Late-stage C–H functionalization offers new opportunities in drug discovery

Nature Reviews Chemistry, Journal Year: 2021, Volume and Issue: 5(8), P. 522 - 545

Published: July 13, 2021

Language: Английский

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Photons or Electrons? A Critical Comparison of Electrochemistry and Photoredox Catalysis for Organic Synthesis

Chemical Reviews, Journal Year: 2021, Volume and Issue: 122(2), P. 2487 - 2649

Published: Nov. 9, 2021

Redox processes are at the heart of synthetic methods that rely on either electrochemistry or photoredox catalysis, but how do and catalysis compare? Both approaches provide access to high energy intermediates (e.g., radicals) enable bond formations not constrained by rules ionic 2 electron (e) mechanisms. Instead, they 1e mechanisms capable bypassing electronic steric limitations protecting group requirements, thus enabling chemists disconnect molecules in new different ways. However, while providing similar intermediates, differ several physical chemistry principles. Understanding those differences can be key designing transformations forging disconnections. This review aims highlight these similarities between comparing their underlying principles describing impact electrochemical photochemical methods.

Language: Английский

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Graph neural networks for materials science and chemistry

Communications Materials, Journal Year: 2022, Volume and Issue: 3(1)

Published: Nov. 26, 2022

Abstract Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict properties, accelerate simulations, design new structures, synthesis routes materials. Graph neural networks (GNNs) are one the fastest growing classes machine models. They particular relevance for as they directly work on a graph or structural representation molecules therefore have full access all relevant information required characterize In this Review, we provide overview basic principles GNNs, widely datasets, state-of-the-art architectures, followed by discussion wide range recent applications GNNs concluding with road-map further development application GNNs.

Language: Английский

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The Open Reaction Database

Journal of the American Chemical Society, Journal Year: 2021, Volume and Issue: 143(45), P. 18820 - 18826

Published: Nov. 2, 2021

Chemical reaction data in journal articles, patents, and even electronic laboratory notebooks are currently stored various formats, often unstructured, which presents a significant barrier to downstream applications, including the training of machine-learning models. We present Open Reaction Database (ORD), an open-access schema infrastructure for structuring sharing organic data, centralized repository. The ORD supports conventional emerging technologies, from benchtop reactions automated high-throughput experiments flow chemistry. schema, supporting code, web-based user interfaces all publicly available on GitHub. Our vision is that consistent representation support will enable applications greatly improve state art with respect computer-aided synthesis planning, prediction, other predictive chemistry tasks.

Language: Английский

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Computational Discovery of Transition-metal Complexes: From High-throughput Screening to Machine Learning

Chemical Reviews, Journal Year: 2021, Volume and Issue: 121(16), P. 9927 - 10000

Published: July 14, 2021

Transition-metal complexes are attractive targets for the design of catalysts and functional materials. The behavior metal-organic bond, while very tunable achieving target properties, is challenging to predict necessitates searching a wide complex space identify needles in haystacks applications. This review will focus on techniques that make high-throughput search transition-metal chemical feasible discovery with desirable properties. cover development, promise, limitations "traditional" computational chemistry (i.e., force field, semiempirical, density theory methods) as it pertains data generation inorganic molecular discovery. also discuss opportunities leveraging experimental sources. We how advances statistical modeling, artificial intelligence, multiobjective optimization, automation accelerate lead compounds rules. overall objective this showcase bringing together from diverse areas computer science have enabled rapid uncovering structure-property relationships chemistry. aim highlight unique considerations motifs bonding (e.g., variable spin oxidation state, strength/nature) set them their apart more commonly considered organic molecules. uncertainty relative scarcity motivate specific developments machine learning representations, model training, Finally, we conclude an outlook opportunity accelerated complexes.

Language: Английский

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A Brief Introduction to Chemical Reaction Optimization

Chemical Reviews, Journal Year: 2023, Volume and Issue: 123(6), P. 3089 - 3126

Published: Feb. 23, 2023

From the start of a synthetic chemist's training, experiments are conducted based on recipes from textbooks and manuscripts that achieve clean reaction outcomes, allowing scientist to develop practical skills some chemical intuition. This procedure is often kept long into researcher's career, as new developed similar protocols, intuition-guided deviations through learning failed experiments. However, when attempting understand systems interest, it has been shown model-based, algorithm-based, miniaturized high-throughput techniques outperform human intuition optimization in much more time- material-efficient manner; this covered detail paper. As many chemists not exposed these undergraduate teaching, leads disproportionate number scientists wish optimize their reactions but unable use methodologies or simply unaware existence. review highlights basics, cutting-edge, modern well its relation process scale-up can thereby serve reference for inspired each techniques, detailing several respective applications.

Language: Английский

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A review of molecular representation in the age of machine learning

Wiley Interdisciplinary Reviews Computational Molecular Science, Journal Year: 2022, Volume and Issue: 12(5)

Published: Feb. 18, 2022

Abstract Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances computing, machine learning, and artificial intelligence. Everyone working with molecules, whether chemist or not, needs an understanding of the representation molecules a machine‐readable format, as this is central to computational chemistry. Four classes representations are introduced: string, connection table, feature‐based, computer‐learned representations. Three most significant simplified molecular‐input line‐entry system (SMILES), International Chemical Identifier (InChI), MDL molfile, which SMILES was first successfully be used conjunction variational autoencoder (VAE) yield continuous molecules. This noteworthy because allows for efficient navigation immensely large chemical space possible Since 2018, when model type published, considerable effort has been put into developing novel improved methodologies. Most, if not all, researchers community make their easily accessible on GitHub, though discussion computation time domain applicability often overlooked. Herein, we present questions consideration future believe will VAEs even more accessible. article categorized under: Data Science > Chemoinformatics

Language: Английский

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A field guide to flow chemistry for synthetic organic chemists

Chemical Science, Journal Year: 2023, Volume and Issue: 14(16), P. 4230 - 4247

Published: Jan. 1, 2023

This review explores the benefits of flow chemistry and dispels notion that it is a mysterious “black box”, demonstrating how can push boundaries organic synthesis through understanding its governing principles.

Language: Английский

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Depolymerization of plastics by means of electrified spatiotemporal heating

Nature, Journal Year: 2023, Volume and Issue: 616(7957), P. 488 - 494

Published: April 19, 2023

Language: Английский

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Review of Machine Learning for Hydrodynamics, Transport, and Reactions in Multiphase Flows and Reactors

Industrial & Engineering Chemistry Research, Journal Year: 2022, Volume and Issue: 61(28), P. 9901 - 9949

Published: July 7, 2022

Artificial intelligence (AI), machine learning (ML), and data science are leading to a promising transformative paradigm. ML, especially deep physics-informed is valuable toolkit that complements incomplete domain-specific knowledge in conventional experimental computational methods. ML can provide flexible techniques facilitate the conceptual development of new robust predictive models for multiphase flows reactors by finding hidden pattern/information/mechanism set. Due such emergence, we thereby comprehensively survey, explore, analyze, discuss key advancements recent applications hydrodynamics, heat mass transfer, reactions single-phase flow systems from different aspects: (1) closure drag force, turbulence stresses heat/mass transfer improve accuracy efficiency typical CFD simulations; (2) image reconstruction, regime identification, parameter predictions, optimization transport fields; (3) reaction kinetics modeling (e.g., predictions networks, kinetic parameters, species production) condition optimization. These sections also analyze advantages weakness solving problems domain reactors. Finally, summarize under-solving challenges opportunities order identify future directions would be useful research community. Future study envisaged accelerated science.

Language: Английский

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