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Liu Y, Yu Y, Wu B, Qian J, Mu H, Gu L, Zhou R, Zhang H, Wu H, Bu Y. A comprehensive prediction system for silkworm acute toxicity assessment of environmental and in-silico pesticides. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2024; 282:116759. [PMID: 39029220 DOI: 10.1016/j.ecoenv.2024.116759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/03/2024] [Accepted: 07/16/2024] [Indexed: 07/21/2024]
Abstract
The excessive application and loss of pesticides poses a great risk to the ecosystem, and the environmental safety assessment of pesticides is time-consuming and expensive using traditional animal toxicity tests. In this work, a pesticide acute toxicity dataset was created for silkworm integrating extensive experiments and various common pesticide formulations considering the sensitivity of silkworm to adverse environment, its economic value in China, and a gap in machine learning (ML) research on the toxicity prediction of this species, which addressed the previous limitation of only being able to predict toxicity classification without specific toxicity values. A new comprehensive voting model (CVR) was developed based on ML, combined with three regression algorithms, namely, Bayesian Ridge (BR), K Neighbors Regressor (KNN), Random Forest Regressor (RF) to accurately calculate lethal concentration 50 % (LC50). Three conformal models were successfully constructed, marking the first combination of conformal models with confidence intervals to predict silkworm toxicity. Further, the mechanism by analyzing structural alerts was summarized, and identified 25 warning structures, 24 positive compounds and 14 negative compounds. Importantly, a novel comprehensive prediction system was constructed that can provide LC50 and confidence intervals, structural alerts analysis, lipid-water partition coefficient (LogP) and similarity analysis, which can comprehensively evaluate the ecological toxicity risk of substances to make up for the incomplete toxicity data of new pesticides. The validity and generalization of the CVR model were verified by an external validation set. In addition, five new, low-toxic and green pesticide alternatives were designed through 50,000 cycles. Moreover, our software and ST Profiler can provide low-cost information access to accelerate environmental risk assessment, which can predict not only a single chemical, but also batches of chemicals, simply by inputting the SMILES / CAS / (Chinese / English) name of chemicals.
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Affiliation(s)
- Yutong Liu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China; Department of Chemistry, College of Sciences, Nanjing Agricultural University, Nanjing 210095, PR China
| | - Yue Yu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Jieshu Qian
- School of Environmental Engineering, Wuxi University, Jiangsu 214105, PR China
| | - Hongxin Mu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Luyao Gu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Rong Zhou
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Houhu Zhang
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China
| | - Hua Wu
- Department of Chemistry, College of Sciences, Nanjing Agricultural University, Nanjing 210095, PR China.
| | - Yuanqing Bu
- Research Center of Solid Waste Pollution and Prevention, Nanjing Institute of Environmental Science, Ministry of Ecology and Environment, Nanjing 210042, PR China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Nanjing University of Information Science & Technology, Nanjing 210044, PR China.
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2
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Baczewska P, Kulczykowski M, Zambroń B, Jaszczewska-Adamczak J, Pakulski Z, Roszak R, Grzybowski BA, Mlynarski J. Machine Learning Algorithm Guides Catalyst Choices for Magnesium-Catalyzed Asymmetric Reactions. Angew Chem Int Ed Engl 2024:e202318487. [PMID: 38878001 DOI: 10.1002/anie.202318487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Indexed: 08/13/2024]
Abstract
Organic-chemical literature encompasses large numbers of catalysts and reactions they can effect. Many of these examples are published merely to document the catalysts' scope but do not necessarily guarantee that a given catalyst is "optimal"-in terms of yield or enantiomeric excess-for a particular reaction. This paper describes a Machine Learning model that aims to improve such catalyst-reaction assignments based on the carefully curated literature data. As we show here for the case of asymmetric magnesium catalysis, this model achieves relatively high accuracy and offers out of-the-box predictions successfully validated by experiment, e.g., in synthetically demanding asymmetric reductions or Michael additions.
