Nov 2025· Nature Communications· Vol 16· 0 citations· 61 references
Medicine
TL;DR
The results indicate that this fully automated, open-source system holds potential value for improving the efficiency and sustainability of molecular synthesis, and the integration of organic and enzymatic synthesis enhances molecule construction efficiency.
Abstract
The integration of organic synthesis with enzymatic catalysis offers a promising route toward efficient and sustainable construction of complex molecules. While organic synthesis enables diverse transformations, enzymatic catalysis enhances stereoselectivity under mild conditions, improving cost-effectiveness and environmental impact. However, current enzymatic synthesis planning algorithms face challenges in formulating robust hybrid organic–enzymatic strategies. Key issues include the difficulty in devising hybrid planning approaches and the reliance on template-based enzyme recommendations, which limits their adaptability across diverse scenarios. Here we show ChemEnzyRetroPlanner, an open-source hybrid synthesis planning platform that combines organic and enzymatic strategies with AI-driven decision-making. The platform features advanced computational modules, including hybrid retrosynthesis planning, reaction condition prediction, plausibility evaluation, enzymatic reaction identification, enzyme recommendation, and in silico validation of enzyme active sites. A central innovation is the RetroRollout* search algorithm, which outperforms existing tools in planning synthesis routes for organic compounds and natural products across multiple datasets. ChemEnzyRetroPlanner provides an intuitive graphical interface and programmatic APIs for scalability, while leveraging the chain-of-thought strategy and the Llama3.1 model to autonomously activate hybrid synthesis strategies for diverse scenarios. The results indicate that this fully automated, open-source system holds potential value for improving the efficiency and sustainability of molecular synthesis. The integration of organic and enzymatic synthesis enhances molecule construction efficiency. Here, the authors present ChemEnzyRetroPlanner, an AI-driven platform for automated hybrid synthesis planning, improving synthesis route efficiency and sustainability.
The total synthesis of a complex molecule is among the most demanding intellectual and experimental feats in chemistry: a chemist must plan many steps ahead for how to assemble simple building blocks into an intricate target, devise backup strategies, and anticipate procedural challenges. It is also a profoundly creative activity. For half a century, efforts to automate the retrosynthetic design of natural products and other complex molecules have drawn on catalogued reactions, and the resulting tools now report near-complete success on benchmarks built from that same source. But these tools were shaped to fit benchmarked chemistry, and they falter on many natural products, the frontier of the field, whose densely functionalized, polycyclic architectures demand precisely the inventive chemistry the record contains least. Whether a machine could reasonably design such syntheses like an expert chemist does has remained unclear. Here, we show that SynthEx, an agentic framework built on large language models, plans routes to complex natural products that lie beyond the reach of conventional design algorithms. SynthEx proposes competing strategies, assembles a sequence of routine and key steps into a cohesive route, and critiques and improves its own design; the chemistry it favours is more convergent than existing tools produce, and spans a region of reaction space that catalogue-based tools cannot match. Most notably, in blinded assessments, expert chemists judged its key steps comparable to those of published human syntheses and engaged with them as genuine synthesis plans, a response algorithmic route prediction has not previously accomplished. We release routes to more than a thousand natural products as SynthAtlas, an open, interactive database, and anticipate it will become a shared resource for a collection of complex target molecules that lack existing literature routes.
Daniel P. Armstrong, X. Nguyen, Octavian Susanu et al.· 0 citations
Accurate generation of intricate 3D molecular structures is a fundamental challenge in computational chemistry. Transition states (TS), the transient structures that dictate reaction kinetics, exemplify this challenge, as their calculation remains a major bottleneck in elucidating reaction mechanisms. While generative AI has shown promise in automating TS generation, existing methods are largely confined to simple systems, struggling with the complex structures like those in transition metal catalysis. Here we show a unified framework for general-purpose TS generation, comprising the UniTS-Lib library of 4,391 high-quality structures spanning 42 elements and diverse chemical transformations, coupled with the UniTS-Gen diffusion model that generates 3D TS configurations from 2D reactant graphs using a custom-designed higher-degree equivariant network. Validation demonstrates UniTS-Gen’s superior accuracy and robust generalization to unseen chemical systems. We show that UniTS-Gen provides reliable initial guesses and accelerates discovery by locating kinetically favored conformations. This work provides a scalable and transferable solution for automating mechanistic studies in organic synthesis and beyond.
