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Kouzalis A, Antoniou A, Rossides N, Panaoura R, Yadav P. Advanced technologies and mathematical metacognition: The present and future orientation. Biosystems 2024; 245:105312. [PMID: 39182715 DOI: 10.1016/j.biosystems.2024.105312] [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: 05/10/2024] [Revised: 08/21/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
The intersection of mathematical cognition, metacognition, and advanced technologies presents a frontier with profound implications for human learning and artificial intelligence. This paper traces the historical roots of these concepts from the Pythagoreans and Aristotle to modern cognitive science and explores their relevance to contemporary technological applications. We examine how the Pythagoreans' view of mathematics as fundamental to understanding the universe and Aristotle's contributions to logic and categorization have shaped our current understanding of mathematical cognition and metacognition. The paper investigates the role of Boolean logic in computational processes and its relationship to human logical reasoning, as well as the significance of Bayesian inference and fuzzy logic in modelling uncertainty in human cognition and decision-making. We also explore the emerging field of Chemical Artificial Intelligence and its potential applications. We argue for unifying mathematical metacognition with advanced technologies, including artificial intelligence and robotics, while identifying the multifaceted benefits and challenges of such unification. The present paper examines essential research directions for integrating cognitive sciences and advanced technologies, discussing applications in education, healthcare, and business management. We provide suggestions for developing cognitive robots using specific cognitive tasks and explore the ethical implications of these advancements. Our analysis underscores the need for interdisciplinary collaboration to realize the full potential of this integration while mitigating potential risks.
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Affiliation(s)
| | - Antonios Antoniou
- European University Cyprus, Nicosia, Cyprus; Minjiang University, Fuzhou, China
| | - Nicos Rossides
- European University Cyprus, Nicosia, Cyprus; Minjiang University, Fuzhou, China
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2
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Sultanov A, Crivello JC, Rebafka T, Sokolovska N. Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells. J Chem Inf Model 2023; 63:6986-6997. [PMID: 37947477 DOI: 10.1021/acs.jcim.3c00969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2023]
Abstract
The discovery of new functional and stable materials is a big challenge due to its complexity. This work aims at the generation of new crystal structures with desired properties, such as chemical stability and specified chemical composition, by using machine learning generative models. Compared with the generation of molecules, crystal structures pose new difficulties arising from the periodic nature of the crystal and from the specific symmetry constraints related to the space group. In this work, score-based probabilistic models based on annealed Langevin dynamics, which have shown excellent performance in various applications, are adapted to the task of crystal generation. The novelty of the presented approach resides in the fact that the lattice of the crystal cell is not fixed. During the training of the model, the lattice is learned from the available data, whereas during the sampling of a new chemical structure, two denoising processes are used in parallel to generate the lattice along with the generation of the atomic positions. A multigraph crystal representation is introduced that respects symmetry constraints, yielding computational advantages and a better quality of the sampled structures. We show that our model is capable of generating new candidate structures in any chosen chemical system and crystal group without any additional training. To illustrate the functionality of the proposed method, a comparison of our model to other recent generative models based on descriptor-based metrics is provided.
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Affiliation(s)
- Arsen Sultanov
- Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 rue Henri Dunant, 94320 Thiais, France
| | - Jean-Claude Crivello
- Univ Paris Est Creteil, CNRS, ICMPE, UMR 7182, 2 rue Henri Dunant, 94320 Thiais, France
- CNRS-Saint-Gobain-NIMS, IRL 3629, Laboratory for Innovative Key Materials and Structures (LINK), 1-1 Namiki, 305-0044 Tsukuba, Japan
| | - Tabea Rebafka
- LPSM, Sorbonne Université, Université Paris Cité, CNRS, 75005 Paris, France
- Université Paris-Saclay, INRAE, MaIAGE, 78350 Jouy-en-Josas, France
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3
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Taylor CJ, Felton KC, Wigh D, Jeraal MI, Grainger R, Chessari G, Johnson CN, Lapkin AA. Accelerated Chemical Reaction Optimization Using Multi-Task Learning. ACS CENTRAL SCIENCE 2023; 9:957-968. [PMID: 37252348 PMCID: PMC10214532 DOI: 10.1021/acscentsci.3c00050] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Indexed: 05/31/2023]
Abstract
Functionalization of C-H bonds is a key challenge in medicinal chemistry, particularly for fragment-based drug discovery (FBDD) where such transformations require execution in the presence of polar functionality necessary for protein binding. Recent work has shown the effectiveness of Bayesian optimization (BO) for the self-optimization of chemical reactions; however, in all previous cases these algorithmic procedures have started with no prior information about the reaction of interest. In this work, we explore the use of multitask Bayesian optimization (MTBO) in several in silico case studies by leveraging reaction data collected from historical optimization campaigns to accelerate the optimization of new reactions. This methodology was then translated to real-world, medicinal chemistry applications in the yield optimization of several pharmaceutical intermediates using an autonomous flow-based reactor platform. The use of the MTBO algorithm was shown to be successful in determining optimal conditions of unseen experimental C-H activation reactions with differing substrates, demonstrating an efficient optimization strategy with large potential cost reductions when compared to industry-standard process optimization techniques. Our findings highlight the effectiveness of the methodology as an enabling tool in medicinal chemistry workflows, representing a step-change in the utilization of data and machine learning with the goal of accelerated reaction optimization.
