A survey on the computational approaches to identify drug targets in the postgenomic era

The OREGANO knowledge graph for computational drug repurposing

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ).

A Computational Workflow to Predict Biological Target Mutations: The Spike Glycoprotein Case Study

The biological target identification process, a pivotal phase in the drug discovery workflow, becomes particularly challenging when mutations affect proteins' mechanisms of action. COVID-19 Spike glycoprotein mutations are known to modify the affinity toward the human angiotensin-converting enzyme ACE2 and several antibodies, compromising their neutralizing effect. Predicting new possible mutations would be an efficient way to develop specific and efficacious drugs, vaccines, and antibodies. In this work, we developed and applied a computational procedure, combining constrained logic programming and careful structural analysis based on the Structural Activity Relationship (SAR) approach, to predict and determine the structure and behavior of new future mutants. "Mutations rules" that would track statistical and functional types of substitutions for each residue or combination of residues were extracted from the GISAID database and used to define constraints for our software, having control of the process step by step. A careful molecular dynamics analysis of the predicted mutated structures was carried out after an energy evaluation of the intermolecular and intramolecular interactions using the HINT (Hydrophatic INTeraction) force field. Our approach successfully predicted, among others, known Spike mutants.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

Network neighborhood operates as a drug repositioning method for cancer treatment

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

An extensive survey on the use of supervised machine learning techniques in the past two decades for prediction of drug side effects

Approved drugs for sale must be effective and safe, implying that the drug's advantages outweigh its known harmful side effects. Side effects (SE) of drugs are one of the common reasons for drug failure that may halt the whole drug discovery pipeline. The side effects might vary from minor concerns like a runny nose to potentially life-threatening issues like liver damage, heart attack, and death. Therefore, predicting the side effects of the drug is vital in drug development, discovery, and design. Supervised machine learning-based side effects prediction task has recently received much attention since it reduces time, chemical waste, design complexity, risk of failure, and cost. The advancement of supervised learning approaches for predicting side effects have emerged as essential computational tools. Supervised machine learning technique provides early information on drug side effects to develop an effective drug based on drug properties. Still, there are several challenges to predicting drug side effects. Thus, a near-exhaustive survey is carried out in this paper on the use of supervised machine learning approaches employed in drug side effects prediction tasks in the past two decades. In addition, this paper also summarized the drug descriptor required for the side effects prediction task, commonly utilized drug properties sources, computational models, and their performances. Finally, the research gap, open problems, and challenges for the further supervised learning-based side effects prediction task have been discussed.

Optimizing Genomic Control in Hit Network-Target Set Model Associations with Lung Adenocarcinoma

Background: Hit network-target sets (HNSs), compiled sets of different network nodes of the same type, are available and play a significant role in cancer development but are notoriously more difficult to select than a single target. This is due to a combination of challenges attributed to the differential of node interactions, node heterogeneity, and the limitations of node-hit information. Methods: In this study, we constructed a lung adenocarcinoma regulatory network using TCGA data and obtained different HNSs of driver nodes (DNs), core modules (CMs) and core nodes (CNs) through three kinds of methods. Then, the optimized HNS (OHNS) was obtained by integrating CMs, CNs and DNs, and the performance of different HNSs was evaluated according to network structure importance, control capability, and clinical value. Results: We found that the OHNS has two main advantages, the central location of the network and the ability to control the network, and it plays an important role in the disease network through its multifaceted capabilities. Three unique pathways were discovered in the OHNS, which is consistent with previous experiments. Additionally, 13 genes were predicted to play roles in risk prognosis, disease drivers, and cell perturbation effects of lung adenocarcinoma, of which 12 may be candidates for new drugs and biomarkers of lung adenocarcinoma. Conclusion: This study can help us understand and control a network more effectively to determine the development trend of a disease, design effective multitarget drugs, and guide the therapeutic community to optimize appropriate strategies according to different research aims in cancer treatment.

