Convolutional architectures for virtual screening

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% in high precision discrimination). Conclusion: The proposed architecture outperforms state-of-the-art ML approaches, and some interesting insights on molecular fingerprints are devised

Adolescent School Bullying Victimization and Later Life Outcomes

We analyse the consequences of experiencing bullying victimization in junior high school, using data on a cohort of English adolescents. The data contain self-reports of five types of bullying and their frequency, for three waves, when the pupils were aged 13–16 years. We assess the effects of bullying victimization on short- and long-term outcomes, including educational achievements, income and mental ill-health at age 25 years using a variety of estimation strategies – least squares, matching and inverse probability weighting. The detailed longitudinal data, linked to administrative records, allows us to control for many of the determinants of child outcomes that have been explored in previous literature, and we employ comprehensive sensitivity analyses to assess the potential role of unobserved variables. The pattern of results suggests that there are quantitatively important detrimental effects on victims. We find that both type of bullying and its intensity matter for high-stakes outcomes at 16 years, and for long-term outcomes at 25 such as mental health and incom

COVID-19 vaccination intentions and subsequent uptake:An analysis of the role of marginalisation in society using British longitudinal data

COVID-19 vaccine hesitancy has previously been modelled using data on intentions – expressed prior to vaccine availability. Once vaccines became widely available, it became possible to model hesitancy using actual vaccination uptake data. This paper estimates the determinants of the joint distribution of COVID-19 vaccination intentions (declared before the release of any vaccine) and actual vaccination take-up (when it was widely available across the age distribution). We use high quality longitudinal data (UK Household Longitudinal Study) collected during the pandemic in the UK, merged to a wide variety of individual characteristics collected prior to the COVID-19 pandemic. Our estimation draws on pre-Covid values of variables for a sample that includes 10,073 observations from the September 2021 wave. The contribution of this paper is to model hesitancy and uptake jointly. The work shows that people who might be regarded as marginalised in society (measured, before the pandemic began) are less likely to say that they intend to be vaccinated and they go on to also be more likely to actually remain unvaccinated. It also shows that there is a large positive correlation between the unobservable determinants of intention and of uptake. This high positive correlation has an important implication - that information campaigns can be reasonably well profiled to target specific groups on the basis of intention data alone. We also show that changing one's mind is not correlated with observable data. This is consistent with two explanations. Firstly, the new information available on the arrival of vaccines, that they are safe and effective, may be more optimistic than was originally assumed. Secondly, individuals may have been more pessimistic about the effects associated with infection before vaccines became available

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 molecular fingerprints as a suitable embedding for describing molecules; up to our knowledge there is no Deep Learning approach for VS that makes use of such descriptor. Several kinds of fingerprints are reported in the scientific literature to address different aspects of both the structure and the local properties of a molecule. Both 1D and 2D CNNs have been trained to test the performance of each single descriptor separately, along with suitable ensembles of multiple descriptors for the same compound; the best performing architecture has been used for prediction. The CNN architectures are described in detail, and the results are compared with some recent approaches for Virtual Screening with respect to Cyclin-Dependent Kinase proteins that do not use molecular fingerprints as their descriptor

EMBER\u2014Embedding 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 \u201cspectra\u201d to describe the same molecule, and we prove its effectiveness by using deep convolutional architecture that assesses ligands\u2019 bioactivity on a data set containing twenty protein kinases with similar binding sites to CDK1. The data set itself is presented, and the architecture is explained in detail along with its training procedure. We report experimental results and an explainability analysis to assess the contribution of each fingerprint to different targets

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 fabricate and improve coatings for biomedical devices. Metallic materials, especially in the orthopedic field, represent the most common materials 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 features, to inhibit corrosion and thus improve the lifetime of implanted devices. In this work, the attention was focused on Hydroxyapatite-Chitosan (HA/CS) and Hydroxyapatite-Polyvinylacetate (HA/PVAc) composites, that have been studied as biocoatings for 304 SS based devices. Hydroxyapatite was selected for its osteoconductivity due to its chemical structure similar to bones. Furthermore, Chitosan and Polyvinylacetate are largely used yet in medical field (e.g. antibacterial agent or drug deliver) and in this work were used to create a synergic interaction with hydroxyapatite to increase the strength and bioactivity of coating. Despite bio-coatings were obtained by different techniques, in this work, they were fabricated by galvanic deposition process that has different advantages, among which it does not require external power supply. It is a spontaneous electrochemical reaction in which materials with different standard electrochemical potential were short-circuited and immersed in an electrolytic solution. Electrons supplied by the anodic reaction at the less noble electrode flow to cathode where they oxidize the less noblest ions in solution. SEM, EDS, XRD and RAMAN were performed for chemical-physics characterization of biocoatings. Polarization and impedance measurements have been also carried out to evaluate corrosion behavior. Besides, in-vitro cytotoxicity assays have been done for the biological features

Calcium phosphate/polyvinyl acetate coatings on SS304 via galvanic co-deposition for orthopedic implant applications

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 interface. The best performing deposits are those obtained by using Al as the sacrificial anode. The metal ion release, measured after 21 days of aging, is very low and is attributable to the presence of a coating that slows the steel corrosion. Coating cytotoxicity is investigated through cell viability assays with MC3T3-E1 osteoblastic cells. The results reveal a high cytocompatibility comparable to that of a pure cell culture medium