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Article ; Online: Development and evaluation of a java-based deep neural network method for drug response predictions.

Huang, Beibei / Fong, Lon W R / Chaudhari, Rajan / Zhang, Shuxing

Frontiers in artificial intelligence

2023 Volume 6, Page(s) 1069353

Abstract: Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This ...

Abstract	Accurate prediction of drug response is a crucial step in personalized medicine. Recently, deep learning techniques have been witnessed with significant breakthroughs in a variety of areas including biomedical research and chemogenomic applications. This motivated us to develop a novel deep learning platform to accurately and reliably predict the response of cancer cells to different drug treatments. In the present work, we describe a Java-based implementation of deep neural network method, termed JavaDL, to predict cancer responses to drugs solely based on their chemical features. To this end, we devised a novel cost function and added a regularization term which suppresses overfitting. We also adopted an early stopping strategy to further reduce overfit and improve the accuracy and robustness of our models. To evaluate our method, we compared with several popular machine learning and deep neural network programs and observed that JavaDL either outperformed those methods in model building or obtained comparable predictions. Finally, JavaDL was employed to predict drug responses of several aggressive breast cancer cell lines, and the results showed robust and accurate predictions with
Language	English
Publishing date	2023-03-23
Publishing country	Switzerland
Document type	Journal Article
ISSN	2624-8212
ISSN (online)	2624-8212
DOI	10.3389/frai.2023.1069353
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Article ; Online: Structural Modeling and in Silico Screening of Potential Small-Molecule Allosteric Agonists of a Glucagon-like Peptide 1 Receptor

Tejashree Redij / Rajan Chaudhari / Zhiyu Li / Xianxin Hua / Zhijun Li

ACS Omega, Vol 4, Iss 1, Pp 961-

2019 Volume 970

Keywords	Chemistry ; QD1-999
Language	English
Publishing date	2019-01-01T00:00:00Z
Publisher	American Chemical Society
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: PyMine: a PyMOL plugin to integrate and visualize data for drug discovery.

Chaudhari, Rajan / Li, Zhijun

BMC research notes

2015 Volume 8, Page(s) 517

Abstract: Background: Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to ...

Abstract	Background: Tremendous amount of chemical and biological data are being generated by various high-throughput biotechnologies that could facilitate modern drug discovery. However, lack of integration makes it very challenging for individual scientists to access and understand all the data related to a specific protein of interest. Findings: To overcome this challenge, we developed PyMine, a PyMOL plugin that retrieves chemical, structural, pathway and other related biological data of a receptor and small molecules from a variety of high-quality databases and presents them in a graphic and uniformed way. Conclusions: Developed as an interactive and user-friendly tool, PyMine can be used as a central data-hub for users to access and visualize multiple types of data and to generate new ideas intuitively for structure-based molecule design.
MeSH term(s)	Drug Discovery ; Software ; Statistics as Topic
Language	English
Publishing date	2015-10-01
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2413336-X
ISSN	1756-0500 ; 1756-0500
ISSN (online)	1756-0500
ISSN	1756-0500
DOI	10.1186/s13104-015-1483-3
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Book ; Online: Designing Counterfactual Generators using Deep Model Inversion

Thiagarajan, Jayaraman J. / Narayanaswamy, Vivek / Rajan, Deepta / Liang, Jason / Chaudhari, Akshay / Spanias, Andreas

2021

Abstract: Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models. Commonly referred to as counterfactuals, the synthesized ... ...

Abstract	Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting black-box models. Commonly referred to as counterfactuals, the synthesized explanations are required to contain discernible changes (for easy interpretability) while also being realistic (consistency to the data manifold). In this paper, we focus on the case where we have access only to the trained deep classifier and not the actual training data. While the problem of inverting deep models to synthesize images from the training distribution has been explored, our goal is to develop a deep inversion approach to generate counterfactual explanations for a given query image. Despite their effectiveness in conditional image synthesis, we show that existing deep inversion methods are insufficient for producing meaningful counterfactuals. We propose DISC (Deep Inversion for Synthesizing Counterfactuals) that improves upon deep inversion by utilizing (a) stronger image priors, (b) incorporating a novel manifold consistency objective and (c) adopting a progressive optimization strategy. We find that, in addition to producing visually meaningful explanations, the counterfactuals from DISC are effective at learning classifier decision boundaries and are robust to unknown test-time corruptions. Comment: Neurips 2021
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition
Subject code	006
Publishing date	2021-09-29
Publishing country	us
Document type	Book ; Online
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Article ; Online: An up-to-date overview of computational polypharmacology in modern drug discovery.

