scholarly journals Improving drug discovery using image-based multiparametric analysis of epigenetic landscape

2019 ◽  
Author(s):  
Chen Farhy ◽  
Luis Orozco ◽  
Fu-Yue Zeng ◽  
Ian Pass ◽  
Jarkko Ylanko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Nuclear Staining ◽  
Response Curves ◽  
Efficient Detection ◽  
Novel Approach ◽  
Novel Drug ◽  
Multiple Cells ◽  
Screening Approaches ◽  
Target Effects

AbstractWith the advent of automatic cell imaging and machine learning, high-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug specific multilayered data and compare it to known profiles. In the field of epigenetics such screening approaches has suffered from the lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach Microscopic Imaging of Epigenetic Landscapes (MIEL) that captures patterns of nuclear staining of epigenetic marks (e.g. acetylated and methylated histones) and employs machine learning to accurately distinguish between such patterns (1). We demonstrated that MIEL has superior resolution compared to conventional intensity thresholding techniques and enables efficient detection of epigenetically active compounds, function-based classification, flagging possible off-target effects and even predict novel drug function. We validated MIEL platform across multiple cells lines and using dose-response curves to insure the robustness of this approach for the high content high throughput drug discovery.

scholarly journals Improving drug discovery using image-based multiparametric analysis of the epigenetic landscape

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Chen Farhy ◽  
Santosh Hariharan ◽  
Jarkko Ylanko ◽  
Luis Orozco ◽  
Fu-Yue Zeng ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Chemotherapeutic Agents ◽  
Screening Methods ◽  
Nuclear Staining ◽  
Epigenetic Alterations ◽  
Epigenetic Landscape ◽  
Response Curves ◽  
Novel Approach ◽  
Increased Sensitivity ◽  
Multiple Cells

High-content phenotypic screening has become the approach of choice for drug discovery due to its ability to extract drug-specific multi-layered data. In the field of epigenetics, such screening methods have suffered from a lack of tools sensitive to selective epigenetic perturbations. Here we describe a novel approach, Microscopic Imaging of Epigenetic Landscapes (MIEL), which captures the nuclear staining patterns of epigenetic marks and employs machine learning to accurately distinguish between such patterns. We validated the MIEL platform across multiple cells lines and using dose-response curves, to insure the fidelity and robustness of this approach for high content high throughput drug discovery. Focusing on noncytotoxic glioblastoma treatments, we demonstrated that MIEL can identify and classify epigenetically active drugs. Furthermore, we show MIEL was able to accurately rank candidate drugs by their ability to produce desired epigenetic alterations consistent with increased sensitivity to chemotherapeutic agents or with induction of glioblastoma differentiation.

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.

Use of machine learning approaches for novel drug discovery

2016 ◽  
Vol 11 (3) ◽  
pp. 225-239 ◽  
Author(s):  
Angélica Nakagawa Lima ◽  
Eric Allison Philot ◽  
Gustavo Henrique Goulart Trossini ◽  
Luis Paulo Barbour Scott ◽  
Vinícius Gonçalves Maltarollo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Learning Approaches ◽  
Novel Drug

scholarly journals Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches

Molecules ◽  
2020 ◽  
Vol 25 (20) ◽  
pp. 4723 ◽  
Author(s):  
Javier Vázquez ◽  
Manel López ◽  
Enric Gibert ◽  
Enric Herrero ◽  
F. Javier Luque
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
Molecular Similarity ◽  
Computational Framework ◽  
Widespread Application ◽  
Virtual Libraries ◽  
Available Information ◽  
Novel Drug ◽  
Discovery Pipeline ◽  
Screening Approaches

Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.

Thermal Taste and Anti-Aspiration Drugs: A Novel Drug Discovery Against Pneumonia

2014 ◽  
Vol 20 (16) ◽  
pp. 2755-2759 ◽  
Author(s):  
Satoru Ebihara ◽  
Takae Ebihara ◽  
Peijun Gui ◽  
Ken Osaka ◽  
Yasunori Sumi ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Thermal Taste ◽  
Novel Drug

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

Epigenetics, Nutrition, Disease and Drug Development

2019 ◽  
Vol 16 (4) ◽  
pp. 386-391 ◽  
Author(s):  
Kenneth Lundstrom
Keyword(s):  
Dna Methylation ◽  
Drug Discovery ◽  
Rna Interference ◽  
Nutritional Requirements ◽  
Signal Pathways ◽  
Disease Development ◽  
Epigenetic Mechanisms ◽  
Epigenetic Changes ◽  
Health And Disease ◽  
Novel Drug

Epigenetic mechanisms comprising of DNA methylation, histone modifications and gene silencing by RNA interference have been strongly linked to the development and progression of various diseases. These findings have triggered research on epigenetic functions and signal pathways as targets for novel drug discovery. Dietary intake has also presented significant influence on human health and disease development and nutritional modifications have proven important in prevention, but also the treatment of disease. Moreover, a strong link between nutrition and epigenetic changes has been established. Therefore, in attempts to develop novel safer and more efficacious drugs, both nutritional requirements and epigenetic mechanisms need to be addressed.

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