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Affiliation(s)
- Connor J. Taylor
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
| | - Kobi C. Felton
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Daniel Wigh
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
| | - Mohammed I. Jeraal
- Cambridge
Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, 138602, Singapore
| | - Rachel Grainger
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Gianni Chessari
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Christopher N. Johnson
- Astex
Pharmaceuticals, 436 Cambridge Science Park, Milton Road, Cambridge, CB4 0QA, United Kingdom
| | - Alexei A. Lapkin
- Innovation
Centre in Digital Molecular Technologies, Yusuf Hamied Department
of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United
Kingdom
- Department
of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, United Kingdom
- Cambridge
Centre for Advanced Research and Education in Singapore Ltd., 1 Create Way, CREATE Tower #05-05, 138602, Singapore
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4
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Wang L, Song Y, Wang H, Zhang X, Wang M, He J, Li S, Zhang L, Li K, Cao L. Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade. Pharmaceuticals (Basel) 2023; 16:253. [PMID: 37259400 PMCID: PMC9963982 DOI: 10.3390/ph16020253] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 10/13/2023] Open
Abstract
Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Kang Li
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
| | - Lei Cao
- Department of Biostatistics, School of Public Health, Harbin Medical University, Harbin 150081, China
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5
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Harris Y, Sason H, Niezni D, Shamay Y. Automated discovery of nanomaterials via drug aggregation induced emission. Biomaterials 2022; 289:121800. [PMID: 36166893 DOI: 10.1016/j.biomaterials.2022.121800] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 08/30/2022] [Accepted: 09/07/2022] [Indexed: 12/19/2022]
Abstract
Nanoformulations of small molecule drugs are essential to effectively deliver them and treat a wide range of diseases. They are normally complex to develop, lack predictability, and exhibit low drug loading. Recently, nanoparticles made via co-assembly of hydrophobic drugs and organic dyes, exhibited drug-loading of up to 90% with high predictability from the drug structure. However, these particles have relatively short stability and can formulate only a small fraction of the drug space. Here, we developed an automated workflow to synthesize and select novel dye stabilizers, based on their ability to inhibit drug aggregation-induced emission (AIE). We first screened and identified 10 drugs with previously unknown strong AIE activity and exploited this trait to automatically synthesize and select a new ultra-stabilizer named R595. Interestingly, it shares several synthetic similarities and advantages with polydopamine. We found that R595 is superior to myriad types of excipients and solubilizers such as cyclodextrins, poloxamers, albumin, and previously published organic dyes, in both long-term stability and drug compatibility. We investigated the biodistribution, pharmacokinetics, safety and efficacy of the AIEgenic MEK inhibitor trametinib-R595 nanoparticles in vitro and in vivo and demonstrated that they are non-toxic and effective in KRAS driven colon and lung cancer models.
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Affiliation(s)
- Yuval Harris
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Hagit Sason
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Danna Niezni
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
| | - Yosi Shamay
- Faculty of Biomedical Engineering, Technion - Israel Institute of Technology, Haifa, Israel.
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6
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Guo M, Shou W, Makatura L, Erps T, Foshey M, Matusik W. Polygrammar: Grammar for Digital Polymer Representation and Generation. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2101864. [PMID: 35678650 PMCID: PMC9376847 DOI: 10.1002/advs.202101864] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 12/04/2021] [Indexed: 05/22/2023]
Abstract
Polymers are widely studied materials with diverse properties and applications determined by molecular structures. It is essential to represent these structures clearly and explore the full space of achievable chemical designs. However, existing approaches cannot offer comprehensive design models for polymers because of their inherent scale and structural complexity. Here, a parametric, context-sensitive grammar designed specifically for polymers (PolyGrammar) is proposed. Using the symbolic hypergraph representation and 14 simple production rules, PolyGrammar can represent and generate all valid polyurethane structures. An algorithm is presented to translate any polyurethane structure from the popular Simplified Molecular-Input Line-entry System (SMILES) string format into the PolyGrammar representation. The representative power of PolyGrammar is tested by translating a dataset of over 600 polyurethane samples collected from the literature. Furthermore, it is shown that PolyGrammar can be easily extended to other copolymers and homopolymers. By offering a complete, explicit representation scheme and an explainable generative model with validity guarantees, PolyGrammar takes an essential step toward a more comprehensive and practical system for polymer discovery and exploration. As the first bridge between formal languages and chemistry, PolyGrammar also serves as a critical blueprint to inform the design of similar grammars for other chemistries, including organic and inorganic molecules.
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Affiliation(s)
- Minghao Guo
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
- CUHK Multimedia LabThe Chinese University of Hong KongSha TinHong Kong
| | - Wan Shou
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Liane Makatura
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Timothy Erps
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Michael Foshey
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Wojciech Matusik
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyCambridgeMA02139USA
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7
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Park S, Han H, Kim H, Choi S. Machine Learning Applications for Chemical Reactions. Chem Asian J 2022; 17:e202200203. [PMID: 35471772 PMCID: PMC9401034 DOI: 10.1002/asia.202200203] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 04/26/2022] [Indexed: 11/30/2022]
Abstract
Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies.
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Affiliation(s)
- Sanggil Park
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Herim Han
- Digital Bio R&D CenterMediazenSeoul07789Republic of Korea
- Department of Polymer Science and EngineeringDankook UniversityYongin, Gyeonggi16890Republic of Korea
| | - Hyungjun Kim
- Department of ChemistryIncheon Natoinal University and Research Institute of Basic SciencesIncheon22012Republic of Korea
| | - Sunghwan Choi
- Division of National SupercomputingKorea Institute of Science and Technology InformationDaejeon34141Republic of Korea
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8
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Xu Z, Mahadevan R. Efficient Enumeration of Branched Novel Biochemical Pathways Using a Probabilistic Technique. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c02211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhiqing Xu
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
| | - Radhakrishnan Mahadevan
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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9
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Gensch T, Smith SR, Colacot TJ, Timsina YN, Xu G, Glasspoole BW, Sigman MS. Design and Application of a Screening Set for Monophosphine Ligands in Cross-Coupling. ACS Catal 2022. [DOI: 10.1021/acscatal.2c01970] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Affiliation(s)
- Tobias Gensch
- Department of Chemistry, TU Berlin, Straße des 17. Juni 135, Sekr. C2, 10623 Berlin, Germany
| | - Sleight R. Smith
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
| | - Thomas J. Colacot
- MilliporeSigma, 6000 N. Teutonia Ave, Milwaukee, Wisconsin 53209, United States
| | - Yam N. Timsina
- MilliporeSigma, 6000 N. Teutonia Ave, Milwaukee, Wisconsin 53209, United States
| | - Guolin Xu
- MilliporeSigma, 6000 N. Teutonia Ave, Milwaukee, Wisconsin 53209, United States
| | - Ben W. Glasspoole
- MilliporeSigma, 6000 N. Teutonia Ave, Milwaukee, Wisconsin 53209, United States
| | - Matthew S. Sigman
- Department of Chemistry, University of Utah, 315 South 1400 East, Salt Lake City, Utah 84112, United States
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Konan KE, Abollé A, Barré E, Aka EC, Coeffard V, Felpin FX. Developing flow photo-thiol–ene functionalizations of cinchona alkaloids with an autonomous self-optimizing flow reactor. REACT CHEM ENG 2022. [DOI: 10.1039/d1re00509j] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Continuous flow photo-thiol–ene reactions on cinchona alkaloids with a variety of organic thiols have been developed using enabling technologies such as a self-optimizing flow photochemical reactor.
