Applying statistical modeling strategies to sparse datasets in synthetic chemistry

Science Advances, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

The application of statistical modeling in organic chemistry is emerging as a standard practice for probing structure-activity relationships and predictive tool many optimization objectives. This review aimed tutorial those entering the area chemistry. We provide case studies to highlight considerations approaches that can be used successfully analyze datasets low data regimes, common situation encountered given experimental demands Statistical hinges on (what being modeled), descriptors (how are represented), algorithms modeled). Herein, we focus how various reaction outputs (e.g., yield, rate, selectivity, solubility, stability, turnover number) structures binned, heavily skewed, distributed) influence choice algorithm constructing chemically insightful models.

Language: Английский

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Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates

Journal of the American Chemical Society, Journal Year: 2025, Volume and Issue: 147(9), P. 7476 - 7484

Published: Feb. 21, 2025

The development of machine learning models to predict the regioselectivity C(sp3)-H functionalization reactions is reported. A data set for dioxirane oxidations was curated from literature and used generate a model C-H oxidation. To assess whether smaller, intentionally designed sets could provide accuracy on complex targets, series acquisition functions were developed select most informative molecules specific target. Active learning-based that leverage predicted reactivity uncertainty found outperform those based molecular site similarity alone. use elaboration significantly reduced number points needed perform accurate prediction, it machine-designed can give predictions when larger, randomly selected fail. Finally, workflow experimentally validated five substrates shown be applicable predicting arene radical borylation. These studies quantitative alternative intuitive extrapolation "model substrates" frequently estimate molecules.

Language: Английский

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Enantioselective Sulfonimidamide Acylation via a Cinchona Alkaloid-Catalyzed Desymmetrization: Scope, Data Science, and Mechanistic Investigation

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(12), P. 8536 - 8546

Published: March 13, 2024

Methods to access chiral sulfur(VI) pharmacophores are of interest in medicinal and synthetic chemistry. We report the desymmetrization unprotected sulfonimidamides via asymmetric acylation with a cinchona-phosphinate catalyst. The desired products formed excellent yield enantioselectivity no observed bis-acylation. A data-science-driven approach substrate scope evaluation was coupled high throughput experimentation (HTE) facilitate statistical modeling order inform mechanistic studies. Reaction kinetics, catalyst structural studies, density functional theory (DFT) transition state analysis elucidated turnover-limiting step be collapse tetrahedral intermediate provided key insights into catalyst-substrate structure–activity relationships responsible for origin enantioselectivity. This study offers reliable method accessing enantioenriched propel their application as serves an example insight that can gleaned from integrating data science traditional physical organic techniques.

Language: Английский

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Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective

Journal of Chemical Information and Modeling, Journal Year: 2024, Volume and Issue: 64(8), P. 2955 - 2970

Published: March 15, 2024

Chemical reactions serve as foundational building blocks for organic chemistry and drug design. In the era of large AI models, data-driven approaches have emerged to innovate design novel reactions, optimize existing ones higher yields, discover new pathways synthesizing chemical structures comprehensively. To effectively address these challenges with machine learning it is imperative derive robust informative representations or engage in feature engineering using extensive data sets reactions. This work aims provide a comprehensive review established reaction featurization approaches, offering insights into selection features wide array tasks. The advantages limitations employing SMILES, molecular fingerprints, graphs, physics-based properties are meticulously elaborated. Solutions bridge gap between different will also be critically evaluated. Additionally, we introduce frontier pretraining, holding promise an innovative yet unexplored avenue.