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Affiliation(s)
- Paulina Baczewska
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
| | - Michał Kulczykowski
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
| | - Bartosz Zambroń
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
| | | | - Zbigniew Pakulski
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
| | - Rafał Roszak
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
- Center for Algorithmic and Robotized Synthesis (CARS) of Korea's Institute for Basic Science (IBS) and Department of Chemistry, Ulsan National Institute of Science and Technology 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan, 44919, South Korea
| | - Jacek Mlynarski
- Institute of Organic Chemistry, Polish Academy of Sciences, Kasprzaka 44/52, 02-224, Warsaw, Poland
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3
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Strieth-Kalthoff F, Szymkuć S, Molga K, Aspuru-Guzik A, Glorius F, Grzybowski BA. Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge. J Am Chem Soc 2024. [PMID: 38598363 DOI: 10.1021/jacs.4c00338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/12/2024]
Abstract
Rapid advancements in artificial intelligence (AI) have enabled breakthroughs across many scientific disciplines. In organic chemistry, the challenge of planning complex multistep chemical syntheses should conceptually be well-suited for AI. Yet, the development of AI synthesis planners trained solely on reaction-example-data has stagnated and is not on par with the performance of "hybrid" algorithms combining AI with expert knowledge. This Perspective examines possible causes of these shortcomings, extending beyond the established reasoning of insufficient quantities of reaction data. Drawing attention to the intricacies and data biases that are specific to the domain of synthetic chemistry, we advocate augmenting the unique capabilities of AI with the knowledge base and the reasoning strategies of domain experts. By actively involving synthetic chemists, who are the end users of any synthesis planning software, into the development process, we envision to bridge the gap between computer algorithms and the intricate nature of chemical synthesis.
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Affiliation(s)
- Felix Strieth-Kalthoff
- University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
- University of Toronto, Department of Computer Science, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
| | - Sara Szymkuć
- Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
| | - Karol Molga
- Allchemy, 2145 45th Street #201, Highland, Indiana 46322, United States
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
| | - Alán Aspuru-Guzik
- University of Toronto, Department of Chemistry and Department of Computer Science, 80 St. George St., Toronto, Ontario M5S 3H6, Canada
- University of Toronto, Department of Computer Science, 10 King's College Road, Toronto, Ontario M5S 3G4, Canada
- Vector Institute for Artificial Intelligence, 661 University Ave., Toronto, Ontario M5G 1M1, Canada
- University of Toronto, Department of Chemical Engineering and Applied Chemistry, 200 College St., Toronto, Ontario M5S 3E5, Canada
- University of Toronto, Department of Materials Science and Engineering, 184 College St., Toronto, Ontario M5S 3E4, Canada
| | - Frank Glorius
- Universität Münster, Organisch-Chemisches Institut, Corrensstr. 36, 48149 Münster, Germany
| | - Bartosz A Grzybowski
- Institute of Organic Chemistry, Polish Academy of Sciences, ul. Kasprzaka 44/52, Warsaw 01-224, Poland
- IBS Center for Algorithmic and Robotized Synthesis, CARS, UNIST 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
- Department of Chemistry, UNIST, 50, UNIST-gil, Eonyang-eup, Ulju-gun, Ulsan 689-798, South Korea
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4
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Tropsha A, Isayev O, Varnek A, Schneider G, Cherkasov A. Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR. Nat Rev Drug Discov 2024; 23:141-155. [PMID: 38066301 DOI: 10.1038/s41573-023-00832-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2023] [Indexed: 02/08/2024]
Abstract
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Affiliation(s)
| | | | | | | | - Artem Cherkasov
- University of British Columbia, Vancouver, BC, Canada.
- Photonic Inc., Coquitlam, BC, Canada.