Identify Biomarkers and Design Effective Multi-Target Drugs in Ovarian Cancer: Hit Network-Target Sets Model Optimizing

Epithelial ovarian cancer (EOC) is highly aggressive with poor patient outcomes, and a deeper understanding of ovarian cancer tumorigenesis could help guide future treatment development. We proposed an optimized hit network-target sets model to systematically characterize the underlying pathological mechanisms and intra-tumoral heterogeneity in human ovarian cancer. Using TCGA data, we constructed an epithelial ovarian cancer regulatory network in this study. We use three distinct methods to produce different HNSs for identification of the driver genes/nodes, core modules, and core genes/nodes. Following the creation of the optimized HNS (OHNS) by the integration of DN (driver nodes), CM (core module), and CN (core nodes), the effectiveness of various HNSs was assessed based on the significance of the network topology, control potential, and clinical value. Immunohistochemical (IHC), qRT-PCR, and Western blotting were adopted to measure the expression of hub genes and proteins involved in epithelial ovarian cancer (EOC). We discovered that the OHNS has two key advantages: the network's central location and controllability. It also plays a significant role in the illness network due to its wide range of capabilities. The OHNS and clinical samples revealed the endometrial cancer signaling, and the PI3K/AKT, NER, and BMP pathways. MUC16, FOXA1, FBXL2, ARID1A, COX15, COX17, SCO1, SCO2, NDUFA4L2, NDUFA, and PTEN hub genes were predicted and may serve as potential candidates for new treatments and biomarkers for EOC. This research can aid in better capturing the disease progression, the creation of potent multi-target medications, and the direction of the therapeutic community in the optimization of effective treatment regimens by various research objectives in cancer treatment.

(R)-Carvone is a potential soil fumigant against Meloidogyne incognita whose likely enzymatic target in the nematode is acetylcholinesterase

To contribute to the development of new fumigant nematicides for the control of the plant-parasitic nematode Meloidogyne incognita, this study started with 31 volatile organic compounds reported as toxic to nematodes. At 500 μg/mL, α-ionone, (S)-carvone, (R)-carvone, 2-methylpropyl acetate, undecan-2-one, decan-2-one, and dodecan-2-one caused mortalities to M. incognita second-stage juveniles (J2) that were similar to those obtained with the commercial nematicides carbofuran (170 μg/mL) and fluensulfone (42.2 μg/mL). (R)-carvone, with a lethal concentration to 50% J2 (LC₅₀) equal to 524 μg/mL, was selected for subsequent studies. When J2 were exposed to the (R)-carvone solution, the infectivity and reproduction on tomato were reduced. In the M. incognita egg hatching assay, (R)-carvone behaved like a true ovicide. When employed as a fumigant, (R)-carvone (3.9 g/L) was as efficient as the soil fumigant dazomet (0.245 g/L) in eliminating eggs of the nematode in a substrate to be used for tomato planting. According to in silico studies employing pharmacophoric searches and molecular docking, acetylcholinesterases are the target of (R)-carvone in the nematode.

Repurposing Drugs via Network Analysis: Opportunities for Psychiatric Disorders

Despite advances in pharmacology and neuroscience, the path to new medications for psychiatric disorders largely remains stagnated. Drug repurposing offers a more efficient pathway compared with de novo drug discovery with lower cost and less risk. Various computational approaches have been applied to mine the vast amount of biomedical data generated over recent decades. Among these methods, network-based drug repurposing stands out as a potent tool for the comprehension of multiple domains of knowledge considering the interactions or associations of various factors. Aligned well with the poly-pharmacology paradigm shift in drug discovery, network-based approaches offer great opportunities to discover repurposing candidates for complex psychiatric disorders. In this review, we present the potential of network-based drug repurposing in psychiatry focusing on the incentives for using network-centric repurposing, major network-based repurposing strategies and data resources, applications in psychiatry and challenges of network-based drug repurposing. This review aims to provide readers with an update on network-based drug repurposing in psychiatry. We expect the repurposing approach to become a pivotal tool in the coming years to battle debilitating psychiatric disorders.

