Search results for "Molecular Fingerprint"

Chemical partitioning and DNA fingerprinting of some pistachio (Pistacia vera L.) varieties of different geographical origin

2019

The genus Pistacia (Anacardiaceae family) is represented by several species, of which only P. vera L. produces edible seeds (pistachio). Despite the different flavor and taste, a correct identification of pistachio varieties based on the sole phenotypic character is sometimes hard to achieve. Here we used a combination of chemical partitioning and molecular fingerprinting for the unequivocal identification of commercial pistachio seed varieties (Bronte, Kern, Kerman, Larnaka, Mateur and Mawardi) of different geographical origin. The total phenolic content was higher in the variety Bronte followed by Larnaka and Mawardi cultivars. The total anthocyanin content was higher in Bronte and Larnak…

Deep neural networks leveraging different arrangements of molecular fingerprints to define a novel embedding for virtual screening procedure

2022

A convolutional neural network for virtual screening of molecular fingerprints

2019

In the last few years, Deep Learning (DL) gained more and more impact on drug design because it allows a huge increase of the prediction accuracy in many stages of such a complex process. In this paper a Virtual Screening (VS) procedure based on Convolutional Neural Networks (CNN) is presented, that is aimed at classifying a set of candidate compounds as regards their biological activity on a particular target protein. The model has been trained on a dataset of active/inactive compounds with respect to the Cyclin-Dependent Kinase 1 (CDK1) a very important protein family, which is heavily involved in regulating the cell cycle. One qualifying point of the proposed approach is the use of molec…

Convolutional architectures for virtual screening

2020

Abstract Background A Virtual Screening algorithm has to adapt to the different stages of this process. Early screening needs to ensure that all bioactive compounds are ranked in the first positions despite of the number of false positives, while a second screening round is aimed at increasing the prediction accuracy. Results A novel CNN architecture is presented to this aim, which predicts bioactivity of candidate compounds on CDK1 using a combination of molecular fingerprints as their vector representation, and has been trained suitably to achieve good results as regards both enrichment factor and accuracy in different screening modes (98.55% accuracy in active-only selection, and 98.88% …