scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

COMPUTATIONAL APPROACHES FOR DRUG DISCOVERY FROM MEDICINAL PLANTS IN THE ERA OF DATA DRIVEN RESEARCH

INDIAN DRUGS ◽  
2021 ◽  
Vol 58 (08) ◽  
pp. 7-23
Author(s):  
Pratibha Pansari ◽  
Keyword(s):  
Drug Discovery ◽  
Medicinal Plants ◽  
In Silico ◽  
Drug Target ◽  
Target Gene ◽  
Scientific Work ◽  
Data Driven ◽  
Network Pharmacology ◽  
Gene Pathway ◽  
Topological Network

The significant scientific work on the development of bio-active compound databases, computational technologies, and the integration of Information Technology with Biotechnology has brought a revolution in the domain of drug discovery. These tools facilitate the medicinal plant-based in silico drug discovery, which has become the frontier of pharmacological science. In this review article, we elucidate the methodology of in silico drug discovery for the medicinal plants and present an outlook on recent tools and technologies. Further, we explore the multi-component, multi-target, and multi-pathway mechanism of the bio-active compounds with the help of Network Pharmacology, which enables us to create a topological network between drug, target, gene, pathway, and disease.

scholarly journals Machine Learning Methods in Drug Discovery

Molecules ◽  
2020 ◽  
Vol 25 (22) ◽  
pp. 5277
Author(s):  
Lauv Patel ◽  
Tripti Shukla ◽  
Xiuzhen Huang ◽  
David W. Ussery ◽  
Shanzhi Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Learning Algorithms ◽  
Machine Learning Techniques ◽  
Online Information ◽  
Drug Candidates ◽  
Novel Drug

The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.

scholarly journals Unbiased Identification of Extracellular Protein–Protein Interactions for Drug Target and Biologic Drug Discovery

2021 ◽  
Author(s):  
Shengya Cao ◽  
Nadia Martinez-Martin
Keyword(s):  
Drug Discovery ◽  
Protein Interactions ◽  
Drug Target ◽  
Drug Targets ◽  
Extracellular Protein ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Drug Target Discovery ◽  
Technological Improvements ◽  
Targeted Drug Discovery

Technological improvements in unbiased screening have accelerated drug target discovery. In particular, membrane-embedded and secreted proteins have gained attention because of their ability to orchestrate intercellular communication. Dysregulation of their extracellular protein–protein interactions (ePPIs) underlies the initiation and progression of many human diseases. Practically, ePPIs are also accessible for modulation by therapeutics since they operate outside of the plasma membrane. Therefore, it is unsurprising that while these proteins make up about 30% of human genes, they encompass the majority of drug targets approved by the FDA. Even so, most secreted and membrane proteins remain uncharacterized in terms of binding partners and cellular functions. To address this, a number of approaches have been developed to overcome challenges associated with membrane protein biology and ePPI discovery. This chapter will cover recent advances that use high-throughput methods to move towards the generation of a comprehensive network of ePPIs in humans for future targeted drug discovery.

scholarly journals Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0261267
Author(s):  
Elmira Nazarshodeh ◽  
Sayed-Amir Marashi ◽  
Sajjad Gharaghani
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Protein Structures ◽  
System Level ◽  
Metabolic Reconstruction ◽  
Structural Systems ◽  
Systems Pharmacology ◽  
Genome Scale

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.

scholarly journals DTI-SNNFRA: Drug-target interaction prediction by shared nearest neighbors and fuzzy-rough approximation

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246920
Author(s):  
Sk Mazharul Islam ◽  
Sk Md Mosaddek Hossain ◽  
Sumanta Ray
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Repositioning ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Drug Repurposing ◽  
Search Space ◽  
Classification Model ◽  
Target Interaction ◽  
Rough Approximation