A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19

Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Discovery of Novel Drug Candidates for Alzheimer’s Disease by Molecular Network Modeling

To identify the molecular mechanisms and novel therapeutic agents of late-onset Alzheimer’s disease (AD), we performed integrative network analysis using multiple transcriptomic profiles of human brains. With the hypothesis that AD pathology involves the whole cerebrum, we first identified co-expressed modules across multiple cerebral regions of the aging human brain. Among them, two modules (M3 and M8) consisting of 1,429 protein-coding genes were significantly enriched with AD-correlated genes. Differential expression analysis of microarray, bulk RNA-sequencing (RNA-seq) data revealed the dysregulation of M3 and M8 across different cerebral regions in both normal aging and AD. The cell-type enrichment analysis and differential expression analysis at the single-cell resolution indicated the extensive neuronal vulnerability in AD pathogenesis. Transcriptomic-based drug screening from Connectivity Map proposed Gly-His-Lys acetate salt (GHK) as a potential drug candidate that could probably restore the dysregulated genes of the M3 and M8 network. Pretreatment with GHK showed a neuroprotective effect against amyloid-beta-induced injury in differentiated human neuron-like SH-SY5Y cells. Taken together, our findings uncover a dysregulated network disrupted across multiple cerebral regions in AD and propose pretreatment with GHK as a novel neuroprotective strategy against AD.

DeepVir: Graphical Deep Matrix Factorization for In Silico Antiviral Repositioning-Application to COVID-19

This study formulates antiviral repositioning as a matrix completion problem wherein the antiviral drugs are along the rows and the viruses are along the columns. The input matrix is partially filled, with ones in positions where the antiviral drug has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) are encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion (GRMC) problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results of our curated RNA drug-virus association data set show that the proposed approach excels over state-of-the-art GRMC techniques. When applied to in silico prediction of antivirals for COVID-19, our approach returns antivirals that are either used for treating patients or are under trials for the same.

A Hybrid Protocol for Finding Novel Gene Targets for Various Diseases Using Microarray Expression Data Analysis and Text Mining

The advancement in technology for various scientific experiments and the amount of raw data produced from that is enormous, thus giving rise to various subsets of biologists working with genome, proteome, transcriptome, expression, pathway, and so on. This has led to exponential growth in scientific literature which is becoming beyond the means of manual curation and annotation for extracting information of importance. Microarray data are expression data, analysis of which results in a set of up/downregulated lists of genes that are functionally annotated to ascertain the biological meaning of genes. These genes are represented as vocabularies and/or Gene Ontology terms when associated with pathway enrichment analysis need relational and conceptual understanding to a disease. The chapter deals with a hybrid approach we designed for identifying novel drug-disease targets. Microarray data for muscular dystrophy is explored here as an example and text mining approaches are utilized with an aim to identify promisingly novel drug targets. Our main objective is to give a basic overview from a biologist's perspective for whom text mining approaches of data mining and information retrieval is fairly a new concept. The chapter aims to bridge the gap between biologist and computational text miners and bring about unison for a more informative research in a fast and time efficient manner.

Comorbidities and Susceptibility to COVID-19: A Generalized Gene Set Data Mining Approach

The COVID-19 pandemic has led to over 2.26 million deaths for almost 104 million confirmed cases worldwide, as of 4 February 2021 (WHO). Risk factors include pre-existing conditions such as cancer, cardiovascular disease, diabetes, and obesity. Although several vaccines have been deployed, there are few alternative anti-viral treatments available in the case of reduced or non-existent vaccine protection. Adopting a long-term holistic approach to cope with the COVID-19 pandemic appears critical with the emergence of novel and more infectious SARS-CoV-2 variants. Our objective was to identify comorbidity-associated single nucleotide polymorphisms (SNPs), potentially conferring increased susceptibility to SARS-CoV-2 infection using a computational meta-analysis approach. SNP datasets were downloaded from a publicly available genome-wide association studies (GWAS) catalog for 141 of 258 candidate COVID-19 comorbidities. Gene-level SNP analysis was performed to identify significant pathways by using the program MAGMA. An SNP annotation program was used to analyze MAGMA-identified genes. Differential gene expression was determined for significant genes across 30 general tissue types using the Functional and Annotation Mapping of GWAS online tool GENE2FUNC. COVID-19 comorbidities (n = 22) from six disease categories were found to have significant associated pathways, validated by Q-Q plots (p < 0.05). Protein-protein interactions of significant (p < 0.05) differentially expressed genes were visualized with the STRING program. Gene interaction networks were found to be relevant to SARS and influenza pathogenesis. In conclusion, we were able to identify the pathways potentially affected by or affecting SARS-CoV-2 infection in underlying medical conditions likely to confer susceptibility and/or the severity of COVID-19. Our findings have implications in future COVID-19 experimental research and treatment development.

