0000000000947676
AUTHOR
Isabella Mendolia
Co-Deposition and Characterization of Hydroxyapatite-Chitosan and Hydroxyapatite-Polyvinylacetate Coatings on 304 SS for Biomedical Devices
During the last decades, biomaterials have been deeply studied to perform and improve coatings for biomedical devices. Metallic materials, especially in the orthopedic field, represent the most common material used for different type of devices thanks to their good mechanical properties. Nevertheless, low/medium resistance to corrosion and low osteointegration ability characterizes these materials. To overcome these problems, the use of biocoatings on metals substrate is largely diffused. In fact, biocoatings have a key role to confer biocompatibility properties, to inhibit corrosion and thus improve the lifetime of implanted devices. In this work, the attention was focused on Hydroxyapatit…
Calcium phosphate/polyvinyl acetate coatings on SS304 via galvanic co-deposition for orthopedic implant applications
Abstract In this work, the galvanic deposition method is used to deposit coatings of brushite/hydroxyapatite/polyvinyl acetate on 304 stainless steel. Coatings are obtained at different temperatures and with different sacrificial anodes, consisting of a mixture of brushite and hydroxyapatite. Samples are aged in a simulated body fluid (SBF), where a complete conversion of brushite into hydroxyapatite with a simultaneous change in morphology and wettability occurred. The corrosion tests show that, compared with bare 304, the coating shifts Ecorr to anodic values and reduces icorr Ecorr, and icorr has different values at different aging times due to chemical interactions at the solid/liquid i…
EMBER—Embedding Multiple Molecular Fingerprints for Virtual Screening
In recent years, the debate in the field of applications of Deep Learning to Virtual Screening has focused on the use of neural embeddings with respect to classical descriptors in order to encode both structural and physical properties of ligands and/or targets. The attention on embeddings with the increasing use of Graph Neural Networks aimed at overcoming molecular fingerprints that are short range embeddings for atomic neighborhoods. Here, we present EMBER, a novel molecular embedding made by seven molecular fingerprints arranged as different “spectra” to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands&…
Convolutional architectures for virtual screening
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% …
A convolutional neural network for virtual screening of molecular fingerprints
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…