Drug-Target Interactions: Prediction Methods and Applications

Shanmugam, Anusuya; Kesherwani, Manish; Priya, K. Vishnu; Vimala, A.; Shanmugam, Gnanendra; Velmurugan, D.; Gromiha, M. Michael

doi:10.2174/1389203718666161108091609

Cited by 34 publications

(19 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep learning approaches are already used to predict interaction of a chemical substance, like drug, with a target protein. Likely, deep learning approaches for prediction of drug-target interactions (Anusuya et al, 2018; Lee et al, 2019) and to annotate protein functions (Sureyya Rifaioglu et al, 2019) can be adapted to predict interaction of neurotransmitters or neuromodulators with proteins with unknown function. Thus, novel specific neurotransmitter-binding proteins can be found while limitations derived from use of GPCRs or finite number of bacterial proteins used in biosensor engineering could be overcome.…”

Section: Resultsmentioning

confidence: 99%

Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications

Leopold

Shcherbakova

Verkhusha

2019

Front. Cell. Neurosci.

View full text Add to dashboard Cite

Understanding how neuronal activity patterns in the brain correlate with complex behavior is one of the primary goals of modern neuroscience. Chemical transmission is the major way of communication between neurons, however, traditional methods of detection of neurotransmitter and neuromodulator transients in mammalian brain lack spatiotemporal precision. Modern fluorescent biosensors for neurotransmitters and neuromodulators allow monitoring chemical transmission in vivo with millisecond precision and single cell resolution. Changes in the fluorescent biosensor brightness occur upon neurotransmitter binding and can be detected using fiber photometry, stationary microscopy and miniaturized head-mounted microscopes. Biosensors can be expressed in the animal brain using adeno-associated viral vectors, and their cell-specific expression can be achieved with Cre-recombinase expressing animals. Although initially fluorescent biosensors for chemical transmission were represented by glutamate biosensors, nowadays biosensors for GABA, acetylcholine, glycine, norepinephrine, and dopamine are available as well. In this review, we overview functioning principles of existing intensiometric and ratiometric biosensors and provide brief insight into the variety of neurotransmitter-binding proteins from bacteria, plants, and eukaryotes including G-protein coupled receptors, which may serve as neurotransmitter-binding scaffolds. We next describe a workflow for development of neurotransmitter and neuromodulator biosensors. We then discuss advanced setups for functional imaging of neurotransmitter transients in the brain of awake freely moving animals. We conclude by providing application examples of biosensors for the studies of complex behavior with the single-neuron precision.

show abstract

Section: Resultsmentioning

confidence: 99%

Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications

Leopold

Shcherbakova

Verkhusha

2019

Front. Cell. Neurosci.

View full text Add to dashboard Cite

show abstract

“…In the identification of the novel targets of drugs, there has been increasing interest in predicting drug–target interaction (DTI), given its relevance for side effect prediction and drug‐repositioning attempts 101 . The availability of heterogeneous biological data on known DTI has enabled the development of various AI/ML‐based strategies to exploit unknown DTI, 102 including ensemble learning, 103–106 tree‐ensemble learning, 107 active learning, 108 DL, 109 end‐to‐end DL, 110 and kernel‐based learning 111–115 . Such AI/ML‐enabled data integration strategies outperform the traditional methods in classifying both positive and negative interactions, 110 improved the quality of the predicted interactions, and expedited the identification of new DTI 115 …”

Section: Ai/ml Applications In Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

175

View full text Add to dashboard Cite

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

show abstract

“…Most of these methods use three types of information which are: drug-related information (e.g., chemical information for drugs), target-related information (e.g., protein sequences), or/and known DTI information. These methods can be grouped under three main categories namely: machine learning (ML)-based methods [8][9][10][11][12], deep learning (DL)-based methods [13][14][15][16] (DL is a branch of ML), and network-based methods [17][18][19][20][21][22]. Several comprehensive review articles summarized, analyzed, and compared the methods belonging to these categories [23][24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

et al. 2020

View full text Add to dashboard Cite

In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drugtarget interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely: drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

show abstract

Drug-Target Interactions: Prediction Methods and Applications

Cited by 34 publications

References 0 publications

Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications

Fluorescent Biosensors for Neurotransmission and Neuromodulation: Engineering and Applications

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Contact Info

Product

Resources

About