthetic conditions to arrive at the highest-yielding conversions. Amide couplings, which comprise nearly 40% of synthetic transformations performed in a medicinal chemistry setting, present a particularly challenging context for predictive modeling given the diversity of coupling agents and reaction parameters that must be matched to substrate reactivity. Amide coupling reaction data is particularly well-suited for machine-learning approaches that predict optimal reaction conditions given a particular substrate feature set. Herein, we report a platform for standardizing and filtering open-source reaction data from the ORD (Open Reaction Database) and using this machine-readable data set of 3800 amide coupling reactions to evaluate 13 machine learning models. These include linear, tree-based, kernel method, instance-based, neural network, and ensemble architectures in the yield prediction and classification of coupling agents in amide coupling reactions. Yield prediction remained a difficult task because of the complexity of our reaction data, achieving R 2 scores of only 0.61. However, the models were largely successful in classifying reactions to their ideal coupling agent category (carbodiimide-based, uronium salts, or phosphonium salts) with ensemble- and kernel-based models achieving up to 87% accuracy. To further validate this approach, we deployed the classification models on literature-reported data not in the ORD database, achieving similarly high predictive performance. Our results demonstrate that kernel methods and ensemble-based architectures perform significantly better than other models such as linear or single-tree. Additionally, molecular environment features, captured by XYZ coordinates, three-dimensional features, and Morgan fingerprints around reactive functional groups, boosted model predictivity more than bulk material properties derived from SMILES such as molecular weight and log P.
Abhinav Sai Chalasani, Sourodeep Deb, Aarav Anand et al.· ACS Omega· 0 citations
ABSTRACT General catalysts are usually identified through broad experimental screening to find structures that perform reliably across many substrates and reaction classes. In secondary amine organocatalysis, historical reporting is strongly skewed toward a small set of standard catalysts, leaving many plausible scaffolds underexplored and difficult to evaluate objectively. Here, we apply a bias‐aware machine learning workflow designed for small, uneven datasets to prioritize candidate general catalysts from limited historical data. Within the iminium‐based reaction space used to construct the curated and virtually balanced dataset, this analysis surfaced several high‐performing candidates, including a rarely studied imidazolidinone bearing a benzyl‐protected indole substituent. Despite minimal precedent, this scaffold performed competitively in experimental benchmarking and external transferability tests. In a retrospective analysis restricted to pre‐2005 examples, the same workflow prioritized catalyst families that later became widely adopted (e.g., diarylprolinol silyl ethers and imidazolidinones) among its top candidates, consistent with earlier prioritization from the literature available at the time. Together, these results show how bias‐aware modeling can highlight overlooked scaffolds and reduce the experimental burden required to identify broadly useful catalysts. Pairing targeted experiments with data‐driven prioritization provides a practical route to expanding the set of reliable secondary‐amine catalysts beyond the structures that dominate current practice.
Jiajing Li, Isaiah O. Betinol, Junshan Lai et al.· Angewandte Chemie· 0 citations
Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies. Notably, it is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines. By synergizing both modalities, superior predictive accuracy compared to traditional methodologies is achieved by the optimal cross-attention framework (Test RMSE = 5.27%). Through mechanistic probing, active, descriptor-guided spatial querying is observed, effectively offloading macroscopic steric identification to the visual pathway. Furthermore, a dynamic chemical hierarchy is learned by the network to heavily prioritize critical steric bottlenecks, such as the aryl halide. Concurrently, residual skip connections are utilized to protect non-spatial electronic parameters from destructive attenuation during fusion. Collectively, a scalable and highly interpretable blueprint is provided for augmenting physical chemistry with deep visual learning.
Qiwei Han, Chi Zhou· 0 citations
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