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI identification is still timeconsuming and expensive. Therefore, it is of great significance to develop effective computational methods for identifying DTIs. In this paper, we develop a novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end-to-end graph (EEG)-DTI. Given a heterogeneous network containing multiple types of biological entities (i.e. drug, protein, disease, side-effect), EEG-DTI learns the low-dimensional feature representation of drugs and targets using a graph convolutional networks-based model and predicts DTIs based on the learned features. During the training process, EEG-DTI learns the feature representation of nodes in an end-to-end mode. The evaluation test shows that EEG-DTI performs better than existing state-of-art methods. The data and source code are available at: https://github.com/MedicineBiology-AI/EEG-DTI.

Applications of Machine Learning in Drug Target Discovery

Drug target discovery is a critical step in drug development. It is the basis of modern drug development because it determines the target molecules related to specific diseases in advance. Predicting drug targets by computational methods saves a great deal of financial and material resources compared to in vitro experiments. Therefore, several computational methods for drug target discovery have been designed. Recently, machine learning (ML) methods in biomedicine have developed rapidly. In this paper, we present an overview of drug target discovery methods based on machine learning. Considering that some machine learning methods integrate network analysis to predict drug targets, network-based methods are also introduced in this article. Finally, the challenges and future outlook of drug target discovery are discussed.

Turning genome-wide association study findings into opportunities for drug repositioning

Drug development is a very costly and lengthy process, while repositioned or repurposed drugs could be brought into clinical practice within a shorter time-frame and at a much reduced cost. Numerous computational approaches to drug repositioning have been developed, but methods utilizing genome-wide association studies (GWASs) data are less explored.

The past decade has observed a massive growth in the amount of data from GWAS; the rich information contained in GWAS has great potential to guide drug repositioning or discovery. While multiple tools are available for finding the most relevant genes from GWAS hits, searching for top susceptibility genes is only one way to guide repositioning, which has its own limitations.

Here we provide a comprehensive review of different computational approaches that employ GWAS data to guide drug repositioning. These methods include selecting top candidate genes from GWAS as drug targets, deducing drug candidates based on drug-drug and disease-disease similarities, searching for reversed expression profiles between drugs and diseases, pathway-based methods as well as approaches based on analysis of biological networks. Each method is illustrated with examples, and their respective strengths and limitations are discussed. We also discussed several areas for future research.

Machine learning approaches and databases for prediction of drug-target interaction: a survey paper

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

Drug-target interaction prediction using Multi Graph Regularized Nuclear Norm Minimization

The identification of potential interactions between drugs and target proteins is crucial in pharmaceutical sciences. The experimental validation of interactions in genomic drug discovery is laborious and expensive; hence, there is a need for efficient and accurate in-silico techniques which can predict potential drug-target interactions to narrow down the search space for experimental verification. In this work, we propose a new framework, namely, Multi-Graph Regularized Nuclear Norm Minimization, which predicts the interactions between drugs and target proteins from three inputs: known drug-target interaction network, similarities over drugs and those over targets. The proposed method focuses on finding a low-rank interaction matrix that is structured by the proximities of drugs and targets encoded by graphs. Previous works on Drug Target Interaction (DTI) prediction have shown that incorporating drug and target similarities helps in learning the data manifold better by preserving the local geometries of the original data. But, there is no clear consensus on which kind and what combination of similarities would best assist the prediction task. Hence, we propose to use various multiple drug-drug similarities and target-target similarities as multiple graph Laplacian (over drugs/targets) regularization terms to capture the proximities exhaustively. Extensive cross-validation experiments on four benchmark datasets using standard evaluation metrics (AUPR and AUC) show that the proposed algorithm improves the predictive performance and outperforms recent state-of-the-art computational methods by a large margin. Software is publicly available at https://github.com/aanchalMongia/MGRNNMforDTI.