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5
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Klucznik T, Syntrivanis LD, Baś S, Mikulak-Klucznik B, Moskal M, Szymkuć S, Mlynarski J, Gadina L, Beker W, Burke MD, Tiefenbacher K, Grzybowski BA. Computational prediction of complex cationic rearrangement outcomes. Nature 2024; 625:508-515. [PMID: 37967579 PMCID: PMC10864989 DOI: 10.1038/s41586-023-06854-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 11/08/2023] [Indexed: 11/17/2023]
Abstract
Recent years have seen revived interest in computer-assisted organic synthesis1,2. The use of reaction- and neural-network algorithms that can plan multistep synthetic pathways have revolutionized this field1,3-7, including examples leading to advanced natural products6,7. Such methods typically operate on full, literature-derived 'substrate(s)-to-product' reaction rules and cannot be easily extended to the analysis of reaction mechanisms. Here we show that computers equipped with a comprehensive knowledge-base of mechanistic steps augmented by physical-organic chemistry rules, as well as quantum mechanical and kinetic calculations, can use a reaction-network approach to analyse the mechanisms of some of the most complex organic transformations: namely, cationic rearrangements. Such rearrangements are a cornerstone of organic chemistry textbooks and entail notable changes in the molecule's carbon skeleton8-12. The algorithm we describe and deploy at https://HopCat.allchemy.net/ generates, within minutes, networks of possible mechanistic steps, traces plausible step sequences and calculates expected product distributions. We validate this algorithm by three sets of experiments whose analysis would probably prove challenging even to highly trained chemists: (1) predicting the outcomes of tail-to-head terpene (THT) cyclizations in which substantially different outcomes are encoded in modular precursors differing in minute structural details; (2) comparing the outcome of THT cyclizations in solution or in a supramolecular capsule; and (3) analysing complex reaction mixtures. Our results support a vision in which computers no longer just manipulate known reaction types1-7 but will help rationalize and discover new, mechanistically complex transformations.
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Affiliation(s)
- Tomasz Klucznik
- Allchemy, Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Leonidas-Dimitrios Syntrivanis
- Roger Adams Laboratory, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Basel, Basel, Switzerland.
| | - Sebastian Baś
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
- Faculty of Chemistry, Jagiellonian University, Krakow, Poland
| | - Barbara Mikulak-Klucznik
- Allchemy, Highland, IN, USA
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | | | | | - Jacek Mlynarski
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Louis Gadina
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
| | - Wiktor Beker
- Allchemy, Highland, IN, USA.
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland.
| | - Martin D Burke
- Roger Adams Laboratory, School of Chemical Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Molecule Maker Laboratory Institute, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Molecule Maker Laboratory at the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
- Carle Illinois College of Medicine, University of Illinois at Urbana-Champaign, Urbana, IL, USA.
| | - Konrad Tiefenbacher
- Department of Chemistry, University of Basel, Basel, Switzerland.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
| | - Bartosz A Grzybowski
- Allchemy, Highland, IN, USA.
- Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland.
- IBS Center for Algorithmic and Robotized Synthesis, CARS, Eonyang-eup, Ulju-gun, Ulsan, South Korea.
- Department of Chemistry, UNIST, Eonyang-eup, Ulju-gun, Ulsan, South Korea.
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6
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Ryu G, Kim GB, Yu T, Lee SY. Deep learning for metabolic pathway design. Metab Eng 2023; 80:130-141. [PMID: 37734652 DOI: 10.1016/j.ymben.2023.09.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/17/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023]
Abstract
The establishment of a bio-based circular economy is imperative in tackling the climate crisis and advancing sustainable development. In this realm, the creation of microbial cell factories is central to generating a variety of chemicals and materials. The design of metabolic pathways is crucial in shaping these microbial cell factories, especially when it comes to producing chemicals with yet-to-be-discovered biosynthetic routes. To aid in navigating the complexities of chemical and metabolic domains, computer-supported tools for metabolic pathway design have emerged. In this paper, we evaluate how digital strategies can be employed for pathway prediction and enzyme discovery. Additionally, we touch upon the recent strides made in using deep learning techniques for metabolic pathway prediction. These computational tools and strategies streamline the design of metabolic pathways, facilitating the development of microbial cell factories. Leveraging the capabilities of deep learning in metabolic pathway design is profoundly promising, potentially hastening the advent of a bio-based circular economy.
