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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Data-Driven Multi-Objective Optimization Tactics for Catalytic Asymmetric Reactions Using Bisphosphine Ligands

Journal of the American Chemical Society, Journal Year: 2022, Volume and Issue: 145(1), P. 110 - 121

Published: Dec. 27, 2022

Optimization of the catalyst structure to simultaneously improve multiple reaction objectives (e.g., yield, enantioselectivity, and regioselectivity) remains a formidable challenge. Herein, we describe machine learning workflow for multi-objective optimization catalytic reactions that employ chiral bisphosphine ligands. This was demonstrated through two sequential required in asymmetric synthesis an active pharmaceutical ingredient. To accomplish this, density functional theory-derived database >550 ligands constructed, designer chemical space mapping technique established. The protocol used classification methods identify catalysts, followed by linear regression model selectivity. led prediction validation significantly improved all outputs, suggesting general strategy can be readily implemented optimizations where performance is controlled

Language: Английский

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Machine Learning-Guided Development of Trialkylphosphine Ni^(I) Dimers and Applications in Site-Selective Catalysis

Journal of the American Chemical Society, Journal Year: 2023, Volume and Issue: 145(28), P. 15414 - 15424

Published: July 6, 2023

Owing to the unknown correlation of a metal’s ligand and its resulting preferred speciation in terms oxidation state, geometry, nuclearity, rational design multinuclear catalysts remains challenging. With goal accelerate identification suitable ligands that form trialkylphosphine-derived dihalogen-bridged Ni(I) dimers, we herein employed an assumption-based machine learning approach. The workflow offers guidance space for desired without (or only minimal) prior experimental data points. We experimentally verified predictions synthesized numerous novel dimers as well explored their potential catalysis. demonstrate C–I selective arylations polyhalogenated arenes bearing competing C–Br C–Cl sites under 5 min at room temperature using 0.2 mol % newly developed dimer, [Ni(I)(μ-Br)PAd2(n-Bu)]2, which is so far unmet with alternative dinuclear or mononuclear Ni Pd catalysts.

Language: Английский

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Small Data Can Play a Big Role in Chemical Discovery

Angewandte Chemie International Edition, Journal Year: 2023, Volume and Issue: 62(26)

Published: March 23, 2023

The chemistry community is currently witnessing a surge of scientific discoveries in organic supported by machine learning (ML) techniques. Whereas many these techniques were developed for big data applications, the nature experimental often confines practitioners to small datasets. Herein, we touch upon limitations associated with ML and emphasize impact bias variance on constructing reliable predictive models. We aim raise awareness possible pitfalls, thus, provide an introductory guideline good practice. Ultimately, stress great value statistical analysis data, which can be further boosted adopting holistic data-centric approach chemistry.

Language: Английский

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Multi‐Threshold Analysis for Chemical Space Mapping of Ni‐Catalyzed Suzuki‐Miyaura Couplings

European Journal of Organic Chemistry, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 11, 2024

Abstract A key challenge in synthetic chemistry is the selection of high‐performing ligands for cross‐coupling reactions. To address this challenge, work presents a classification workflow to identify physicochemical descriptors that bin monophosphine as active or inactive Ni‐catalyzed Suzuki‐Miyaura coupling Using five previously published high‐throughput experimentation datasets training, we found binary classifier using phosphine's minimum buried volume and Boltzmann‐averaged electrostatic potential most effective at distinguishing high low‐yielding ligands. Experimental validations are also presented. two from represent chemical space leads more predictive guide structure‐reactivity relationships compared with classic representations.

Language: Английский

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Connecting the Complexity of Stereoselective Synthesis to the Evolution of Predictive Tools

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

Published: Jan. 1, 2025

This review provides an overview of predictive tools in asymmetric synthesis. The evolution methods from simple qualitative pictures to complicated quantitative approaches is connected with the increased complexity stereoselective

Language: Английский

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Probability Guided Chemical Reaction Scopes

Published: Jan. 1, 2025

Language: Английский

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Recent developments in the use of machine learning in catalysis: A broad perspective with applications in kinetics

Chemical Engineering Journal, Journal Year: 2025, Volume and Issue: unknown, P. 160872 - 160872

Published: Feb. 1, 2025

Language: Английский

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Optimising Materials Properties with Minimal Data: Lessons from Vanadium Catalyst Modelling

Challenges and advances in computational chemistry and physics, Journal Year: 2025, Volume and Issue: unknown, P. 117 - 138

Published: Jan. 1, 2025

Language: Английский

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Putting Chemical Knowledge to Work in Machine Learning for Reactivity

CHIMIA International Journal for Chemistry, Journal Year: 2023, Volume and Issue: 77(1/2), P. 22 - 22

Published: Feb. 22, 2023

Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances computer science have resulted deep neural networks that can learn directly from the molecular structure. Neural are good choice when large amounts of data available. However, many datasets chemistry small, models utilizing knowledge required performance. Adding be achieved either by adding more information about molecules or adjusting model architecture itself. The current method is descriptors based on computed quantum-chemical properties. Exciting new research directions show it possible augment with better performance low-data regime. To modify models, differentiable programming enables seamless merging mathematical physics. resulting methods also data-efficient make predictions different initial dataset which they were trained. Application these chemistry-informed machine promise accelerate drug design, materials catalysis reactivity.

Language: Английский

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