Pub Date : 2021-04-03DOI: 10.1080/0889311X.2021.1978079
A. Bacchi, P. P. Mazzeo
Cocrystallization is an extensively used method in Crystal Engineering for tuning the properties of target compounds by pairing them with ad-hoc selected molecular partners (i.e. coformers) in a stoichiometric ratio within the same crystal structure. The formation of a new intermolecular network significantly alters the physical–chemical properties of the final material, becoming crucial for target applications such as pharmaceutical, agrochemical and nuctraceutical where cocrystals are largely investigated. Although, the majority of the cocrystals reported in the literature so far are generally made of coformers which are solid at room temperature, there is no restriction in using liquid or low melting compounds as a coformer. This contribution aims at reviewing some significant cases and applications where cocrystallization is used to stabilize liquid ingredients, that are generally poorly stable and their manufacturing, transportation, and storage conditions present considerable environmental, logistical, and cost-related challenges.
{"title":"Cocrystallization as a tool to stabilize liquid active ingredients","authors":"A. Bacchi, P. P. Mazzeo","doi":"10.1080/0889311X.2021.1978079","DOIUrl":"https://doi.org/10.1080/0889311X.2021.1978079","url":null,"abstract":"Cocrystallization is an extensively used method in Crystal Engineering for tuning the properties of target compounds by pairing them with ad-hoc selected molecular partners (i.e. coformers) in a stoichiometric ratio within the same crystal structure. The formation of a new intermolecular network significantly alters the physical–chemical properties of the final material, becoming crucial for target applications such as pharmaceutical, agrochemical and nuctraceutical where cocrystals are largely investigated. Although, the majority of the cocrystals reported in the literature so far are generally made of coformers which are solid at room temperature, there is no restriction in using liquid or low melting compounds as a coformer. This contribution aims at reviewing some significant cases and applications where cocrystallization is used to stabilize liquid ingredients, that are generally poorly stable and their manufacturing, transportation, and storage conditions present considerable environmental, logistical, and cost-related challenges.","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"102 - 123"},"PeriodicalIF":3.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47259296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Powering the U.S. army of the future","authors":"E. Ferg","doi":"10.17226/26052","DOIUrl":"https://doi.org/10.17226/26052","url":null,"abstract":"","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"130 - 132"},"PeriodicalIF":3.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46547914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-03DOI: 10.1080/0889311X.2021.1982914
M. Vollmar, G. Evans
After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography.
{"title":"Machine learning applications in macromolecular X-ray crystallography","authors":"M. Vollmar, G. Evans","doi":"10.1080/0889311X.2021.1982914","DOIUrl":"https://doi.org/10.1080/0889311X.2021.1982914","url":null,"abstract":"After more than half a century of evolution, machine learning and artificial intelligence, in general, are entering a truly exciting era of broad application in commercial and research sectors. In X-ray crystallography, and its application to structural biology, machine learning is finding a home within expert and automated systems, is forecasting experiment and data analysis outcomes, is predicting whether crystals can be grown and even generating macromolecular structures. This review provides a historical perspective on AI and machine learning, offers an introduction and guide to its application in crystallography and concludes with topical examples of how it is currently influencing macromolecular crystallography.","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"54 - 101"},"PeriodicalIF":3.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42387075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-04-03DOI: 10.1080/0889311x.2021.2000094
P. Bombicz
“Everythingwe love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as wemanage to keep the technology beneficial.” saidMax Tegmark, President of the Future of Life Institute [1]. There is half a century of evolution behind artificial intelligence (AI) and machine learning (ML). The exponentially developing technology can do a good job at narrow tasks for example in mathematics, modelling climate change, internet searches, facial recognition, speech recognition, driving autonomous cars, customer service, playing chess, or Facebook uses algorithms to block content that breaks its rules. It can be applied in automated stock trading, it is offered for the commercial sectors, solving business problems for public and private sectors. Science fiction often portrays artificial intelligence with human-like characteristics, which emerges conversations about the impact on society and around the ethics of AI. Artificial General or Super Intelligence is a theoretical form of AI, where it would have a self-aware consciousness that had the ability to solve problems surpassing the intelligence and capacity of the human brain. An example isHAL, the rogue computer assistant in 2001: A Space Odyssey. Back to reality, algorithms cannot understand the essence of humans: emotion, morality, culture, since these abilities cannot be expressed in mathematical equations. Artificial Intelligence enables problem-solving by the combination of computer science and robust datasets. Machine learning is the subfields of artificial intelligence. Deep learning refers to a neural network, which comprise of multiple hidden layers between the input and output layers. Machine learning and deep learning differs in the way how their