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Affiliation(s)
- Kouakou Eric Konan
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Abollé Abollé
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Elvina Barré
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
| | - Ehu Camille Aka
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
- Laboratoire de Thermodynamique et de Physico-Chimie du Milieu, Université Nangui Abrogoua, 02 BP 801 Abidjan 02, Côte d'Ivoire
| | - Vincent Coeffard
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
| | - François-Xavier Felpin
- CNRS, Université de Nantes, CEISAM UMR 6230, 2 rue de la Houssinière, 44322 Nantes, France
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Izor S, Schantz A, Jawaid A, Grabowski C, Dagher T, Koerner H, Park K, Vaia R. Coexistence and Phase Behavior of Solvent–Polystyrene-Grafted Gold Nanoparticle Systems. Macromolecules 2021. [DOI: 10.1021/acs.macromol.1c01714] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Sarah Izor
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
- UES, Inc., Dayton, Ohio 45432, United States
| | - Allen Schantz
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
| | - Ali Jawaid
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
- UES, Inc., Dayton, Ohio 45432, United States
| | - Chris Grabowski
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
| | - Tony Dagher
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
| | - Hilmar Koerner
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
| | - Kyoungweon Park
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
- UES, Inc., Dayton, Ohio 45432, United States
| | - Richard Vaia
- Materials and Manufacturing Directorate, Air Force Research Laboratory, Wright-Patterson AFB, Ohio 45433-7702, United States
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Xie Y, Zhang C, Deng H, Zheng B, Su JW, Shutt K, Lin J. Accelerate Synthesis of Metal-Organic Frameworks by a Robotic Platform and Bayesian Optimization. ACS APPLIED MATERIALS & INTERFACES 2021; 13:53485-53491. [PMID: 34709793 DOI: 10.1021/acsami.1c16506] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Synthesis of materials with desired structures, e.g., metal-organic frameworks (MOFs), involves optimization of highly complex chemical and reaction spaces due to multiple choices of chemical elements and reaction parameters/routes. Traditionally, realizing such an aim requires rapid screening of these nonlinear spaces by experimental conduction with human intuition, which is quite inefficient and may cause errors or bias. In this work, we report a platform that integrates a synthesis robot with the Bayesian optimization (BO) algorithm to accelerate the synthesis of MOFs. This robotic platform consists of a direct laser writing apparatus, precursor injecting and Joule-heating components. It can automate the MOFs synthesis upon fed reaction parameters that are recommended by the BO algorithm. Without any prior knowledge, this integrated platform continuously improves the crystallinity of ZIF-67, a demo MOF employed in this study, as the number of operation iterations increases. This work represents a methodology enabled by a data-driven synthesis robot, which achieves the goal of material synthesis with targeted structures, thus greatly shortening the reaction time and reducing energy consumption. It can be easily generalized to other material systems, thus paving a new route to the autonomous discovery of a variety of materials in a cost-effective way in the future.
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Affiliation(s)
- Yunchao Xie
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Chi Zhang
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Heng Deng
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Bujingda Zheng
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Jheng-Wun Su
- Department of Physics and Engineering, Slippery Rock University, Slippery Rock, Pennsylvania 16057, United States
| | - Kenyon Shutt
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Jian Lin
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri 65211, United States
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13
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Wang Z, Zhang W, Liu B. Computational Analysis of Synthetic Planning: Past and Future. CHINESE J CHEM 2021. [DOI: 10.1002/cjoc.202100273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Zhuang Wang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Wenhan Zhang
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
| | - Bo Liu
- Key Laboratory of Green Chemistry & Technology of Ministry of Education, College of Chemistry, Sichuan University, 29 Wangjiang Rd., Chengdu, Sichuan 610064 (China) Center for Molecular Discovery, Department of Chemistry, Boston University, 590 Commonwealth Ave., Boston, Massachusetts 02215, United States cCurrent Address: One Amgen Center Dr. Amgen Inc., Thousand Oaks California 91320 United States
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14
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Machine learning modelling of chemical reaction characteristics: yesterday, today, tomorrow. MENDELEEV COMMUNICATIONS 2021. [DOI: 10.1016/j.mencom.2021.11.003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Hammer AS, Leonov AI, Bell NL, Cronin L. Chemputation and the Standardization of Chemical Informatics. JACS AU 2021; 1:1572-1587. [PMID: 34723260 PMCID: PMC8549037 DOI: 10.1021/jacsau.1c00303] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Indexed: 05/11/2023]
Abstract
The explosion in the use of machine learning for automated chemical reaction optimization is gathering pace. However, the lack of a standard architecture that connects the concept of chemical transformations universally to software and hardware provides a barrier to using the results of these optimizations and could cause the loss of relevant data and prevent reactions from being reproducible or unexpected findings verifiable or explainable. In this Perspective, we describe how the development of the field of digital chemistry or chemputation, that is the universal code-enabled control of chemical reactions using a standard language and ontology, will remove these barriers allowing users to focus on the chemistry and plug in algorithms according to the problem space to be explored or unit function to be optimized. We describe a standard hardware (the chemical processing programming architecture-the ChemPU) to encompass all chemical synthesis, an approach which unifies all chemistry automation strategies, from solid-phase peptide synthesis, to HTE flow chemistry platforms, while at the same time establishing a publication standard so that researchers can exchange chemical code (χDL) to ensure reproducibility and interoperability. Not only can a vast range of different chemistries be plugged into the hardware, but the ever-expanding developments in software and algorithms can also be accommodated. These technologies, when combined will allow chemistry, or chemputation, to follow computation-that is the running of code across many different types of capable hardware to get the same result every time with a low error rate.