Toxicities of 4,5-Dihydroisoxazoles Against Root-Knot Nematodes and in Silico Studies of Their Modes of Action

The present work sought to contribute to the development of new nematicides. Benzaldehydes were initially converted to nitrile oxides that underwent 1,3-dipolar cycloaddition reactions with methyl acrylate to generate 4,5-dihydroisoxazoles. In in vitro tests, methyl 3-phenyl-4,5-dihydroisoxazole-5-carboxylate (1) and methyl 3-(4-chlorophenyl)-4,5-dihydroisoxazole-5-carboxylate (4) increased the mortality of Meloidogyne exigua and Meloidogyne incognita second-stage juveniles (J2). Compounds 1 and 4 presented necessary concentrations of 398 and 501 μg mL^-1, respectively, to kill 50% of M. incognita J2 (LC₅₀ values), while the value for carbofuran (positive control) was 168 μg mL^-1. In in vivo tests, compounds 1 and 4 reduced the number of M. incognita galls in tomato roots by 70 and 40%, respectively, and the number of eggs by 89 and 44%. Using an in silico approach, we showed that compounds 1 and 4 were toxic to the nematodes by binding to the allosteric binding sites of the agonist-binding domains of the nematode nicotinic acetylcholine receptors. These results opened up possibilities for further investigations aimed at developing novel commercial nematicides.

Prioritizing target-disease associations with novel safety and efficacy scoring methods

Biological target (commonly genes or proteins) identification is still largely a manual process, where experts manually try to collect and combine information from hundreds of data sources, ranging from scientific publications to omics databases. Targeting the wrong gene or protein will lead to failure of the drug development process, as well as incur delays and costs. To improve this process, different software platforms are being developed. These platforms rely strongly on efficacy estimates based on target-disease association scores created by computational methods for drug target prioritization. Here novel computational methods are presented to more accurately evaluate the efficacy and safety of potential drug targets. The proposed efficacy scores utilize existing gene expression data and tissue/disease specific networks to improve the inference of target-disease associations. Conversely, safety scores enable the identification of genes that are essential, potentially susceptible to adverse effects or carcinogenic. Benchmark results demonstrate that our transcriptome-based methods for drug target prioritization can increase the true positive rate of target-disease associations. Additionally, the proposed safety evaluation system enables accurate predictions of targets of withdrawn drugs and targets of drug trials prematurely discontinued.

Combined Scaffold Evaluation and Systems-Level Transcriptome-Based Analysis for Accelerated Lead Optimization Reveals Ribosomal Targeting Spirooxindole Cyclopropanes

With evolutionary drug resistance impacting efforts to treat disease, the need for small molecules that exhibit novel molecular mechanisms of action is paramount. In this study, we combined scaffold-directed synthesis with a hybrid experimental and transcriptome analysis to identify bis-spirooxindole cyclopropanes that inhibit cancer cell proliferation through disruption of ribosomal function. These findings demonstrate the value of an integrated, biologically inspired synthesis and assay strategy for the accelerated identification of first-in-class cancer therapeutic candidates.

A review of network-based approaches to drug repositioning

Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.

Bioinformatics Identification of Drug Gene Modules: Application to Clear Cell Carcinoma of the Ovary

Targeted therapy is a treatment that targets the cancer's specific genes, proteins, or the tissue environment that contributes to cancer growth and survival. Identification of therapeutics targets is a very challenging problem in bioinformatics. An integrative and iterative approach for the identification of drug-gene modules (i.e., groups of genes and drugs such that genes in the same module may regulate each other and are targets of some of the drugs in the same module) is developed. Application to clear cell carcinoma of the ovary data reveals several drug-gene modules and a target network that may play important roles in treating this disease.

