Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

A Novel Deep Neural Network Technique for Drug–Target Interaction

The authors wish to acknowledge the financial support of the CoordenacAo de Aperfeicoamento de Pessoal de Nivel Superior (CAPES). This research was supported by the High-Performance Computing Center at UFRN (NPAD/UFRN). This study was financed in part by the CoordenacAo de Aperfeicoamento de Pessoal de Nivel Superior (CAPES)-Finance Code 001.Drug discovery (DD) is a time-consuming and expensive process. Thus, the industry employs strategies such as drug repositioning and drug repurposing, which allows the application of already approved drugs to treat a di erent disease, as occurred in the first months of 2020, during the COVID-19 pandemic. The prediction of drug–target interactions is an essential part of the DD process because it can accelerate it and reduce the required costs. DTI prediction performed in silico have used approaches based on molecular docking simulations, including similarity-based and network- and graph-based ones. This paper presents MPS2IT-DTI, a DTI prediction model obtained from research conducted in the following steps: the definition of a new method for encoding molecule and protein sequences onto images; the definition of a deep-learning approach based on a convolutional neural network in order to create a new method for DTI prediction. Training results conducted with the Davis and KIBA datasets show that MPS2IT-DTI is viable compared to other state-of-the-art (SOTA) approaches in terms of performance and complexity of the neural network model. With the Davis dataset, we obtained 0.876 for the concordance index and 0.276 for the MSE; with the KIBA dataset, we obtained 0.836 and 0.226 for the concordance index and the MSE, respectively. Moreover, the MPS2IT-DTI model represents molecule and protein sequences as images, instead of treating them as an NLP task, and as such, does not employ an embedding layer, which is present in other models.Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (CAPES) 001High-Performance Computing Center at UFRN (NPAD/UFRN

Targeting molecular networks for drug research.

The study of molecular networks has recently moved into the limelight of biomedical research. While it has certainly provided us with plenty of new insights into cellular mechanisms, the challenge now is how to modify or even restructure these networks. This is especially true for human diseases, which can be regarded as manifestations of distorted states of molecular networks. Of the possible interventions for altering networks, the use of drugs is presently the most feasible. In this mini-review, we present and discuss some exemplary approaches of how analysis of molecular interaction networks can contribute to pharmacology (e.g., by identifying new drug targets or prediction of drug side effects), as well as list pointers to relevant resources and software to guide future research. We also outline recent progress in the use of drugs for in vitro reprogramming of cells, which constitutes an example par excellence for altering molecular interaction networks with drugs

Unsupervised Learning and Multipartite Network Models: A Promising Approach for Understanding Traditional Medicine

The ultimate goal of precision medicine is to determine right treatment for right patients based on precise diagnosis. To achieve this goal, correct stratification of patients using molecular features and clinical phenotypes is crucial. During the long history of medical science, our understanding on disease classification has been improved greatly by chemistry and molecular biology. Nowadays, we gain access to large scale patient-derived data by high-throughput technologies, generating a greater need for data science including unsupervised learning and network modeling. Unsupervised learning methods such as clustering could be a better solution to stratify patients when there is a lack of predefined classifiers. In network modularity analysis, clustering methods can be also applied to elucidate the complex structure of biological and disease networks at the systems level. In this review, we went over the main points of clustering analysis and network modeling, particularly in the context of Traditional Chinese medicine (TCM). We showed that this approach can provide novel insights on the rationale of classification for TCM herbs. In a case study, using a modularity analysis of multipartite networks, we illustrated that the TCM classifications are associated with the chemical properties of the herb ingredients. We concluded that multipartite network modeling may become a suitable data integration tool for understanding the mechanisms of actions of traditional medicine.Peer reviewe

Modularity in Protein Complex and Drug Interactions Reveals New Polypharmacological Properties

Recent studies have highlighted the importance of interconnectivity in a large range of molecular and human disease-related systems. Network medicine has emerged as a new paradigm to deal with complex diseases. Connections between protein complexes and key diseases have been suggested for decades. However, it was not until recently that protein complexes were identified and classified in sufficient amounts to carry out a large-scale analysis of the human protein complex system. We here present the first systematic and comprehensive set of relationships between protein complexes and associated drugs and analyzed their topological features. The network structure is characterized by a high modularity, both in the bipartite graph and in its projections, indicating that its topology is highly distinct from a random network and that it contains a rich and heterogeneous internal modular structure. To unravel the relationships between modules of protein complexes, drugs and diseases, we investigated in depth the origins of this modular structure in examples of particular diseases. This analysis unveils new associations between diseases and protein complexes and highlights the potential role of polypharmacological drugs, which target multiple cellular functions to combat complex diseases driven by gain-of-function mutations

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseasesThis work was partially funded by The Spanish Ministry of Economy and Competitiveness with European Regional Development Fund [grant numbers PID2019-108096RB-C21 and PID2019-108096RB-C22]; the European Food Safety Authority [grant number GP/EFSA/ENCO/2020/02]; the Andalusian Government with European Regional Development Fund [grant numbers UMA18- FEDERJA-102 and PAIDI 2020:PY20-00372]; Fundacion Progreso y Salud [grant number PI-0075-2017], also from the Andalusian Government; the Ramón Areces foundation, which funds project for the investigation of rare disease (National call for research on life and material sciences, XIX edition); the University of Malaga (Ayudas del I Plan Propio) and the Institute of Health Carlos III which funds the IMPaCT-Data project. The CIBERER is an initiative from the Institute of Health Carlos III. The conclusions, findings and opinions expressed in this scientific paper reflect only the view of the authors and not the official position of the European Food Safety Authority. Partial funding for open access charge: Universidad de Málag

Chemoinformatics-Driven Approaches for Kinase Drug Discovery

Given their importance for the majority of cell physiology processes, protein kinases are among the most extensively studied protein targets in drug discovery. Inappropriate regulation of their basal levels results in pathophysiological disorders. In this regard, small-molecule inhibitors of human kinome have been developed to treat these conditions effectively and improve the survival rates and life quality of patients. In recent years, kinase-related data has become increasingly available in the public domain. These large amounts of data provide a rich knowledge source for the computational studies of kinase drug discovery concepts. This thesis aims to systematically explore properties of kinase inhibitors on the basis of publicly available data. Hence, an established "selectivity versus promiscuity" conundrum of kinase inhibitors is evaluated, close structural analogs with diverging promiscuity levels are analyzed, and machine learning is employed to classify different kinase inhibitor binding modes. In the first study, kinase inhibitor selectivity trends are explored on the kinase pair level where kinase structural features and phylogenetic relationships are used to explain the obtained selectivity information. Next, selectivity of clinical kinase inhibitors is inspected on the basis of cell-based profiling campaign results to consolidate the previous findings. Further, clinical candidates are mapped to medicinal chemistry sources and promiscuity levels of different inhibitor subsets are estimated, including designated chemical probes. Additionally, chemical probe analysis is extended to expert-curated representatives to correlate the views established by scientific community and evaluate their potential for chemical biology applications. Then, large-scale promiscuity analysis of kinase inhibitor data combining several public repositories is performed to subsequently explore promiscuity cliffs (PCs) and PC pathways and study structure-promiscuity relationships. Furthermore, an automated extraction protocol prioritizing the most informative pathways is proposed with focus on those containing promiscuity hubs. In addition, the generated promiscuity data structures including cliffs, pathways, and hubs are discussed for their potential in experimental and computational follow-ups and subsequently made publicly available. Finally, machine learning methods are used to develop classification models of kinase inhibitors with distinct experimental binding modes and their potential for the development of novel therapeutics is assessed