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16
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Sicho M, Liu X, Svozil D, van Westen GJP. GenUI: interactive and extensible open source software platform for de novo molecular generation and cheminformatics. J Cheminform 2021; 13:73. [PMID: 34563271 PMCID: PMC8465716 DOI: 10.1186/s13321-021-00550-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 09/05/2021] [Indexed: 03/05/2023] Open
Abstract
Many contemporary cheminformatics methods, including computer-aided de novo drug design, hold promise to significantly accelerate and reduce the cost of drug discovery. Thanks to this attractive outlook, the field has thrived and in the past few years has seen an especially significant growth, mainly due to the emergence of novel methods based on deep neural networks. This growth is also apparent in the development of novel de novo drug design methods with many new generative algorithms now available. However, widespread adoption of new generative techniques in the fields like medicinal chemistry or chemical biology is still lagging behind the most recent developments. Upon taking a closer look, this fact is not surprising since in order to successfully integrate the most recent de novo drug design methods in existing processes and pipelines, a close collaboration between diverse groups of experimental and theoretical scientists needs to be established. Therefore, to accelerate the adoption of both modern and traditional de novo molecular generators, we developed Generator User Interface (GenUI), a software platform that makes it possible to integrate molecular generators within a feature-rich graphical user interface that is easy to use by experts of diverse backgrounds. GenUI is implemented as a web service and its interfaces offer access to cheminformatics tools for data preprocessing, model building, molecule generation, and interactive chemical space visualization. Moreover, the platform is easy to extend with customizable frontend React.js components and backend Python extensions. GenUI is open source and a recently developed de novo molecular generator, DrugEx, was integrated as a proof of principle. In this work, we present the architecture and implementation details of GenUI and discuss how it can facilitate collaboration in the disparate communities interested in de novo molecular generation and computer-aided drug discovery.
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Affiliation(s)
- M. Sicho
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
| | - X. Liu
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
| | - D. Svozil
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Department of Informatics and Chemistry, Faculty of Chemical Technology, University of Chemistry and Technology Prague, Technická 5, 166 28 Prague, Czech Republic
- CZ-OPENSCREEN: National Infrastructure for Chemical Biology, Institute of Molecular Genetics of the ASCR, v. v. i., Vídeňská 1083, 142 20 Prague 4, Czech Republic
| | - G. J. P. van Westen
- Computational Drug Discovery, Drug Discovery and Safety, Leiden Academic Centre for Drug Research, Einsteinweg 55, Leiden, The Netherlands
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17
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Vasudevan RK, Kelley KP, Hinkle J, Funakubo H, Jesse S, Kalinin SV, Ziatdinov M. Autonomous Experiments in Scanning Probe Microscopy and Spectroscopy: Choosing Where to Explore Polarization Dynamics in Ferroelectrics. ACS NANO 2021; 15:11253-11262. [PMID: 34228427 DOI: 10.1021/acsnano.0c10239] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Polarization dynamics in ferroelectric materials are explored via automated experiment in piezoresponse force microscopy/spectroscopy (PFM/S). A Bayesian optimization (BO) framework for imaging is developed, and its performance for a variety of acquisition and pathfinding functions is explored using previously acquired data. The optimized algorithm is then deployed on an operational scanning probe microscope (SPM) for finding areas of large electromechanical response in a thin film of PbTiO3, with results showing that, with just 20% of the area sampled, most high-response clusters were captured. This approach can allow performing more complex spectroscopies in SPM that were previously not possible due to time constraints and sample stability. Improvements to the framework to enable the incorporation of more prior information and improve efficiency further are modeled and discussed.
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Affiliation(s)
- Rama K Vasudevan
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Kyle P Kelley
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Jacob Hinkle
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Hiroshi Funakubo
- Department of Material Science and Engineering, Tokyo Institute of Technology, Yokohama, 226-8502, Japan
| | - Stephen Jesse
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Sergei V Kalinin
- Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Maxim Ziatdinov
- Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States
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18
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Cao L, Russo D, Lapkin AA. Automated robotic platforms in design and development of formulations. AIChE J 2021. [DOI: 10.1002/aic.17248] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Liwei Cao
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
| | - Danilo Russo
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
| | - Alexei A. Lapkin
- Department of Chemical Engineering and Biotechnology University of Cambridge Cambridge UK
- Cambridge Centre for Advanced Research and Education in Singapore, CARES Ltd. Singapore
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19
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Self-Driving Laboratories for Development of New Functional Materials and Optimizing Known Reactions. NANOMATERIALS 2021; 11:nano11030619. [PMID: 33801472 PMCID: PMC8000792 DOI: 10.3390/nano11030619] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/25/2021] [Accepted: 02/25/2021] [Indexed: 12/17/2022]
Abstract
Innovations often play an essential role in the acceleration of the new functional materials discovery. The success and applicability of the synthesis results with new chemical compounds and materials largely depend on the previous experience of the researcher himself and the modernity of the equipment used in the laboratory. Artificial intelligence (AI) technologies are the next step in developing the solution for practical problems in science, including the development of new materials. Those technologies go broadly beyond the borders of a computer science branch and give new insights and practical possibilities within the far areas of expertise and chemistry applications. One of the attractive challenges is an automated new functional material synthesis driven by AI. However, while having many years of hands-on experience, chemistry specialists have a vague picture of AI. To strengthen and underline AI's role in materials discovery, a short introduction is given to the essential technologies, and the machine learning process is explained. After this review, this review summarizes the recent studies of new strategies that help automate and accelerate the development of new functional materials. Moreover, automatized laboratories' self-driving cycle could benefit from using AI algorithms to optimize new functional nanomaterials' synthetic routes. Despite the fact that such technologies will shape material science in the nearest future, we note the intelligent use of algorithms and automation is required for novel discoveries.
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20
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Wang Z, Zhao W, Hao G, Song B. Mapping the resources and approaches facilitating computer-aided synthesis planning. Org Chem Front 2021. [DOI: 10.1039/d0qo00946f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Computer-aided synthesis planning could facilitate organic synthesis study and relieve chemists of manual tasks. Artificial intelligence and deep learning would be useful for the development of computer-aided synthesis planning.