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Affiliation(s)
- Gahyeon Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Taeho Yu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Four), KAIST Institute for BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea; Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea; BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea; Graduate School of Engineering Biology, KAIST, Daejeon, 34141, Republic of Korea.
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7
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Hayashi H, Maeda S, Mita T. Quantum chemical calculations for reaction prediction in the development of synthetic methodologies. Chem Sci 2023; 14:11601-11616. [PMID: 37920348 PMCID: PMC10619630 DOI: 10.1039/d3sc03319h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/29/2023] [Indexed: 11/04/2023] Open
Abstract
Quantum chemical calculations have been used in the development of synthetic methodologies to analyze the reaction mechanisms of the developed reactions. Their ability to estimate chemical reaction pathways, including transition state energies and connected equilibria, has led researchers to embrace their use in predicting unknown reactions. This perspective highlights strategies that leverage quantum chemical calculations for the prediction of reactions in the discovery of new methodologies. Selected examples demonstrate how computation has driven the development of unknown reactions, catalyst design, and the exploration of synthetic routes to complex molecules prior to often laborious, costly, and time-consuming experimental investigations.
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Affiliation(s)
- Hiroki Hayashi
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University Kita 21, Nishi 10, Kita-ku Sapporo Hokkaido 001-0021 Japan
- JST-ERATO, Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project Kita 10, Nishi 8, Kita-ku Sapporo Hokkaido 060-0810 Japan
| | - Satoshi Maeda
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University Kita 21, Nishi 10, Kita-ku Sapporo Hokkaido 001-0021 Japan
- JST-ERATO, Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project Kita 10, Nishi 8, Kita-ku Sapporo Hokkaido 060-0810 Japan
- Department of Chemistry, Faculty of Science, Hokkaido University Kita 10, Nishi 8, Kita-ku Sapporo Hokkaido 060-0810 Japan
| | - Tsuyoshi Mita
- Institute for Chemical Reaction Design and Discovery (WPI-ICReDD), Hokkaido University Kita 21, Nishi 10, Kita-ku Sapporo Hokkaido 001-0021 Japan
- JST-ERATO, Maeda Artificial Intelligence in Chemical Reaction Design and Discovery Project Kita 10, Nishi 8, Kita-ku Sapporo Hokkaido 060-0810 Japan
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8
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Shim E, Tewari A, Cernak T, Zimmerman PM. Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit. J Chem Inf Model 2023; 63:3659-3668. [PMID: 37312524 PMCID: PMC11163943 DOI: 10.1021/acs.jcim.3c00577] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Machine learning models are increasingly being utilized to predict outcomes of organic chemical reactions. A large amount of reaction data is used to train these models, which is in stark contrast to how expert chemists discover and develop new reactions by leveraging information from a small number of relevant transformations. Transfer learning and active learning are two strategies that can operate in low-data situations, which may help fill this gap and promote the use of machine learning for tackling real-world challenges in organic synthesis. This Perspective introduces active and transfer learning and connects these to potential opportunities and directions for further research, especially in the area of prospective development of chemical transformations.
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Affiliation(s)
- Eunjae Shim
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Ambuj Tewari
- Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Tim Cernak
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Paul M Zimmerman
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
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9
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Zhong W, Yang Z, Chen CYC. Retrosynthesis prediction using an end-to-end graph generative architecture for molecular graph editing. Nat Commun 2023; 14:3009. [PMID: 37230985 DOI: 10.1038/s41467-023-38851-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 05/17/2023] [Indexed: 05/27/2023] Open
Abstract
Retrosynthesis planning, the process of identifying a set of available reactions to synthesize the target molecules, remains a major challenge in organic synthesis. Recently, computer-aided synthesis planning has gained renewed interest and various retrosynthesis prediction algorithms based on deep learning have been proposed. However, most existing methods are limited to the applicability and interpretability of model predictions, and further improvement of predictive accuracy to a more practical level is still required. In this work, inspired by the arrow-pushing formalism in chemical reaction mechanisms, we present an end-to-end architecture for retrosynthesis prediction called Graph2Edits. Specifically, Graph2Edits is based on graph neural network to predict the edits of the product graph in an auto-regressive manner, and sequentially generates transformation intermediates and final reactants according to the predicted edits sequence. This strategy combines the two-stage processes of semi-template-based methods into one-pot learning, improving the applicability in some complicated reactions, and also making its predictions more interpretable. Evaluated on the standard benchmark dataset USPTO-50k, our model achieves the state-of-the-art performance for semi-template-based retrosynthesis with a promising 55.1% top-1 accuracy.
