Scientific discovery in the age of artificial intelligence

Nature, Journal Year: 2023, Volume and Issue: 620(7972), P. 47 - 60

Published: Aug. 2, 2023

Language: Английский

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The rise of self-driving labs in chemical and materials sciences

Nature Synthesis, Journal Year: 2023, Volume and Issue: 2(6), P. 483 - 492

Published: Jan. 30, 2023

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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SELFIES and the future of molecular string representations

Patterns, Journal Year: 2022, Volume and Issue: 3(10), P. 100588 - 100588

Published: Oct. 1, 2022

Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks chemistry materials science. Examples include the prediction of properties, discovery new reaction pathways, or design molecules. The needs read write fluently a chemical language each these tasks. Strings common tool represent molecular graphs, most popular string representation, Smiles, has powered cheminformatics since late 1980s. However, context AI ML chemistry, Smiles several shortcomings—most pertinently, combinations symbols lead invalid results with no valid interpretation. To overcome this issue, molecules was introduced 2020 that guarantees 100% robustness: SELF-referencing embedded (Selfies). Selfies simplified enabled numerous chemistry. In perspective, we look future discuss representations, along their respective opportunities challenges. We propose 16 concrete projects robust representations. These involve extension toward domains, exciting questions at interface languages, interpretability both humans machines. hope proposals will inspire follow-up works exploiting full potential representations

Language: Английский

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Machine Learning for Chemical Reactivity: The Importance of Failed Experiments

Angewandte Chemie International Edition, Journal Year: 2022, Volume and Issue: 61(29)

Published: May 5, 2022

Abstract Assessing the outcomes of chemical reactions in a quantitative fashion has been cornerstone across all synthetic disciplines. Classically approached through empirical optimization, data‐driven modelling bears an enormous potential to streamline this process. However, such predictive models require significant quantities high‐quality data, availability which is limited: Main reasons for include experimental errors and, importantly, human biases regarding experiment selection and result reporting. In series case studies, we investigate impact these drawing general conclusions from reaction revealing utmost importance “negative” examples. Eventually, studies into data expansion approaches showcase directions circumvent limitations—and demonstrate perspectives towards long‐term quality enhancement chemistry.

Language: Английский

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14 examples of how LLMs can transform materials science and chemistry: a reflection on a large language model hackathon

Digital Discovery, Journal Year: 2023, Volume and Issue: 2(5), P. 1233 - 1250

Published: Jan. 1, 2023

We report the findings of a hackathon focused on exploring diverse applications large language models in molecular and materials science.

Language: Английский

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Machine intelligence for chemical reaction space

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

Published: March 7, 2022

Abstract Discovering new reactions, optimizing their performance, and extending the synthetically accessible chemical space are critical drivers for major technological advances more sustainable processes. The current wave of machine intelligence is revolutionizing all data‐rich disciplines. Machine has emerged as a potential game‐changer reaction exploration synthesis novel molecules materials. Herein, we will address recent development data‐driven technologies tasks, including forward prediction, retrosynthesis, optimization, catalysts design, inference experimental procedures, classification. Accurate predictions reactivity changing R&D processes and, at same time, promoting an accelerated discovery scheme both in academia across pharmaceutical industries. This work help to clarify key contributions fields open challenges that remain be addressed. article categorized under: Data Science > Artificial Intelligence/Machine Learning Computer Algorithms Programming Chemoinformatics

Language: Английский

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A graph representation of molecular ensembles for polymer property prediction

Chemical Science, Journal Year: 2022, Volume and Issue: 13(35), P. 10486 - 10498

Published: Jan. 1, 2022

Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational property prediction virtual screening can accelerate polymer design by prioritizing candidates expected have favorable properties. However, in contrast often not well-defined single structures but an ensemble similar which poses unique challenges traditional representations machine learning approaches. Here, we introduce graph representation molecular ensembles associated neural network architecture that tailored prediction. We demonstrate this approach captures critical features polymeric materials, like chain architecture, monomer stoichiometry, degree polymerization, achieves superior accuracy off-the-shelf cheminformatics methodologies. While doing so, built dataset simulated electron affinity ionization potential values for >40k with varying composition, may be the development other The models presented work pave path toward new classes algorithms informatics and, more broadly, framework modeling ensembles.

Language: Английский

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