Study and identification of new molecular descriptors, finalized to the development of Virtual Screening techniques through the use of deep neural networks

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&…

SAIApp: un'app dell'Università di Palermo dedicata ai bambini con malattie autoinfiammatorie

Boolean Networks: A Primer

Abstract Autism Spectrum Disorders (ASDs) stand out as a relevant example where omics-data approaches have been extensively and successfully employed. For instance, an outstanding outcome of the Autism Genome Project relies in the identification of biomarkers and the mapping of biological processes potentially implicated in ASDs’ pathogenesis. Several of these mapped processes are related to molecular and cellular events (e.g., synaptogenesis and synapse function, axon growth and guidance, etc.) that are required for the development of a correct neuronal connectivity. Interestingly, these data are consistent with results of brain imaging studies of some patients. Despite these remarkable pr…

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…

Neuronal Cytoskeleton in Intellectual Disability: From Systems Biology and Modeling to Therapeutic Opportunities

Intellectual disability (ID) is a pathological condition characterized by limited intellectual functioning and adaptive behaviors. It affects 1–3% of the worldwide population, and no pharmacological therapies are currently available. More than 1000 genes have been found mutated in ID patients pointing out that, despite the common phenotype, the genetic bases are highly heterogeneous and apparently unrelated. Bibliomic analysis reveals that ID genes converge onto a few biological modules, including cytoskeleton dynamics, whose regulation depends on Rho GTPases transduction. Genetic variants exert their effects at different levels in a hierarchical arrangement, starting from the molecular lev…