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Affiliation(s)
- Weihe Zhong
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Ziduo Yang
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China
| | - Calvin Yu-Chian Chen
- Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, 518107, China.
- Department of Medical Research, China Medical University Hospital, Taichung, 40447, Taiwan.
- Department of Bioinformatics and Medical Engineering, Asia University, Taichung, 41354, Taiwan.
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10
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Li K, Wang J, Song Y, Wang Y. Machine learning-guided discovery of ionic polymer electrolytes for lithium metal batteries. Nat Commun 2023; 14:2789. [PMID: 37188717 DOI: 10.1038/s41467-023-38493-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 05/04/2023] [Indexed: 05/17/2023] Open
Abstract
As essential components of ionic polymer electrolytes (IPEs), ionic liquids (ILs) with high ionic conductivity and wide electrochemical window are promising candidates to enable safe and high-energy-density lithium metal batteries (LMBs). Here, we describe a machine learning workflow embedded with quantum calculation and graph convolutional neural network to discover potential ILs for IPEs. By selecting subsets of the recommended ILs, combining with a rigid-rod polyelectrolyte and a lithium salt, we develop a series of thin (~50 μm) and robust (>200 MPa) IPE membranes. The Li|IPEs|Li cells exhibit ultrahigh critical-current-density (6 mA cm-2) at 80 °C. The Li|IPEs|LiFePO4 (10.3 mg cm-2) cells deliver outstanding capacity retention in 350 cycles (>96% at 0.5C; >80% at 2C), fast charge/discharge capability (146 mAh g-1 at 3C) and excellent efficiency (>99.92%). This performance is rarely reported by other single-layer polymer electrolytes without any flammable organics for LMBs.
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Affiliation(s)
- Kai Li
- Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, China
| | - Jifeng Wang
- Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, China
| | - Yuanyuan Song
- Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, China
| | - Ying Wang
- Department of Macromolecular Science, State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai, 200438, China.
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11
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Lin Y, Zhang R, Wang D, Cernak T. Computer-aided key step generation in alkaloid total synthesis. Science 2023; 379:453-457. [PMID: 36730413 DOI: 10.1126/science.ade8459] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Efficient chemical synthesis is critical to satisfying future demands for medicines, materials, and agrochemicals. Retrosynthetic analysis of modestly complex molecules has been automated over the course of decades, but the combinatorial explosion of route possibilities has challenged computer hardware and software until only recently. Here, we explore a computational strategy that merges computer-aided synthesis planning with molecular graph editing to minimize the number of synthetic steps required to produce alkaloids. Our study culminated in an enantioselective three-step synthesis of (-)-stemoamide by leveraging high-impact key steps, which could be identified in computer-generated retrosynthesis plans using graph edit distances.
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Affiliation(s)
- Yingfu Lin
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rui Zhang
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Di Wang
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tim Cernak
- Department of Medicinal Chemistry, University of Michigan, Ann Arbor, MI 48109, USA.,Department of Chemistry, University of Michigan, Ann Arbor, MI 48109, USA
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12
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Wen M, Spotte-Smith EWC, Blau SM, McDermott MJ, Krishnapriyan AS, Persson KA. Chemical reaction networks and opportunities for machine learning. NATURE COMPUTATIONAL SCIENCE 2023; 3:12-24. [PMID: 38177958 DOI: 10.1038/s43588-022-00369-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 11/08/2022] [Indexed: 01/06/2024]
Abstract
Chemical reaction networks (CRNs), defined by sets of species and possible reactions between them, are widely used to interrogate chemical systems. To capture increasingly complex phenomena, CRNs can be leveraged alongside data-driven methods and machine learning (ML). In this Perspective, we assess the diverse strategies available for CRN construction and analysis in pursuit of a wide range of scientific goals, discuss ML techniques currently being applied to CRNs and outline future CRN-ML approaches, presenting scientific and technical challenges to overcome.