In-silico prediction of repurposable drugs is an effective drug discovery strategy that supplements de-nevo drug discovery from scratch. Reduced development time, less cost and absence of severe side effects are significant advantages of using drug repositioning. Most recent and most advanced artificial intelligence (AI) approaches have boosted drug repurposing in terms of throughput and accuracy enormously. However, with the growing number of drugs, targets and their massive interactions produce imbalanced data which may not be suitable as input to the classification model directly. Here, we have proposed DTI-SNNFRA, a framework for predicting drug-target interaction (DTI), based on shared nearest neighbour (SNN) and fuzzy-rough approximation (FRA). It uses sampling techniques to collectively reduce the vast search space covering the available drugs, targets and millions of interactions between them. DTI-SNNFRA operates in two stages: first, it uses SNN followed by a partitioning clustering for sampling the search space. Next, it computes the degree of fuzzy-rough approximations and proper degree threshold selection for the negative samples’ undersampling from all possible interaction pairs between drugs and targets obtained in the first stage. Finally, classification is performed using the positive and selected negative samples. We have evaluated the efficacy of DTI-SNNFRA using AUC (Area under ROC Curve), Geometric Mean, and F1 Score. The model performs exceptionally well with a high prediction score of 0.95 for ROC-AUC. The predicted drug-target interactions are validated through an existing drug-target database (Connectivity Map (Cmap)).

In Silico Chemogenomics Drug Repositioning Strategies for Neglected Tropical Diseases

2019 ◽  
Vol 26 (23) ◽  
pp. 4355-4379 ◽  
Author(s):  
Carolina Horta Andrade ◽  
Bruno Junior Neves ◽  
Cleber Camilo Melo-Filho ◽  
Juliana Rodrigues ◽  
Diego Cabral Silva ◽  
...  
Keyword(s):  
High Throughput Screening ◽  
Drug Targets ◽  
Neglected Tropical Diseases ◽  
Drug Repositioning ◽  
Tropical Diseases ◽  
Systematic Screening ◽  
Drug Candidates ◽  
Lead Compounds ◽  
The One ◽  
Approved Drugs

Only ~1% of all drug candidates against Neglected Tropical Diseases (NTDs) have reached clinical trials in the last decades, underscoring the need for new, safe and effective treatments. In such context, drug repositioning, which allows finding novel indications for approved drugs whose pharmacokinetic and safety profiles are already known, emerging as a promising strategy for tackling NTDs. Chemogenomics is a direct descendent of the typical drug discovery process that involves the systematic screening of chemical compounds against drug targets in high-throughput screening (HTS) efforts, for the identification of lead compounds. However, different to the one-drug-one-target paradigm, chemogenomics attempts to identify all potential ligands for all possible targets and diseases. In this review, we summarize current methodological development efforts in drug repositioning that use state-of-the-art computational ligand- and structure-based chemogenomics approaches. Furthermore, we highlighted the recent progress in computational drug repositioning for some NTDs, based on curation and modeling of genomic, biological, and chemical data. Additionally, we also present in-house and other successful examples and suggest possible solutions to existing pitfalls.

Established and In-trial GPCR Families in Clinical Trials: A Review for Target Selection

2019 ◽  
Vol 20 (5) ◽  
pp. 522-539 ◽  
Author(s):  
Surovi Saikia ◽  
Manobjyoti Bordoloi ◽  
Rajeev Sarmah
Keyword(s):  
Clinical Trials ◽  
Drug Discovery ◽  
Phase I ◽  
Human Genome ◽  
Phase Ii ◽  
Drug Targets ◽  
Complex Diseases ◽  
Phase Iii ◽  
Computational Techniques ◽  
Receptor Complexes

The largest family of drug targets in clinical trials constitute of GPCRs (G-protein coupled receptors) which accounts for about 34% of FDA (Food and Drug Administration) approved drugs acting on 108 unique GPCRs. Factors such as readily identifiable conserved motif in structures, 127 orphan GPCRs despite various de-orphaning techniques, directed functional antibodies for validation as drug targets, etc. has widened their therapeutic windows. The availability of 44 crystal structures of unique receptors, unexplored non-olfactory GPCRs (encoded by 50% of the human genome) and 205 ligand receptor complexes now present a strong foundation for structure-based drug discovery and design. The growing impact of polypharmacology for complex diseases like schizophrenia, cancer etc. warrants the need for novel targets and considering the undiscriminating and selectivity of GPCRs, they can fulfill this purpose. Again, natural genetic variations within the human genome sometimes delude the therapeutic expectations of some drugs, resulting in medication response differences and ADRs (adverse drug reactions). Around ~30 billion US dollars are dumped annually for poor accounting of ADRs in the US alone. To curb such undesirable reactions, the knowledge of established and currently in clinical trials GPCRs families can offer huge understanding towards the drug designing prospects including “off-target” effects reducing economical resource and time. The druggability of GPCR protein families and critical roles played by them in complex diseases are explained. Class A, class B1, class C and class F are generally established family and GPCRs in phase I (19%), phase II(29%), phase III(52%) studies are also reviewed. From the phase I studies, frizzled receptors accounted for the highest in trial targets, neuropeptides in phase II and melanocortin in phase III studies. Also, the bioapplications for nanoparticles along with future prospects for both nanomedicine and GPCR drug industry are discussed. Further, the use of computational techniques and methods employed for different target validations are also reviewed along with their future potential for the GPCR based drug discovery.