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Affiliation(s)
- Zheng Wang
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering
- Key Laboratory of Green Pesticide and Agricultural Bioengineering
- Ministry of Education
- Center for Research and Development of Fine Chemicals
- Guizhou University
| | - Wei Zhao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering
- Key Laboratory of Green Pesticide and Agricultural Bioengineering
- Ministry of Education
- Center for Research and Development of Fine Chemicals
- Guizhou University
| | - Gefei Hao
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering
- Key Laboratory of Green Pesticide and Agricultural Bioengineering
- Ministry of Education
- Center for Research and Development of Fine Chemicals
- Guizhou University
| | - Baoan Song
- State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering
- Key Laboratory of Green Pesticide and Agricultural Bioengineering
- Ministry of Education
- Center for Research and Development of Fine Chemicals
- Guizhou University
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21
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Kell DB, Samanta S, Swainston N. Deep learning and generative methods in cheminformatics and chemical biology: navigating small molecule space intelligently. Biochem J 2020; 477:4559-4580. [PMID: 33290527 PMCID: PMC7733676 DOI: 10.1042/bcj20200781] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2020] [Revised: 11/11/2020] [Accepted: 11/12/2020] [Indexed: 12/15/2022]
Abstract
The number of 'small' molecules that may be of interest to chemical biologists - chemical space - is enormous, but the fraction that have ever been made is tiny. Most strategies are discriminative, i.e. have involved 'forward' problems (have molecule, establish properties). However, we normally wish to solve the much harder generative or inverse problem (describe desired properties, find molecule). 'Deep' (machine) learning based on large-scale neural networks underpins technologies such as computer vision, natural language processing, driverless cars, and world-leading performance in games such as Go; it can also be applied to the solution of inverse problems in chemical biology. In particular, recent developments in deep learning admit the in silico generation of candidate molecular structures and the prediction of their properties, thereby allowing one to navigate (bio)chemical space intelligently. These methods are revolutionary but require an understanding of both (bio)chemistry and computer science to be exploited to best advantage. We give a high-level (non-mathematical) background to the deep learning revolution, and set out the crucial issue for chemical biology and informatics as a two-way mapping from the discrete nature of individual molecules to the continuous but high-dimensional latent representation that may best reflect chemical space. A variety of architectures can do this; we focus on a particular type known as variational autoencoders. We then provide some examples of recent successes of these kinds of approach, and a look towards the future.
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Affiliation(s)
- Douglas B. Kell
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
- Novo Nordisk Foundation Centre for Biosustainability, Technical University of Denmark, Building 220, Kemitorvet, 2800 Kgs. Lyngby, Denmark
| | - Soumitra Samanta
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
| | - Neil Swainston
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular and Integrative Biology, Faculty of Health and Life Sciences, University of Liverpool, Crown St, Liverpool L69 7ZB, U.K
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22
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Siebert M, Storch G, Trapp O. A Fast and Reliable Screening Setup for Homogeneous Catalysis with Gaseous Reactants at Extreme Temperatures and Pressures. Org Process Res Dev 2020. [DOI: 10.1021/acs.oprd.0c00192] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Max Siebert
- Department Chemie, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 München, Germany
| | - Golo Storch
- Department Chemie and Catalysis Research Center (CRC), Technische Universität München, Lichtenbergstraße 4, 85747 Garching, Germany
| | - Oliver Trapp
- Department Chemie, Ludwig-Maximilians-Universität München, Butenandtstraße 5-13, 81377 München, Germany
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23
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24
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Grizou J, Points LJ, Sharma A, Cronin L. A curious formulation robot enables the discovery of a novel protocell behavior. SCIENCE ADVANCES 2020; 6:eaay4237. [PMID: 32064348 PMCID: PMC6994213 DOI: 10.1126/sciadv.aay4237] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Accepted: 11/20/2019] [Indexed: 05/11/2023]
Abstract
We describe a chemical robotic assistant equipped with a curiosity algorithm (CA) that can efficiently explore the states a complex chemical system can exhibit. The CA-robot is designed to explore formulations in an open-ended way with no explicit optimization target. By applying the CA-robot to the study of self-propelling multicomponent oil-in-water protocell droplets, we are able to observe an order of magnitude more variety in droplet behaviors than possible with a random parameter search and given the same budget. We demonstrate that the CA-robot enabled the observation of a sudden and highly specific response of droplets to slight temperature changes. Six modes of self-propelled droplet motion were identified and classified using a time-temperature phase diagram and probed using a variety of techniques including NMR. This work illustrates how CAs can make better use of a limited experimental budget and significantly increase the rate of unpredictable observations, leading to new discoveries with potential applications in formulation chemistry.
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25
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Haas CP, Biesenroth S, Buckenmaier S, van de Goor T, Tallarek U. Automated generation of photochemical reaction data by transient flow experiments coupled with online HPLC analysis. REACT CHEM ENG 2020. [DOI: 10.1039/d0re00066c] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Competing homo- and crossdimerization reactions between coumarin and 1-methyl-2-quinolinone are investigated by transient continuous-flow experiments combined with online HPLC, enabling the generation and acquisition of large reaction data sets.
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Affiliation(s)
- Christian P. Haas
- Department of Chemistry
- Philipps-Universität Marburg
- 35032 Marburg
- Germany
| | - Simon Biesenroth
- Department of Chemistry
- Philipps-Universität Marburg
- 35032 Marburg
- Germany
| | | | - Tom van de Goor
- Agilent Technologies R&D and Marketing GmbH & Co. KG
- 76337 Waldbronn
- Germany
| | - Ulrich Tallarek
- Department of Chemistry
- Philipps-Universität Marburg
- 35032 Marburg
- Germany
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26
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Gromski PS, Granda JM, Cronin L. Universal Chemical Synthesis and Discovery with ‘The Chemputer’. TRENDS IN CHEMISTRY 2020. [DOI: 10.1016/j.trechm.2019.07.004] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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27
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Xie Y, Zhang C, Hu X, Zhang C, Kelley SP, Atwood JL, Lin J. Machine Learning Assisted Synthesis of Metal–Organic Nanocapsules. J Am Chem Soc 2019; 142:1475-1481. [DOI: 10.1021/jacs.9b11569] [Citation(s) in RCA: 54] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Yunchao Xie
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Chen Zhang
- Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States
| | - Xiangquan Hu
- Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States
| | - Chi Zhang
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
| | - Steven P. Kelley
- Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States
| | - Jerry L. Atwood
- Department of Chemistry, University of Missouri, Columbia, Missouri 65211, United States
| | - Jian Lin
- Department of Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211, United States
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri 65211, United States
- Department of Physics and Astronomy, University of Missouri, Columbia, Missouri 65211, United States
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28
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Teders M, Bernard S, Gottschalk K, Schwarz JL, Standley EA, Decuypere E, Daniliuc CG, Audisio D, Taran F, Glorius F. Accelerated Discovery in Photocatalysis by a Combined Screening Approach Involving MS Tags. Org Lett 2019; 21:9747-9752. [PMID: 31746215 DOI: 10.1021/acs.orglett.9b03936] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Herein we report on the development of an MS tag screening strategy that accelerates the discovery of photocatalytic reactions. By efficiently combining mechanism- and reaction-based screening dimensions, the respective advantages of each strategy were retained, whereas the drawbacks inherent to each screening approach could be eliminated. Applying this approach led to the discovery of a mild photosensitized decarboxylative hydrazide synthesis from mesoionic sydnones and carboxylic acids as starting materials.