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Affiliation(s)
- Mingjian Wen
- Chemical and Biomolecular Engineering, University of Houston, Houston, TX, USA
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Evan Walter Clark Spotte-Smith
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Samuel M Blau
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Matthew J McDermott
- Materials Science Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA
| | - Aditi S Krishnapriyan
- Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Chemical and Biomolecular Engineering, University of California, Berkeley, Berkeley, CA, USA
- Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA
| | - Kristin A Persson
- Materials Science and Engineering, University of California, Berkeley, Berkeley, CA, USA.
- Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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13
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Matlin SA, Cornell SE, Krief A, Hopf H, Mehta G. Chemistry must respond to the crisis of transgression of planetary boundaries. Chem Sci 2022; 13:11710-11720. [PMID: 36348954 PMCID: PMC9627718 DOI: 10.1039/d2sc03603g] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 09/12/2022] [Indexed: 11/22/2022] Open
Abstract
Recent assessments alarmingly indicate that many of the world's leading chemicals are transgressing one or more of the nine planetary boundaries, which define safe operating spaces within which humanity can continue to develop and thrive for generations to come. The unfolding crisis cannot be ignored and there is a once-in-a-century opportunity for chemistry - the science of transformation of matter - to make a critical difference to the future of people and planet. How can chemists contribute to meeting these challenges and restore stability and strengthen resilience to the planetary system that humanity needs for its survival? To respond to the wake-up call, three crucial steps are outlined: (1) urgently working to understand the nature of the looming threats, from a chemistry perspective; (2) harnessing the ingenuity and innovation that are central to the practice of chemistry to develop sustainable solutions; and (3) transforming chemistry itself, in education, research and industry, to re-position it as 'chemistry for sustainability' and lead the stewardship of the world's chemical resources. This will require conservation of material stocks in forms that remain available for use, through attention to circularity, as well as strengthening engagement in systems-based approaches to designing chemistry research and processes informed by convergent working with many other disciplines.
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Affiliation(s)
- Stephen A Matlin
- Institute of Global Health Innovation, Imperial College London London SW7 2AZ UK
- International Organization for Chemical Sciences in Development 61 rue de Bruxelles B-5000 Namur Belgium
| | - Sarah E Cornell
- International Organization for Chemical Sciences in Development 61 rue de Bruxelles B-5000 Namur Belgium
- Stockholm Resilience Centre, Faculty of Science, Stockholm University Stockholm Sweden
| | - Alain Krief
- International Organization for Chemical Sciences in Development 61 rue de Bruxelles B-5000 Namur Belgium
- Chemistry Department, Namur University B-5000 Namur Belgium
| | - Henning Hopf
- International Organization for Chemical Sciences in Development 61 rue de Bruxelles B-5000 Namur Belgium
- Institute of Organic Chemistry, Technische Universität Braunschweig Braunschweig D-38106 Germany
| | - Goverdhan Mehta
- International Organization for Chemical Sciences in Development 61 rue de Bruxelles B-5000 Namur Belgium
- School of Chemistry, University of Hyderabad Hyderabad 500046 India
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14
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Chemistry-informed molecular graph as reaction descriptor for machine-learned retrosynthesis planning. Proc Natl Acad Sci U S A 2022; 119:e2212711119. [PMID: 36191228 PMCID: PMC9564830 DOI: 10.1073/pnas.2212711119] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Infusing "chemical wisdom" should improve the data-driven approaches that rely exclusively on historical synthetic data for automatic retrosynthesis planning. For this purpose, we designed a chemistry-informed molecular graph (CIMG) to describe chemical reactions. A collection of key information that is most relevant to chemical reactions is integrated in CIMG:NMR chemical shifts as vertex features, bond dissociation energies as edge features, and solvent/catalyst information as global features. For any given compound as a target, a product CIMG is generated and exploited by a graph neural network (GNN) model to choose reaction template(s) leading to this product. A reactant CIMG is then inferred and used in two GNN models to select appropriate catalyst and solvent, respectively. Finally, a fourth GNN model compares the two CIMG descriptors to check the plausibility of the proposed reaction. A reaction vector is obtained for every molecule in training these models. The chemical wisdom of reaction propensity contained in the pretrained reaction vectors is exploited to autocategorize molecules/reactions and to accelerate Monte Carlo tree search (MCTS) for multistep retrosynthesis planning. Full synthetic routes with recommended catalysts/solvents are predicted efficiently using this CIMG-based approach.