Prediction of Leishmania major Key Proteins Via Topological Analysis of Protein-Protein Interaction Network

Background:

Although leishmaniasis is regarded as a public health problem, no effective vaccine or decisive treatment has been introduced for this disease. Therefore, representing novel therapeutic proteins is essential. Protein-protein Interaction network analysis is a suitable tool to discover novel drug targets for leishmania major. To this aim, gene and protein expression data is used for instructing protein network and the key proteins are highlighted.

Materials and Methods:

In this computational and bioinformatics study, the protein/gene expression data related to leishmania major were studied, and 252 candidate proteins were extracted. Then, the protein networks of these proteins were explored and visualized by using String database and Cytoscape software. Finally, clustering and gene ontology were performed by MCODE and PANTHER databases, respectively.

Results:

Based on gene ontology analysis, most of the leishmania major proteins were located in cell compartments and membrane. Catalytic activity and binding were regarded as the relevant molecular functions and metabolic and cellular processes were the significant biological process. In this network analysis, UB-EP52, EF-2, chaperonin, Hsp70.4, Hsp60, tubulin alpha and beta chain, and ENOL and LACK were introduced as hub-bottleneck proteins. Based on clustering analysis, Lmjf.32.3270, ENOL and Lmjf.13.0290 were determined as seed proteins in each cluster.

Conclusion:

The results indicated that hub proteins play a significant role in pathogenesis and life cycle of leishmania major. Further studies of hubs will provide a better understanding of leishmaniasis mechanisms. Finally, these key hub proteins could be a suitable and helpful potential for drug targets and treating leishmaniasis by considering their validation.

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and drug-induced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction

In pharmaceutical sciences, a crucial step of the drug discovery process is the identification of drug-target interactions. However, only a small portion of the drug-target interactions have been experimentally validated, as the experimental validation is laborious and costly. To improve the drug discovery efficiency, there is a great need for the development of accurate computational approaches that can predict potential drug-target interactions to direct the experimental verification. In this paper, we propose a novel drug-target interaction prediction algorithm, namely neighborhood regularized logistic matrix factorization (NRLMF). Specifically, the proposed NRLMF method focuses on modeling the probability that a drug would interact with a target by logistic matrix factorization, where the properties of drugs and targets are represented by drug-specific and target-specific latent vectors, respectively. Moreover, NRLMF assigns higher importance levels to positive observations (i.e., the observed interacting drug-target pairs) than negative observations (i.e., the unknown pairs). Because the positive observations are already experimentally verified, they are usually more trustworthy. Furthermore, the local structure of the drug-target interaction data has also been exploited via neighborhood regularization to achieve better prediction accuracy. We conducted extensive experiments over four benchmark datasets, and NRLMF demonstrated its effectiveness compared with five state-of-the-art approaches.

This work introduces a computational approach, namely neighborhood regularized logistic matrix factorization (NRLMF), to predicting potential interactions between drugs and targets. The novelty of NRLMF lies in integrating logistic matrix factorization with neighborhood regularization for drug-target interaction prediction. In NRLMF, we model the interaction probability for each drug-target pair using logistic matrix factorization. As the observed interacting drug-target pairs are experimentally verified, they are more trustworthy than the unknown pairs. We propose to assign higher importance levels to interaction pairs and lower importance levels to unknown pairs. In addition, we further improve the prediction accuracy by neighborhood regularization, which considers the neighborhood influences from most similar drugs and most similar targets. To evaluate the performance of NRLMF, we conducted extensive experiments on four benchmark datasets. The experimental results demonstrated that NRLMF usually outperformed five state-of-the-art methods under three different cross-validation settings, in terms of the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPR). In addition, we confirmed the practical prediction ability of NRLMF by mapping with the latest version of four online biological databases, including ChEMBL, DrugBank, KEGG, and Matador.