Essential gene identification and drug target prioritization in Leishmania species

2014 ◽  
Vol 10 (5) ◽  
pp. 1184-1195 ◽  
Author(s):  
M. L. Stanly Paul ◽  
Amandeep Kaur ◽  
Ankit Geete ◽  
M. Elizabeth Sobhia
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Targets ◽  
Essential Gene ◽  
Leishmania Species ◽  
Gene Identification ◽  
Specific Drug

New stage specific drug targets for contemporary drug discovery for leishmaniasis.

scholarly journals Transforming TRP Channel Drug Discovery Using Medium-Throughput Electrophysiological Assays

2013 ◽  
Vol 19 (3) ◽  
pp. 468-477 ◽  
Author(s):  
Jean-Marie Chambard ◽  
Eric Tagat ◽  
Philippe Boudeau ◽  
Michel Partiseti
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Targets ◽  
Transient Receptor Potential ◽  
Inflammatory Diseases ◽  
Receptor Potential ◽  
Target Class ◽  
Screening Assays ◽  
Transient Receptor ◽  
Almost All

Since the cloning of its first member in 1998, transient receptor potential (TRP) cation channels have become one of the most studied ion channel families in drug discovery. These channels, almost all calcium permeant, have been studied in many different (patho)-physiological and therapeutic areas as diverse as pain; neurodegenerative, cardiovascular, and inflammatory diseases; and cancer. At the same time, implementation of automated electrophysiology screening platforms has significantly increased the tractability of ion channels, mainly voltage gated, as drug targets. The work presented in this article shows the design and validation of TRP screening assays using the IonWorks Quattro platform (Molecular Devices, Sunnyvale, CA), allowing a significant increase in throughput to support drug discovery programs. This new player has a direct impact on resources and timelines by prioritizing potential candidates and reducing the number of molecules requiring final testing by manual patch-clamp, which is still today the gold standard technology for this challenging drug target class.

An in silico structure-based approach to anti-infective drug discovery

Parasitology ◽  
2013 ◽  
Vol 141 (1) ◽  
pp. 17-27 ◽  
Author(s):  
FRASER CUNNINGHAM ◽  
MARTIN J. McPHILLIE ◽  
A. PETER JOHNSON ◽  
COLIN W. G. FISHWICK
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
High Throughput Screening ◽  
Drug Targets ◽  
Enzyme Inhibitors ◽  
Cell Activity ◽  
Structure Based Drug Design ◽  
Lead Compounds ◽  
Target Mode ◽  
Screening Approaches

SUMMARYIn light of the low success rate of target-based genomics and HTS (High Throughput Screening) approaches in anti-infective drug discovery, in silico structure-based drug design (SBDD) is becoming increasingly prominent at the forefront of drug discovery. In silico SBDD can be used to identify novel enzyme inhibitors rapidly, where the strength of this approach lies with its ability to model and predict the outcome of protein-ligand binding. Over the past 10 years, our group have applied this approach to a diverse number of anti-infective drug targets ranging from bacterial D-ala-D-ala ligase to Plasmodium falciparum DHODH. Our search for new inhibitors has produced lead compounds with both enzyme and whole-cell activity with established on-target mode of action. This has been achieved with greater speed and efficiency compared with the more traditional HTS initiatives and at significantly reduced cost and manpower.

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