Rhea, the reaction knowledgebase in 2022

Nucleic Acids Research, Journal Year: 2021, Volume and Issue: 50(D1), P. D693 - D700

Published: Nov. 9, 2021

Abstract Rhea (https://www.rhea-db.org) is an expert-curated knowledgebase of biochemical reactions based on the chemical ontology ChEBI (Chemical Entities Biological Interest) (https://www.ebi.ac.uk/chebi). In this paper, we describe a number key developments in since our last report database issue Nucleic Acids Research 2019. These include improved reaction coverage Rhea, adoption as reference vocabulary for enzyme annotation UniProt UniProtKB (https://www.uniprot.org), development new website, and designation ELIXIR Core Data Resource. We hope that these other will enhance utility resource to study engineer enzymes metabolic systems which they function.

Language: Английский

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Metabolic Engineering: Methodologies and Applications

Chemical Reviews, Journal Year: 2022, Volume and Issue: 123(9), P. 5521 - 5570

Published: Dec. 30, 2022

Metabolic engineering aims to improve the production of economically valuable molecules through genetic manipulation microbial metabolism. While discipline is a little over 30 years old, advancements in metabolic have given way industrial-level molecule benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes design, build, test, learn steps necessary for leading successful campaign. Moreover, we highlight major applications engineering, including synthesizing chemicals fuels, broadening substrate utilization, improving host robustness with focus on specific case studies. Finally, conclude discussion perspectives future challenges related engineering.

Language: Английский

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Deep learning driven biosynthetic pathways navigation for natural products with BioNavi-NP

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: June 10, 2022

Abstract The complete biosynthetic pathways are unknown for most natural products (NPs), it is thus valuable to make computer-aided bio-retrosynthesis predictions. Here, a navigable and user-friendly toolkit, BioNavi-NP, developed predict the both NPs NP-like compounds. First, single-step prediction model trained using general organic reactions through end-to-end transformer neural networks. Based on this model, plausible can be efficiently sampled an AND-OR tree-based planning algorithm from iterative multi-step bio-retrosynthetic routes. Extensive evaluations reveal that BioNavi-NP identify 90.2% of 368 test compounds recover reported building blocks as in set 72.8%, 1.7 times more accurate than existing conventional rule-based approaches. further shown biologically complex collected recent literature. toolkit well curated datasets learned models freely available facilitate elucidation reconstruction NPs.

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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Machine Learning-Guided Protein Engineering

ACS Catalysis, Journal Year: 2023, Volume and Issue: 13(21), P. 13863 - 13895

Published: Oct. 13, 2023

Recent progress in engineering highly promising biocatalysts has increasingly involved machine learning methods. These methods leverage existing experimental and simulation data to aid the discovery annotation of enzymes, as well suggesting beneficial mutations for improving known targets. The field protein is gathering steam, driven by recent success stories notable other areas. It already encompasses ambitious tasks such understanding predicting structure function, catalytic efficiency, enantioselectivity, dynamics, stability, solubility, aggregation, more. Nonetheless, still evolving, with many challenges overcome questions address. In this Perspective, we provide an overview ongoing trends domain, highlight case studies, examine current limitations learning-based We emphasize crucial importance thorough validation emerging models before their use rational design. present our opinions on fundamental problems outline potential directions future research.

Language: Английский

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Machine learning-enabled retrobiosynthesis of molecules

Nature Catalysis, Journal Year: 2023, Volume and Issue: 6(2), P. 137 - 151

Published: Feb. 16, 2023

Language: Английский

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Embracing data science in catalysis research

Nature Catalysis, Journal Year: 2024, Volume and Issue: 7(6), P. 624 - 635

Published: April 23, 2024

Language: Английский

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Machine learning in bioprocess development: from promise to practice

Trends in biotechnology, Journal Year: 2022, Volume and Issue: 41(6), P. 817 - 835

Published: Nov. 29, 2022

Language: Английский

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Merging enzymatic and synthetic chemistry with computational synthesis planning

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: Dec. 14, 2022

Synthesis planning programs trained on chemical reaction data can design efficient routes to new molecules of interest, but are limited in their ability leverage rare transformations. This challenge is acute for enzymatic reactions, which valuable due selectivity and sustainability few number. We report a retrosynthetic search algorithm using two neural network models retrosynthesis-one covering 7984 transformations one 163,723 synthetic transformations-that balances the exploration reactions identify hybrid synthesis plans. approach extends space moves by thousands uniquely one-step transformations, discovers or searches find none, designs shorter others. Application (-)-Δ9 tetrahydrocannabinol (THC) (dronabinol) R,R-formoterol (arformoterol) illustrates how our strategy facilitates replacement metal catalysis, high step counts, costly enantiomeric resolution with more elegant proposals.

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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