Rapid and quantitative analysis of metabolites in fermentor broths using pyrolysis mass spectrometry with supervised learning: application to the screening of Penicillium chrysogenum fermentations for the overproduction of penicillins

Goodacre, Royston; Trew, Sally J.; Wrigley-Jones, Carys; Saunders, Gunter; Neal, Mark; Porter, Neil; Kell, Douglas B.

doi:10.1016/0003-2670(95)00170-5

Cited by 47 publications

(16 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each sample was analysed in triplicate. For full operational procedures see [7, 13, 14]. The sample tube carrying the foil was heated, prior to pyrolysis, at 100°C for 5 s. Curie‐point pyrolysis was at 530°C for 3 s, with a temperature rise time of 0.5 s. These conditions were used for all experiments.…”

Section: Methodsmentioning

confidence: 99%

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of someEubacteriumspecies

Taylor

Goodacre

Wade

et al. 1998

FEMS Microbiology Letters

Self Cite

View full text Add to dashboard Cite

Pyrolysis mass spectrometry was used to produce complex biochemical fingerprints of Eubacterium exiguum, E. infirmum, E. tardum and E. timidum. To examine the relationship between these organisms the spectra were clustered by canonical variates analysis, and four clusters, one for each species, were observed. In an earlier study we trained artificial neural networks to identify these clinical isolates successfully; however, the information used by the neural network was not accessible from this so-called 'black box' technique. To allow the deconvolution of such complex spectra (in terms of which masses were important for discrimination) it was necessary to develop a system that itself produces 'rules' that are readily comprehensible. We here exploit the evolutionary computational technique of genetic programming; this rapidly and automatically produced simple mathematical functions that were also able to classify organisms to each of the four bacterial groups correctly and unambiguously. Since the rules used only a very limited set of masses, from a search space some 50 orders of magnitude greater than the dimensionality actually necessary, visual discrimination of the organisms on the basis of these spectral masses alone was also then possible.

show abstract

Section: Methodsmentioning

confidence: 99%

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of someEubacteriumspecies

Taylor

Goodacre

Wade

et al. 1998

FEMS Microbiology Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…Bond The initial atom-bond feature vector is then passed through a linear layer followed by ReLU Activation [68,69] to get the Depth-0 message vector for each bond. For each bond, the message vectors for the neighbouring bonds are summed up (Convolution step) and passed through a linear layer followed by ReLU and a Dropout layer to get the Depth-1 message vectors.…”

Section: Indices Bond Description 133mentioning

confidence: 99%

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Khemchandani

O’Hagan

Samanta

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

show abstract

“…102 In the latter study samples from fermentations of a single organism in a complex production medium were analyzed quantitatively for a drug of commercial interest; the drug could be quantified in a variety of mutant producing strains cultivated in the same medium, thus effecting a rapid screening for the high-level production of desired substances. In related studies Penicillium chrysogenum fermentation broths were analyzed quantitatively for penicillins using PyMS and ANNs, 103 the production of the protein a2-interferon was quantified in recombinant E. coli bioprocess samples, 104 and Gibberella fujikuroi fermentations were monitored for gibberellic acid production. 105 Other workers have also demonstrated the potential role of PyMS with ANNs in quantitative biotechnology.…”

Section: Quantification By Pyms With Annsmentioning

confidence: 99%

Discrimination and Identification of Microorganisms by Pyrolysis Mass Spectrometry: From Burning Ambitions to Cooling Embers—A Historical Perspective

Timmins

Goodacre

2005

Identification of Microorganisms by Mass Spectrometry

Self Cite

View full text Add to dashboard Cite

Rapid and quantitative analysis of metabolites in fermentor broths using pyrolysis mass spectrometry with supervised learning: application to the screening of Penicillium chrysogenum fermentations for the overproduction of penicillins

Cited by 47 publications

References 63 publications

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of someEubacteriumspecies

The deconvolution of pyrolysis mass spectra using genetic programming: application to the identification of someEubacteriumspecies

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Discrimination and Identification of Microorganisms by Pyrolysis Mass Spectrometry: From Burning Ambitions to Cooling Embers—A Historical Perspective

Contact Info

Product

Resources

About