Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One‐Pot Reactions in the Network of Organic Chemistry

Gothard, Chris M.; Soh, Siowling; Gothard, Nosheen A.; Kowalczyk, Bartłomiej; Wei, Yongliang; Baytekin, Bilge; Grzybowski, Bartosz A.

doi:10.1002/anie.201202155

Cited by 90 publications

(70 citation statements)

References 41 publications

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“…Chemical reaction networks offer help in the design of ‘one-pot’ reactions without the need for isolation, purification and characterization of intermediate structures, and without the production of much chemical waste. Gothard et al (2012) used 8 filters of 86,000 chemical criteria to identify more than 1 million ‘one-pot’ reaction series. The number of possible synthetic pathways can be astronomical having 10 19 routes of just 5 synthetic steps.…”

Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

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Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only gives a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The “central hit strategy” selectively targets central node/edges of the flexible networks of infectious agents or cancer cells to kill them. The “network influence strategy” works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

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Section: The Use Of Molecular Network In Drug Designmentioning

confidence: 99%

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Csermely

Korcsmáros

Kiss

et al. 2013

Pharmacology & Therapeutics

809

661

View full text Add to dashboard Cite

show abstract

“…However, they suffer from several disadvantages: 1) they require knowledge to be manually encoded by experts either directly as reaction rules or indirectly as complex heuristics to extract rules from data, 2) they become complicated to maintain with a growing knowledge base, and 3) they cannot generalise . Furthermore, they will only predict the chemistry encoded within the rules and thus cannot be used to discover novel chemistry …”

Section: Introductionmentioning

confidence: 99%

Modelling Chemical Reasoning to Predict and Invent Reactions

Segler

Waller

2017

Chemistry A European J

179

168

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The ability to reason beyond established knowledge allows organic chemists to solve synthetic problems and invent novel transformations. Herein, we propose a model that mimics chemical reasoning, and formalises reaction prediction as finding missing links in a knowledge graph. We have constructed a knowledge graph containing 14.4 million molecules and 8.2 million binary reactions, which represents the bulk of all chemical reactions ever published in the scientific literature. Our model outperforms a rule-based expert system in the reaction prediction task for 180 000 randomly selected binary reactions. The data-driven model generalises even beyond known reaction types, and is thus capable of effectively (re-)discovering novel transformations (even including transition metal-catalysed reactions). Our model enables computers to infer hypotheses about reactivity and reactions by only considering the intrinsic local structure of the graph and because each single reaction prediction is typically achieved in a sub-second time frame, the model can be used as a high-throughput generator of reaction hypotheses for reaction discovery.

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“…(b) Many different molecules are generated, linked through a reaction network (adapted from [28]). The overall reaction network is similar to in silico networks of reactions in organic chemistry that can be obtained from databases, as shown in (c) where nodes are molecules and connections indicate possible reactions (adapted after [29]). …”

Section: Synthetic Prebiotic Chemistrymentioning

confidence: 90%

Synthetic transitions: towards a new synthesis

Solé

2016

Phil. Trans. R. Soc. B

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One contribution of 13 to a theme issue 'The major synthetic evolutionary transitions'. The evolution of life in our biosphere has been marked by several major innovations. Such major complexity shifts include the origin of cells, genetic codes or multicellularity to the emergence of non-genetic information, language or even consciousness. Understanding the nature and conditions for their rise and success is a major challenge for evolutionary biology. Along with data analysis, phylogenetic studies and dedicated experimental work, theoretical and computational studies are an essential part of this exploration. With the rise of synthetic biology, evolutionary robotics, artificial life and advanced simulations, novel perspectives to these problems have led to a rather interesting scenario, where not only the major transitions can be studied or even reproduced, but even new ones might be potentially identified. In both cases, transitions can be understood in terms of phase transitions, as defined in physics. Such mapping (if correct) would help in defining a general framework to establish a theory of major transitions, both natural and artificial. Here, we review some advances made at the crossroads between statistical physics, artificial life, synthetic biology and evolutionary robotics.This article is part of the themed issue 'The major synthetic evolutionary transitions'.

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Rewiring Chemistry: Algorithmic Discovery and Experimental Validation of One‐Pot Reactions in the Network of Organic Chemistry

Cited by 90 publications

References 41 publications

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Structure and dynamics of molecular networks: A novel paradigm of drug discovery

Modelling Chemical Reasoning to Predict and Invent Reactions

Synthetic transitions: towards a new synthesis

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