scholarly journals Some Applications of Artificial Intelligence on Biotechnology

2014 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Rogerio Antonio Strapasson ◽  
Adenise Lorenci Woiciechiwski ◽  
Luiz Alberto Junior Letti ◽  
Carlos Ricardo Soccol
Keyword(s):  
Neural Networks ◽  
Solid State ◽  
Solid State Fermentation ◽  
Production Efficiency ◽  
Special Kind ◽  
Quantitative Structure Activity Relationship ◽  
Sequencing Analysis ◽  
Quantitative Structure ◽  
Soft Sensing ◽  
Structure Activity

The present work is a revision about neural networks. Initially presents a little introduction to neural networks, fuzzy logic, a brief history, and the applications of Neural Networks on Biotechnology. The chosen sub-areas of the applications of Neural Networks on Biotechnology are, Solid-State Fermentation Optimization, DNA Sequencing, Molecular Sequencing Analysis, Quantitative Structure-Activity Relationship, Soft Sensing, Spectra Interpretation, Data Mining, each one use a special kind of neural network like feedforward, recurrent, siamese, art, among others. Applications of the Neural-Networks in spectra interpretation and Quantitative Structure-activity relationships, is a direct application to Chemistry and consequently also to Biochemistry and Biotechnology. Soft Sensing is a special example for applications on Biotechnology. It is a method to measure variables that normally can’t be directly measure. Solid state fermentation was optimized and presenting, as result, a strong increasing of production efficiency.

scholarly journals Quantitative structure–activity relationship to predict the anti-malarial activity in a set of new imidazolopiperazines based on artificial neural networks

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Saeed Yousefinejad ◽  
Marjan Mahboubifar ◽  
Rayhaneh Eskandari
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Structural Information ◽  
Quantitative Structure Activity Relationship ◽  
Structure Activity Relationship ◽  
Activity Relationship ◽  
Quantitative Structure ◽  
Structure Activity ◽  
Marquardt Algorithm ◽  
Artificial Neural

Abstract Background After years of efforts on the control of malaria, it remains as a most deadly infectious disease. A major problem for the available anti-malarial drugs is the occurrence of drug resistance in Plasmodium. Developing of new compounds or modification of existing anti-malarial drugs is an effective approach to face this challenge. Quantitative structure activity relationship (QSAR) modelling plays an important role in design and modification of anti-malarial compounds by estimation of the activity of the compounds. Methods In this research, the QSAR study was done on anti-malarial activity of 33 imidazolopiperazine compounds based on artificial neural networks (ANN). The structural descriptors of imidazolopiperazine molecules was used as the independents variables and their activity against 3D7 and W2 strains was used as the dependent variables. During modelling process, 70% of compound was used as the training and two 15% of imidazolopiperazines were used as the validation and external test sets. In this work, stepwise multiple linear regression was applied as the valuable selection and ANN with Levenberg–Marquardt algorithm was utilized as an efficient non-linear approach to correlate between structural information of molecules and their anti-malarial activity. Results The sufficiency of the suggested method to estimate the anti-malarial activity of imidazolopiperazine compounds at two 3D7 and W2 strains was demonstrated using statistical parameters, such as correlation coefficient (R2), mean square error (MSE). For instance R2train = 0.947, R2val = 0.959, R2test = 0.920 shows the potential of the suggested model for the prediction of 3D7 activity. Different statistical approaches such as and applicability domain (AD) and y-scrambling was also showed the validity of models. Conclusion QSAR can be an efficient way to virtual screening the molecules to design more efficient compounds with activity against malaria (3D7 and W2 strains). Imidazolopiperazines can be good candidates and change in the structure and functional groups can be done intelligently using QSAR approach to rich more efficient compounds with decreasing trial–error runs during synthesis.

scholarly journals A Comparison of “Neural Networks and Multiple Linear Regressions” Models to Describe the Rejection of Micropollutants by Membranes

2020 ◽  
Vol 69 (3-4) ◽  
pp. 111-127
Author(s):  
Yamina Ammi ◽  
Latifa Khaouane ◽  
Salah Hanini
Keyword(s):  
Neural Networks ◽  
Organic Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Multiple Linear Regressions ◽  
Quantitative Structure ◽  
Feed Forward ◽  
Structure Activity ◽  
Data Points ◽  
Feed Forward Neural Networks ◽  
Linear Regressions

A rejection process of organic compounds by nanofiltration and reverse osmosis membranes was modelled using the artificial neural networks. Three feed-forward neural networks based on quantitative structure-activity relationship (QSAR-NN models) characterised by a similar structure (twelve neurons for QSAR-NN<sub>1</sub>, QSAR-NN<sub>2</sub>, and QSAR-NN<sub>3</sub> in the input layer, one hidden layer and one neuron in the output layer), were constructed with the aim of predicting the rejection of organic compounds. A set of 1394 data points for QSAR-NN<sub>1</sub>, 980 data points for QSAR-NN<sub>2</sub>, and 436 data points for QSAR-NN<sub>3</sub> were used to construct the neural networks. Good agreements between the predicted and experimental rejections were obtained by QSAR-NN models (the correlation coefficient for the total dataset were 0.9191 for QSAR-NN<sub>1</sub>, 0.9338 for QSAR-NN<sub>2</sub>, and 0.9709 for QSAR-NN<sub>3</sub>). Comparison between the feed-forward neural networks and multiple linear regressions based on quantitative structure-activity relationship “QSAR-MLR” revealed the superiority of the QSAR-NN models (the root mean squared errors for the total dataset for the QSAR-NN models were 10.6517 % for QSAR-NN<sub>1</sub>, 9.1991 % for QSAR-NN<sub>2</sub>, and 5.8869 % for QSAR-NN<sub>3</sub>, and for QSAR-MLR models they were 20.1865 % for QSAR-MLR<sub>1</sub>, 19.3815 % for QSAR-MLR<sub>2</sub>, and 16.2062 % for QSAR-MLR<sub>3</sub>).

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