algorithms learn.Machine learning ismore dependent on human intervention, what determines the hierarchy of features. A deep learning-based systemwould be able to achieve the same task in a much shorter time. MelanieVollmar andGwyndaf Evans from theDiamondLight Source Ltd., and from the Rosalind Franklin Institute, respectively, both in Harwell Science and Innovation Campus, Didcot, UK, review the “Machine learning applications in macromolecular X-ray crystallography” in Issue 2 of Volume 27 of Crystallography Reviews. We can quickly understand why introducing Artificial Intelligence is somuch investigated and needed at the Diamond Light Source reading the facts: throughout 2020 user access to MX beamlines was almost exclusively remote, in the 11 months from June 2020 over 33,000 data sets were measured, typically less than 3minutes being used for each crystal sample, the yearly quantities of measured data reach many Petabyte. We may learn from the article that AI can contribute to the question of crystallisability, to the detection of the presence of crystals in crystallisation trials, to forecast of experimental da
{"title":"Artificial Intelligence and Machine Learning in Crystallography Editorial for Crystallography Reviews, Issue 2 of Volume27, 2021","authors":"P. Bombicz","doi":"10.1080/0889311x.2021.2000094","DOIUrl":"https://doi.org/10.1080/0889311x.2021.2000094","url":null,"abstract":"“Everythingwe love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence has the potential of helping civilization flourish like never before – as long as wemanage to keep the technology beneficial.” saidMax Tegmark, President of the Future of Life Institute [1]. There is half a century of evolution behind artificial intelligence (AI) and machine learning (ML). The exponentially developing technology can do a good job at narrow tasks for example in mathematics, modelling climate change, internet searches, facial recognition, speech recognition, driving autonomous cars, customer service, playing chess, or Facebook uses algorithms to block content that breaks its rules. It can be applied in automated stock trading, it is offered for the commercial sectors, solving business problems for public and private sectors. Science fiction often portrays artificial intelligence with human-like characteristics, which emerges conversations about the impact on society and around the ethics of AI. Artificial General or Super Intelligence is a theoretical form of AI, where it would have a self-aware consciousness that had the ability to solve problems surpassing the intelligence and capacity of the human brain. An example isHAL, the rogue computer assistant in 2001: A Space Odyssey. Back to reality, algorithms cannot understand the essence of humans: emotion, morality, culture, since these abilities cannot be expressed in mathematical equations. Artificial Intelligence enables problem-solving by the combination of computer science and robust datasets. Machine learning is the subfields of artificial intelligence. Deep learning refers to a neural network, which comprise of multiple hidden layers between the input and output layers. Machine learning and deep learning differs in the way how their algorithms learn.Machine learning ismore dependent on human intervention, what determines the hierarchy of features. A deep learning-based systemwould be able to achieve the same task in a much shorter time. MelanieVollmar andGwyndaf Evans from theDiamondLight Source Ltd., and from the Rosalind Franklin Institute, respectively, both in Harwell Science and Innovation Campus, Didcot, UK, review the “Machine learning applications in macromolecular X-ray crystallography” in Issue 2 of Volume 27 of Crystallography Reviews. We can quickly understand why introducing Artificial Intelligence is somuch investigated and needed at the Diamond Light Source reading the facts: throughout 2020 user access to MX beamlines was almost exclusively remote, in the 11 months from June 2020 over 33,000 data sets were measured, typically less than 3minutes being used for each crystal sample, the yearly quantities of measured data reach many Petabyte. We may learn from the article that AI can contribute to the question of crystallisability, to the detection of the presence of crystals in crystallisation trials, to forecast of experimental da","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"51 - 53"},"PeriodicalIF":3.0,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46938378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-02DOI: 10.1080/0889311X.2021.1900833
E. Snell, J. Helliwell
ABSTRACT In 2005 we reviewed microgravity for macromolecular crystallization, four years after the final flight of the Space Shuttle Orbiter, and five years before the first commercial flight to the International Space Station. Since then, there have been developments in access to space and advances in technology. More regular space flight is becoming a reality, new diffraction data detectors have become available that have both a faster readout and lower noise, a new generation of extremely bright X-ray sources and X-ray free-electron lasers (XFELs) have become available with beam collimation properties well suited geometrically to more perfect protein crystals. Neutron sources, instrumentation, and methods have also advanced greatly for yielding complete structures at room temperature and radiation damage-free. The larger volumes of protein crystals from microgravity can synergise well with these recent neutron developments. Unfortunately, progress in harnessing these new technologies to maximize the benefits seen in microgravity-grown crystals has been patchy and even disappointing. Despite detailed theoretical analysis and key empirical studies, crystallization in microgravity has not yet produced the results that demonstrate its potential. In this updated review we present some of the key lessons learned and show how processes could yet be optimized given these new developments.
