iPick: Multiprocessing software for integrated NMR signal detection and validation

Rahimi, Mehdi; Lee, Yeongjoon; Markley, John L.; Lee, Woonghee

doi:10.1016/j.jmr.2021.106995

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…Because the APES peak picking program ( Shin et al, 2008 ), which was integral to Integrative NMR , supports data from only a few solution NMR experiments, we recently developed iPick ( Rahimi et al, 2021 ), a program that works with data from a wide range of 2D/3D/4D NMR experiments. We integrated the iPick GUI into POKY to support peak picking and peak importing (two-letter-code iP ).…”

Section: Newly Developed Tools and Methodsmentioning

confidence: 99%

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

Manthey

Tonelli

Clos

et al. 2022

Journal of Structural Biology: X

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Section: Newly Developed Tools and Methodsmentioning

confidence: 99%

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

Manthey

Tonelli

Clos

et al. 2022

Journal of Structural Biology: X

Self Cite

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“…Conventional peak picking methods in a routine NMR instrument may be insufficient. The specialized DNNs are providing respite to analytical chemists to decode these utilizing advanced GUI interface (Rahimi et al 2021 ) and DNNs (Li et al 2022 ). Native MS spectrometry is utilized for unravelling macromolecule structures particularly nucleic acids and proteins.…”

Section: Ai and Automation In Analytical Chemistrymentioning

confidence: 99%

Navigating with chemometrics and machine learning in chemistry

Joshi¹

2023

Artif Intell Rev

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Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.

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“…Due to making peak lists using these automated assignment tools is time-consuming, additional tools have been developed to automate peak picking. These tools use various techniques, such as contour diagrams (CAPP 6 ), Bayesian methods 7 , neural network 8 , AUTOPSY 9 , iPick in POKY 10 , and more, to identify peak resonances in the spectrum, but incomplete or incorrect assignments may still occur due to the poorly detected peaks. Indeed, peak picking remains a challenge in complex biomolecules.…”

Section: Introductionmentioning

confidence: 99%

Breaking boundaries: TINTO in POKY for computer vision-based NMR walking strategies

Giraldo,

Werner,

Rahimi

et al. 2023

J Biomol NMR

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Nuclear Magnetic Resonance is a crucial technique for studying biological complexes, as it provides precise structural and dynamic information at the atomic level. However, the process of assigning resonances can be time-consuming and challenging, particularly in cases where peaks overlap, or the data quality is poor. In this paper, we present TINTO (Two and three-dimensional Imaging for NMR sTrip Operation via CV/ML), an advanced semiautomatic toolset for NMR resonance assignment. TINTO comprises two separate tools, each tailored for either two-dimensional or threedimensional imaging. The toolset utilizes a computer-vision approach and a machine learning approach, specifically Structural Similarity Index (SSIM) and Principal Components Analysis (PCA), to perform visual similarity searches of resonances and quickly locate similar strips, and in that way overcome the challenges associated with peak overlap without requiring peak picking. Our tool offers a user-friendly interface and has the potential to enhance the efficiency and accuracy of NMR resonance assignment, particularly in complex cases. This advancement holds promising implications for furthering our understanding of biological systems at the molecular level. TINTO is pre-installed in the POKY suite, which is available at https://poky.clas.ucdenver.edu.

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iPick: Multiprocessing software for integrated NMR signal detection and validation

Cited by 10 publications

References 12 publications

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

POKY software tools encapsulating assignment strategies for solution and solid-state protein NMR data

Navigating with chemometrics and machine learning in chemistry

Breaking boundaries: TINTO in POKY for computer vision-based NMR walking strategies

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