Li-Cheng Xu, Junyi An, Weiqi Liu et al.· Nature Communications· 0 citations
Biocatalysis, which combines green chemistry and white biotechnology, provides a sustainable alternative to conventional chemical synthesis. Enzymes and microbial systems act as selective, renewable catalysts, enabling complex reactions under mild conditions with minimal waste and high atom economy. In pharmaceuticals and fine chemicals, biocatalysis supports shorter synthetic routes, improved stereoselectivity, and regulatory compliance. Advances in enzyme engineering, such as directed evolution, rational design, and machine learning, have expanded the capabilities and robustness of biocatalysts. Techniques such as enzyme immobilization and whole-cell catalysis enhance stability and cost efficiency. Case studies, including simvastatin and pregabalin synthesis, showcase its practical benefits. The integration of chemoenzymatic strategies merges enzymatic precision with chemical versatility, streamlining synthesis. Aligned with the 12 principles of green chemistry, biocatalysis reduces hazardous reagents, lowers energy use, and enables renewable feedstocks. As regulatory bodies promote greener practices, its industrial adoption grows. Future directions include AI-guided enzyme discovery and continuous flow systems for scalable, eco-friendly production. This review explores the current landscape, key innovations, and future directions of biocatalysis in sustainable chemical manufacturing.
Keywords: Biocatalysis; Green Chemistry, White Biotechnology, Enzyme Engineering, Sustainable Synthesis, Biotransformation, Eco-friendly Catalysis
Sachin K. Bhosale, S. Singh, N. Shinde et al.· Journal of Pharmaceutical Re...· 0 citations
The optimization of chemical reactions, both catalytic and noncatalytic, to simultaneously achieve high selectivity, activity, and stability remains a central challenge in homogeneous catalysis and synthetic organic chemistry. Traditional discovery pipelines rely heavily on labor-intensive experimental screening, while current automated computational workflows are often limited by incomplete end-to-end automation, restricted accessibility beyond expert computational chemists, integration of quantum-mechanical surrogate methods, and reliance on single or approximate performance descriptors. A general-purpose, turnkey software platform that extends accessibility beyond command-line execution and removes the need for programming expertise can empower nonexpert scientists to design molecular catalysts and optimize chemical reactions. Here, we present RxnEnumProfiler, a highly automated, GUI-enabled software platform for mechanism-guided molecular catalyst design and reactivity optimization that integrates quantum mechanical methods, semiempirical tight-binding approaches, and reactive machine-learning interatomic potentials into a unified end-to-end workflow for simultaneous multimetric catalyst scoring and parallel exploration of competing reaction pathways. This platform enables rapid, high-throughput evaluation of arbitrary objective performance descriptors and subsequent ranking of candidate compounds via reaction network enumeration and profiling, substantially expanding the accessible chemical design space and accelerating the discovery of high-performing molecular catalysts and reactions. The generality of this approach is demonstrated across three representative homogeneous catalytic systems─organocatalyzed asymmetric hydrogenation, Pd-catalyzed C-O cross-coupling, and ansa-metallocene-catalyzed isotactic propylene polymerization─as well as a noncatalytic transformation relevant to epoxy curing chemistry.
Pavel A. Dub, Thomas F. Hughes, T. Mustard· Journal of Chemical Informat...· 0 citations
Developing reliable synthesis routes for complex materials remains a major bottleneck in accelerating materials discovery. This study establishes a large language model-based framework for predicting and optimizing synthesis conditions directly from the literature data. Key synthesis information, including target compounds, precursors, and processing parameters, was systematically extracted from 4407 open-access solid-state synthesis papers and organized into a structured recipe dataset. Using a retrieval-augmented generation (RAG) approach, the system first retrieves similar recipes from the corpus and then generates a new candidate recipe conditioned on those exemplars. The generated recipes were benchmarked against literature data using quantitative scoring metrics, achieving strong agreement with experimentally reported conditions. To validate the predictive capability, the framework was applied to unreported solid-state electrolyte candidates identified through first-principles screening, and multiple oxy-selenide compounds were successfully synthesized through iterative feedback between the model and experiment. The recipe generator accurately refined synthesis parameters over successive trials, demonstrating its ability to reproduce phase-pure products while minimizing trial-and-error. This approach establishes a data-driven, feedback-optimized route to accelerate synthesis design, offering a generalizable paradigm for integrating language models into experimental materials research.
Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon et al.· Advances in Materials· 0 citations
Most computationally predicted materials are never synthesized because conventional synthesis optimization is slow, expertise-dependent, and iterative. Here we present a closed-loop framework that automates this expert workflow by placing human tacit knowledge in the loop through a large language model (LLM) that distills synthesis knowledge from the literature, high-throughput hyperspectral imaging for rapid film evaluation, and multi-objective Bayesian optimization guided by experimental feedback. In a paired optimization campaign, LLM-assisted initialization produced more Pareto-optimal samples and higher hypervolume than a Latin hypercube sampling baseline at matched trial counts, and this advantage persisted throughout iterative optimization. We demonstrate the framework by synthesizing the previously unreported perovskite-inspired compound Rb3BiI6 as thin films and validating the optimized films by optical bandgap analysis and X-ray diffraction. The framework transforms synthesis prediction from single-shot recommendation to iterative learning, providing a generalizable strategy to accelerate automated and fully autonomous experimental materials discovery.
Fang Sheng, Steven B. Torrisi, Amanda A. Volk et al.· 0 citations
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