{"title":"Microgravity as an environment for macromolecular crystallization – an outlook in the era of space stations and commercial space flight","authors":"E. Snell, J. Helliwell","doi":"10.1080/0889311X.2021.1900833","DOIUrl":"https://doi.org/10.1080/0889311X.2021.1900833","url":null,"abstract":"ABSTRACT In 2005 we reviewed microgravity for macromolecular crystallization, four years after the final flight of the Space Shuttle Orbiter, and five years before the first commercial flight to the International Space Station. Since then, there have been developments in access to space and advances in technology. More regular space flight is becoming a reality, new diffraction data detectors have become available that have both a faster readout and lower noise, a new generation of extremely bright X-ray sources and X-ray free-electron lasers (XFELs) have become available with beam collimation properties well suited geometrically to more perfect protein crystals. Neutron sources, instrumentation, and methods have also advanced greatly for yielding complete structures at room temperature and radiation damage-free. The larger volumes of protein crystals from microgravity can synergise well with these recent neutron developments. Unfortunately, progress in harnessing these new technologies to maximize the benefits seen in microgravity-grown crystals has been patchy and even disappointing. Despite detailed theoretical analysis and key empirical studies, crystallization in microgravity has not yet produced the results that demonstrate its potential. In this updated review we present some of the key lessons learned and show how processes could yet be optimized given these new developments.","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"3 - 46"},"PeriodicalIF":3.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0889311X.2021.1900833","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42495797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-21DOI: 10.1080/0889311X.2020.1858067
M. Cianci
In 2018, Kessel and Ben-Tal completed the second edition of their INTRODUCTION TO PROTEINS, Structure, function, and motion. Dr Amit Kessel, during his Ph.D. and postdoctoral studies trained as a c...
{"title":"INTRODUCTION TO PROTEINS, Introduction to Proteins: Structure, Function, and Motion, 2nd edition","authors":"M. Cianci","doi":"10.1080/0889311X.2020.1858067","DOIUrl":"https://doi.org/10.1080/0889311X.2020.1858067","url":null,"abstract":"In 2018, Kessel and Ben-Tal completed the second edition of their INTRODUCTION TO PROTEINS, Structure, function, and motion. Dr Amit Kessel, during his Ph.D. and postdoctoral studies trained as a c...","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"27 1","pages":"47 - 50"},"PeriodicalIF":3.0,"publicationDate":"2020-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0889311X.2020.1858067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45834600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-01DOI: 10.1080/0889311x.2020.1822343
J. S. du Toit
{"title":"The Whats of a Scientific Life","authors":"J. S. du Toit","doi":"10.1080/0889311x.2020.1822343","DOIUrl":"https://doi.org/10.1080/0889311x.2020.1822343","url":null,"abstract":"","PeriodicalId":54385,"journal":{"name":"Crystallography Reviews","volume":"26 1","pages":"269 - 271"},"PeriodicalIF":3.0,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/0889311x.2020.1822343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48047125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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