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Affiliation(s)
- Michael Teders
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
| | - Sabrina Bernard
- Service de Chimie Bio-organique et Marquage, DRF-JOLIOT-SCBM, CEA , Université Paris-Saclay , 91191 Gif-sur-Yvette , France
| | - Karin Gottschalk
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
| | - J Luca Schwarz
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
| | - Eric A Standley
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
| | - Elodie Decuypere
- Service de Chimie Bio-organique et Marquage, DRF-JOLIOT-SCBM, CEA , Université Paris-Saclay , 91191 Gif-sur-Yvette , France
| | - Constantin G Daniliuc
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
| | - Davide Audisio
- Service de Chimie Bio-organique et Marquage, DRF-JOLIOT-SCBM, CEA , Université Paris-Saclay , 91191 Gif-sur-Yvette , France
| | - Frédéric Taran
- Service de Chimie Bio-organique et Marquage, DRF-JOLIOT-SCBM, CEA , Université Paris-Saclay , 91191 Gif-sur-Yvette , France
| | - Frank Glorius
- Organisch-Chemisches Institut , Westfälische Wilhelms-Universität Münster , Corrensstraße 40 , 48149 Münster , Germany
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29
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Rodrigues T. The good, the bad, and the ugly in chemical and biological data for machine learning. DRUG DISCOVERY TODAY. TECHNOLOGIES 2019; 32-33:3-8. [PMID: 33386092 PMCID: PMC7382642 DOI: 10.1016/j.ddtec.2020.07.001] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/08/2020] [Accepted: 07/09/2020] [Indexed: 02/05/2023]
Abstract
Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid progression of development pipelines while reshaping how fundamental and clinical research is conducted. By taking advantage of the ever-growing wealth of publicly available and proprietary data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses. Hitherto unknown data patterns may guide and prioritize experiments, and augment expert intuition. Therefore, data is a key component in the model building workflow. Herein, I aim to discuss types of chemical and biological data according to their quality and reemphasize general recommendations for their use in ML/AI.
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Affiliation(s)
- Tiago Rodrigues
- Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Av Prof Egaz Moniz, 1649-028 Lisboa, Portugal; Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto 1649-003, Lisboa, Portugal.
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30
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Aka EC, Wimmer E, Barré E, Vasudevan N, Cortés-Borda D, Ekou T, Ekou L, Rodriguez-Zubiri M, Felpin FX. Reconfigurable Flow Platform for Automated Reagent Screening and Autonomous Optimization for Bioinspired Lignans Synthesis. J Org Chem 2019; 84:14101-14112. [PMID: 31568728 DOI: 10.1021/acs.joc.9b02263] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Naturally occurring benzoxanthenones, which belong to the vast family of lignans, are promising biologically relevant targets. They are biosynthetically produced by the oxidative dimerization of 2-propenyl phenols. In this manuscript, we disclose a powerful automated flow-based strategy for identifying and optimizing a cobalt-catalyzed oxidizing system for the bioinspired dimerization of 2-propenyl phenols. We designed a reconfigurable flow reactor associating online monitoring and process control instrumentation. Our machine was first configured as an automated screening platform to evaluate a matrix of 4 catalysts (plus the blank) and 5 oxidants (plus the blank) at two different temperatures, resulting in an array of 50 reactions. The automated screening was conducted on micromole scale at a rate of one fully characterized reaction every 26 min. After having identified the most promising cobalt-catalyzed oxidizing system, the automated screening platform was straightforwardly reconfigured to an autonomous self-optimizing flow reactor by implementation of an optimization algorithm in the closed-loop system. The optimization campaign allowed the determination of very effective experimental conditions in a limited number of experiments, which allowed us to prepare the natural products carpanone and polemannone B as well as synthetic analogues.
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Affiliation(s)
- Ehu Camille Aka
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Eric Wimmer
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Elvina Barré
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Natarajan Vasudevan
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Daniel Cortés-Borda
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - Tchirioua Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Lynda Ekou
- Université Nangui Abrogoua , Laboratoire de Thermodynamique et de Physico-Chimie du Milieu , 02 BP 801 Abidjan 02 , Côte d'Ivoire
| | - Mireia Rodriguez-Zubiri
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
| | - François-Xavier Felpin
- Université de Nantes , CEISAM, CNRS UMR 6230 , 2 rue de la Houssinière , 44322 Cedex 3 Nantes , France
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31
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Passian A, Imam N. Nanosystems, Edge Computing, and the Next Generation Computing Systems. SENSORS (BASEL, SWITZERLAND) 2019; 19:E4048. [PMID: 31546907 PMCID: PMC6767340 DOI: 10.3390/s19184048] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 09/11/2019] [Accepted: 09/16/2019] [Indexed: 12/24/2022]
Abstract
It is widely recognized that nanoscience and nanotechnology and their subfields, such as nanophotonics, nanoelectronics, and nanomechanics, have had a tremendous impact on recent advances in sensing, imaging, and communication, with notable developments, including novel transistors and processor architectures. For example, in addition to being supremely fast, optical and photonic components and devices are capable of operating across multiple orders of magnitude length, power, and spectral scales, encompassing the range from macroscopic device sizes and kW energies to atomic domains and single-photon energies. The extreme versatility of the associated electromagnetic phenomena and applications, both classical and quantum, are therefore highly appealing to the rapidly evolving computing and communication realms, where innovations in both hardware and software are necessary to meet the growing speed and memory requirements. Development of all-optical components, photonic chips, interconnects, and processors will bring the speed of light, photon coherence properties, field confinement and enhancement, information-carrying capacity, and the broad spectrum of light into the high-performance computing, the internet of things, and industries related to cloud, fog, and recently edge computing. Conversely, owing to their extraordinary properties, 0D, 1D, and 2D materials are being explored as a physical basis for the next generation of logic components and processors. Carbon nanotubes, for example, have been recently used to create a new processor beyond proof of principle. These developments, in conjunction with neuromorphic and quantum computing, are envisioned to maintain the growth of computing power beyond the projected plateau for silicon technology. We survey the qualitative figures of merit of technologies of current interest for the next generation computing with an emphasis on edge computing.