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15
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Le Pogam P, Papon N, Beniddir MA, Courdavault V. Computer-Assisted Design of Sustainable Syntheses of Pharmaceuticals and Agrochemicals from Industrial Wastes. CHEMSUSCHEM 2022; 15:e202201125. [PMID: 35894947 DOI: 10.1002/cssc.202201125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/24/2022] [Indexed: 06/15/2023]
Abstract
Computer-based strategies vastly enhanced the field of analytical chemistry. The impact of data-driven technologies in shaping organic chemistry strategies long remained comparatively elusive but various tools recently emerged to computationally plan multistep organic syntheses. A recent study elegantly takes benefit of an in-house library of chemical reactions enriched with various metadata to provide numerous, reliable and realistic organic chemistry workflows to structurally-varied drugs of interest, from locally available industrial by-products. The retrieval of the different synthetic pathways and a scoring based on different features, especially comprising sustainability considerations, are also proposed.
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Affiliation(s)
- Pierre Le Pogam
- Équipe Chimie des Substances Naturelles, BioCIS, Université Paris-Saclay, CNRS, 92290, Châtenay-Malabry, France
| | - Nicolas Papon
- Univ Angers, Univ Brest, IRF, SFR ICAT, F-49000, Angers, France
| | - Mehdi A Beniddir
- Équipe Chimie des Substances Naturelles, BioCIS, Université Paris-Saclay, CNRS, 92290, Châtenay-Malabry, France
| | - Vincent Courdavault
- Biomolécules et Biotechnologies Végétales, BBV, EA2106, Université de Tours, 37200, Tours, France
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16
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Singh KS, van der Hooft JJJ, van Wees SCM, Medema MH. Integrative omics approaches for biosynthetic pathway discovery in plants. Nat Prod Rep 2022; 39:1876-1896. [PMID: 35997060 PMCID: PMC9491492 DOI: 10.1039/d2np00032f] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Indexed: 12/13/2022]
Abstract
Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.
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Affiliation(s)
- Kumar Saurabh Singh
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Plant-Microbe Interactions, Institute of Environmental Biology, Utrecht University, The Netherlands.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
- Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Saskia C M van Wees
- Plant-Microbe Interactions, Institute of Environmental Biology, Utrecht University, The Netherlands.
| | - Marnix H Medema
- Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
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17
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Zhou H, Wang Y, Ren Y, Li Z, Kong X, Shao M, Duan H. Plastic Waste Valorization by Leveraging Multidisciplinary Catalytic Technologies. ACS Catal 2022. [DOI: 10.1021/acscatal.2c02775] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Hua Zhou
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
| | - Ye Wang
- Department of Chemistry, Tsinghua University, Beijing 100084, China
| | - Yue Ren
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Zhenhua Li
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Xianggui Kong
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Mingfei Shao
- State Key Laboratory of Chemical Resource Engineering, College of Chemistry, Beijing University of Chemical Technology, Beijing 100029, China
| | - Haohong Duan
- Department of Chemistry, Tsinghua University, Beijing 100084, China
- Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China
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18
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Zuin VG, Kümmerer K. Repurposing chemical waste: Sustainable chemistry for circularity beyond artificial intelligence. Cell 2022; 185:2655-2656. [DOI: 10.1016/j.cell.2022.06.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 06/20/2022] [Accepted: 06/20/2022] [Indexed: 10/17/2022]
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