Language: Английский

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Developing BioNavi for Hybrid Retrosynthesis Planning

JACS Au, Journal Year: 2024, Volume and Issue: 4(7), P. 2492 - 2502

Published: July 3, 2024

Illuminating synthetic pathways is essential for producing valuable chemicals, such as bioactive molecules. Chemical and biological syntheses are crucial, their integration often leads to more efficient sustainable pathways. Despite the rapid development of retrosynthesis models, few them consider both chemical syntheses, hindering pathway design high-value chemicals. Here, we propose BioNavi by innovating multitask learning reaction templates into deep learning-driven model hybrid synthesis in a interpretable manner. outperforms existing approaches on different data sets, achieving 75% hit rate replicating reported biosynthetic displaying superior ability designing Additional case studies further illustrate potential application de novo design. The enhanced web server (http://biopathnavi.qmclab.com/bionavi/) simplifies input operations implements step-by-step exploration according user experience. We show that handy navigator various

Language: Английский

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Autonomous chemistry: Navigating self-driving labs in chemical and material sciences

Matter, Journal Year: 2024, Volume and Issue: 7(7), P. 2382 - 2398

Published: July 1, 2024

Language: Английский

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Recommending reaction conditions with label ranking

Chemical Science, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Label ranking is introduced as a conceptually new means for prioritizing experiments. Their simplicity, ease of application, and the use aggregation facilitate their ability to make accurate predictions with small datasets.

Language: Английский

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Parametrization of κ²-N,O-Oxazoline Preligands for Enantioselective Cobaltaelectro-Catalyzed C–H Activations

ACS Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 4450 - 4459

Published: Feb. 28, 2025

Enantioselective electrocatalyzed C–H activations have emerged as a transformative platform for the assembly of value-added chiral organic molecules. Despite recent progress, construction multiple C(sp3)-stereogenic centers via C(sp3)–C(sp3) bond formation has thus far proven to be elusive. In contrast, we herein report an annulative activation strategy, generating Fsp3-rich molecules with high levels diastereo- and enantioselectivity. κ2-N,O-oxazoline preligands were effectively employed in enantioselective cobalt(III)-catalyzed reactions. Using DFT-derived descriptors regression statistical modeling, performed parametrization study on modularity preligands. The resulted model describing ligands' selectivity characterized by key steric, electronic, interaction behaviors.

Language: Английский

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Standardizing Substrate Selection: A Strategy toward Unbiased Evaluation of Reaction Generality

ACS Central Science, Journal Year: 2024, Volume and Issue: unknown

Published: April 8, 2024

With over 10,000 new reaction protocols arising every year, only a handful of these procedures transition from academia to application. A major reason for this gap stems the lack comprehensive knowledge about reaction's scope, i.e., which substrates protocol can or cannot be applied. Even though chemists invest substantial effort assess scope protocols, resulting tables involve significant biases, reducing their expressiveness. Herein we report standardized substrate selection strategy designed mitigate biases and evaluate applicability, as well limits, any chemical reaction. Unsupervised learning is utilized map space industrially relevant molecules. Subsequently, potential candidates are projected onto universal map, enabling structurally diverse set with optimal relevance coverage. By testing our methodology on different reactions, were able demonstrate its effectiveness in finding general reactivity trends by using few highly representative examples. The developed empowers showcase unbiased applicability novel methodologies, facilitating practical applications. We hope that work will trigger interdisciplinary discussions synthetic chemistry, leading improved data quality.

Language: Английский

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Development of High-Throughput Experimentation Approaches for Rapid Radiochemical Exploration

Journal of the American Chemical Society, Journal Year: 2024, Volume and Issue: 146(15), P. 10581 - 10590

Published: April 5, 2024

Positron emission tomography is a widely used imaging platform for studying physiological processes. Despite the proliferation of modern synthetic methodologies radiolabeling, optimization these reactions still primarily relies on inefficient one-factor-at-a-time approaches. High-throughput experimentation (HTE) has proven to be powerful approach optimizing in many areas chemical synthesis. However, date, HTE rarely been applied radiochemistry. This largely because short lifetime common radioisotopes, which presents major challenges efficient parallel reaction setup and analysis using standard equipment workflows. Herein, we demonstrate an effective workflow apply it copper-mediated radiofluorination pharmaceutically relevant boronate ester substrates. The utilizes commercial allows rapid reactions, exploring space aryl boronates radiofluorinations, constructing large radiochemistry data sets.

Language: Английский

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