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Affiliation(s)
- Ali Passian
- Computing & Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
| | - Neena Imam
- Computing & Computational Sciences Directorate, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA.
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32
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Jia X, Lynch A, Huang Y, Danielson M, Lang'at I, Milder A, Ruby AE, Wang H, Friedler SA, Norquist AJ, Schrier J. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature 2019; 573:251-255. [PMID: 31511682 DOI: 10.1038/s41586-019-1540-5] [Citation(s) in RCA: 75] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2018] [Accepted: 07/10/2019] [Indexed: 01/29/2023]
Abstract
Most chemical experiments are planned by human scientists and therefore are subject to a variety of human cognitive biases1, heuristics2 and social influences3. These anthropogenic chemical reaction data are widely used to train machine-learning models4 that are used to predict organic5 and inorganic6,7 syntheses. However, it is known that societal biases are encoded in datasets and are perpetuated in machine-learning models8. Here we identify as-yet-unacknowledged anthropogenic biases in both the reagent choices and reaction conditions of chemical reaction datasets using a combination of data mining and experiments. We find that the amine choices in the reported crystal structures of hydrothermal synthesis of amine-templated metal oxides9 follow a power-law distribution in which 17% of amine reactants occur in 79% of reported compounds, consistent with distributions in social influence models10-12. An analysis of unpublished historical laboratory notebook records shows similarly biased distributions of reaction condition choices. By performing 548 randomly generated experiments, we demonstrate that the popularity of reactants or the choices of reaction conditions are uncorrelated to the success of the reaction. We show that randomly generated experiments better illustrate the range of parameter choices that are compatible with crystal formation. Machine-learning models that we train on a smaller randomized reaction dataset outperform models trained on larger human-selected reaction datasets, demonstrating the importance of identifying and addressing anthropogenic biases in scientific data.
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Affiliation(s)
- Xiwen Jia
- Department of Chemistry, Haverford College, Haverford, PA, USA
| | - Allyson Lynch
- Department of Chemistry, Haverford College, Haverford, PA, USA
| | - Yuheng Huang
- Department of Chemistry, Haverford College, Haverford, PA, USA
| | | | | | | | - Aaron E Ruby
- Department of Chemistry, Haverford College, Haverford, PA, USA
| | - Hao Wang
- Department of Chemistry, Haverford College, Haverford, PA, USA
| | | | | | - Joshua Schrier
- Department of Chemistry, Haverford College, Haverford, PA, USA. .,Department of Chemistry, Fordham University, The Bronx, New York, NY, USA.
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de Almeida AF, Moreira R, Rodrigues T. Synthetic organic chemistry driven by artificial intelligence. Nat Rev Chem 2019. [DOI: 10.1038/s41570-019-0124-0] [Citation(s) in RCA: 111] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Fujinami M, Seino J, Nukazawa T, Ishida S, Iwamoto T, Nakai H. Virtual Reaction Condition Optimization based on Machine Learning for a Small Number of Experiments in High-dimensional Continuous and Discrete Variables. CHEM LETT 2019. [DOI: 10.1246/cl.190267] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022]
Affiliation(s)
- Mikito Fujinami
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
| | - Junji Seino
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- PRESTO, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
| | - Takumi Nukazawa
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Shintaro Ishida
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Takeaki Iwamoto
- Department of Chemistry, Graduate School of Science, Tohoku University, Aoba-ku, Sendai, Miyagi 980-8578, Japan
| | - Hiromi Nakai
- Department of Chemistry and Biochemistry, School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan
- Element Strategy Initiative for Catalysts and Batteries (ESICB), Kyoto University, Katsura, Kyoto 615-8520, Japan
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Friederich P, Fediai A, Kaiser S, Konrad M, Jung N, Wenzel W. Toward Design of Novel Materials for Organic Electronics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2019; 31:e1808256. [PMID: 31012166 DOI: 10.1002/adma.201808256] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2018] [Indexed: 06/09/2023]
Abstract
Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large-area devices, and thin-film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials.
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Affiliation(s)
- Pascal Friederich
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
- Department of Chemistry, University of Toronto, 80 St. George Street, M5S 3H6, Toronto, Ontario, Canada
| | - Artem Fediai
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Simon Kaiser
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Manuel Konrad
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
| | - Nicole Jung
- Institute of Organic Chemistry (IOC), Karlsruhe Institute of Technology (KIT), Fritz-Haber-Weg 6, 76131, Karlsruhe, Germany
| | - Wolfgang Wenzel
- Institute of Nanotechnology (INT), Karlsruhe Institute of Technology (KIT), Hermann-von-Helmholtz-Platz 1, 76344, Eggenstein-Leopoldshafen, Germany
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Häse F, Roch LM, Aspuru-Guzik A. Next-Generation Experimentation with Self-Driving Laboratories. TRENDS IN CHEMISTRY 2019. [DOI: 10.1016/j.trechm.2019.02.007] [Citation(s) in RCA: 114] [Impact Index Per Article: 22.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Isbrandt ES, Sullivan RJ, Newman SG. High Throughput Strategies for the Discovery and Optimization of Catalytic Reactions. Angew Chem Int Ed Engl 2019; 58:7180-7191. [DOI: 10.1002/anie.201812534] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Indexed: 12/29/2022]
Affiliation(s)
- Eric S. Isbrandt
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Ryan J. Sullivan
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
| | - Stephen G. Newman
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Canada
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Schneider G, Clark DE. Automated De Novo Drug Design: Are We Nearly There Yet? Angew Chem Int Ed Engl 2019; 58:10792-10803. [PMID: 30730601 DOI: 10.1002/anie.201814681] [Citation(s) in RCA: 78] [Impact Index Per Article: 15.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2019] [Indexed: 11/09/2022]
Abstract
Medicinal chemistry and, in particular, drug design have often been perceived as more of an art than a science. The many unknowns of human disease and the sheer complexity of chemical space render decision making in medicinal chemistry exceptionally demanding. Computational models can assist the medicinal chemist in this endeavour. Provided here is an overview of recent examples of automated de novo molecular design, a discussion of the concepts and computational approaches involved, and the daring prediction of some of the possibilities and limitations of drug design using machine intelligence.
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Affiliation(s)
- Gisbert Schneider
- ETH Zurich, Department of Chemistry and Applied Biosciences, RETHINK, Vladimir-Prelog-Weg 4, 8093, Zurich, Switzerland
| | - David E Clark
- Charles River, 6-9 Spire Green Centre, Harlow, Essex, CM19 5TR, UK
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Schneider G, Clark DE. Automated De Novo Drug Design: Are We Nearly There Yet? Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201814681] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Gisbert Schneider
- ETH ZurichDepartment of Chemistry and Applied Biosciences, RETHINK Vladimir-Prelog-Weg 4 8093 Zurich Switzerland
| | - David E. Clark
- Charles River 6–9 Spire Green Centre Harlow Essex CM19 5TR UK
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Isbrandt ES, Sullivan RJ, Newman SG. Hochdurchsatzstrategien zur Entdeckung und Optimierung katalytischer Reaktionen. Angew Chem Int Ed Engl 2019. [DOI: 10.1002/ange.201812534] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Eric S. Isbrandt
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
| | - Ryan J. Sullivan
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
| | - Stephen G. Newman
- Centre for Catalysis Research and InnovationDepartment of Chemistry and Biomolecular SciencesUniversity of Ottawa 10 Marie-Curie Ottawa Ontario K1N 6N5 Kanada
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Harnessing liquid-in-liquid printing and micropatterned substrates to fabricate 3-dimensional all-liquid fluidic devices. Nat Commun 2019; 10:1095. [PMID: 30842556 PMCID: PMC6403306 DOI: 10.1038/s41467-019-09042-y] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/06/2019] [Indexed: 11/08/2022] Open
Abstract
Systems comprised of immiscible liquids held in non-equilibrium shapes by the interfacial assembly and jamming of nanoparticle-polymer surfactants have significant potential to advance catalysis, chemical separations, energy storage and conversion. Spatially directing functionality within them and coupling processes in both phases remains a challenge. Here, we exploit nanoclay-polymer surfactant assemblies at an oil-water interface to produce a semi-permeable membrane between the liquids, and from them all-liquid fluidic devices with bespoke properties. Flow channels are fabricated using micropatterned 2D substrates and liquid-in-liquid 3D printing. The anionic walls of the device can be functionalized with cationic small molecules, enzymes, and colloidal nanocrystal catalysts. Multi-step chemical transformations can be conducted within the channels under flow, as can selective mass transport across the liquid-liquid interface for in-line separations. These all-liquid systems become automated using pumps, detectors, and control systems, revealing a latent ability for chemical logic and learning.
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42
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Bedermann AA, McTeague TA, Jamison TF. Automated On-Demand Titration of Organometallic Reagents in Continuous Flow. Org Process Res Dev 2019. [DOI: 10.1021/acs.oprd.8b00434] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Aaron A. Bedermann
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - T. Andrew McTeague
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Timothy F. Jamison
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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Moosavi SM, Chidambaram A, Talirz L, Haranczyk M, Stylianou KC, Smit B. Capturing chemical intuition in synthesis of metal-organic frameworks. Nat Commun 2019; 10:539. [PMID: 30710082 PMCID: PMC6358622 DOI: 10.1038/s41467-019-08483-9] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/07/2019] [Indexed: 12/21/2022] Open
Abstract
We report a methodology using machine learning to capture chemical intuition from a set of (partially) failed attempts to synthesize a metal-organic framework. We define chemical intuition as the collection of unwritten guidelines used by synthetic chemists to find the right synthesis conditions. As (partially) failed experiments usually remain unreported, we have reconstructed a typical track of failed experiments in a successful search for finding the optimal synthesis conditions that yields HKUST-1 with the highest surface area reported to date. We illustrate the importance of quantifying this chemical intuition for the synthesis of novel materials.
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Affiliation(s)
- Seyed Mohamad Moosavi
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland
| | - Arunraj Chidambaram
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland
| | - Leopold Talirz
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland
- Theory and Simulation of Materials (THEOS), Faculté des Sciences et Techniques de l'Ingénieur, École Polytechnique Fédérale de Lausanne (EPFL), Station 9, CH-1015, Lausanne, Switzerland
| | - Maciej Haranczyk
- IMDEA Materials Institute, C/Eric Kandel 2, 28906, Getafe, Madrid, Spain
| | - Kyriakos C Stylianou
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland
| | - Berend Smit
- Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingénierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne (EPFL), Rue de l'Industrie 17, CH-1951, Sion, Switzerland.
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Clayton AD, Manson JA, Taylor CJ, Chamberlain TW, Taylor BA, Clemens G, Bourne RA. Algorithms for the self-optimisation of chemical reactions. REACT CHEM ENG 2019. [DOI: 10.1039/c9re00209j] [Citation(s) in RCA: 60] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Self-optimising chemical systems have experienced a growing momentum in recent years. Herein, we review algorithms used for the self-optimisation of chemical reactions in an accessible way for the general chemist.
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Affiliation(s)
- Adam D. Clayton
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Jamie A. Manson
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Connor J. Taylor
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | - Thomas W. Chamberlain
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
| | | | | | - Richard A. Bourne
- Institute of Process Research and Development
- School of Chemistry & School of Chemical and Process Engineering
- University of Leeds
- UK
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Ferguson AL. ACS Central Science Virtual Issue on Machine Learning. ACS CENTRAL SCIENCE 2018; 4:938-941. [PMID: 30159387 PMCID: PMC6107860 DOI: 10.1021/acscentsci.8b00528] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
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