Chaudhari, Rajan / Fong, Long Wolf / Tan, Zhi / Huang, Beibei / Zhang, Shuxing

Expert opinion on drug discovery

2020 Volume 15, Issue 9, Page(s) 1025–1044

Abstract: Introduction: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug ... ...

Abstract	Introduction: In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success. Areas covered: In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies. Expert opinion: Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of
MeSH term(s)	Computational Biology ; Computational Chemistry ; Drug Development ; Drug Discovery ; Humans ; Molecular Targeted Therapy ; Polypharmacology
Keywords	covid19
Language	English
Publishing date	2020-05-26
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2020.1767063
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Article ; Online: Structural Modeling and in Silico Screening of Potential Small-Molecule Allosteric Agonists of a Glucagon-like Peptide 1 Receptor.

Redij, Tejashree / Chaudhari, Rajan / Li, Zhiyu / Hua, Xianxin / Li, Zhijun

ACS omega

2019 Volume 4, Issue 1, Page(s) 961–970

Abstract: The glucagon-like peptide 1 receptor (GLP-1R) belongs to the pharmaceutically important class B family of G-protein-coupled receptors (GPCRs), and its incretin peptide ligand GLP-1 analogs are adopted drugs for the treatment of type 2 diabetes. Despite ... ...

Abstract	The glucagon-like peptide 1 receptor (GLP-1R) belongs to the pharmaceutically important class B family of G-protein-coupled receptors (GPCRs), and its incretin peptide ligand GLP-1 analogs are adopted drugs for the treatment of type 2 diabetes. Despite remarkable antidiabetic effects, GLP-1 peptide-based drugs are limited by the need of injection. On the other hand, developing nonpeptidic small-molecule drugs targeting GLP-1R remains elusive. Here, we first constructed a three-dimensional structure model of the transmembrane (TM) domain of human GLP-1R using homology modeling and conformational sampling techniques. Next, a potential allosteric binding site on the TM domain was predicted computationally. In silico screening of druglike compounds against this predicted allosteric site has identified nine compounds as potential GLP-1R agonists. The independent agonistic activity of two compounds was subsequently confirmed using a cAMP response element-based luciferase reporting system. One compound was also shown to stimulate insulin secretion through in vitro assay. In addition, this compound synergized with GLP-1 to activate human GLP-1R. These results demonstrated that allosteric regulation potentially exists in GLP-1R and can be exploited for developing small-molecule agonists. The success of this work will help pave the way for small-molecule drug discovery targeting other class B GPCRs through allosteric regulations.
Language	English
Publishing date	2019-01-11
Publishing country	United States
Document type	Journal Article
ISSN	2470-1343
ISSN (online)	2470-1343
DOI	10.1021/acsomega.8b03052
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Book ; Online: Development of End-to-End Low-Cost IoT System for Densely Deployed PM Monitoring Network

Parmar, Ayu / Sara, Spanddhana / Dwivedi, Ayush Kumar / Reddy, C. Rajashekar / Patwardhan, Ishan / Bijjam, Sai Dinesh / Chaudhari, Sachin / Rajan, K. S. / Vemuri, Kavita

An Indian Case Study

2022

Abstract: Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are ... ...

Abstract	Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatio-temporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization. Comment: Submitted to IEEE IoT Journal for review
Keywords	Electrical Engineering and Systems Science - Systems and Control
Publishing date	2022-11-29
Publishing country	us
Document type	Book ; Online
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Article ; Online: Experimental validation of influenza A virus matrix protein (M1) interaction with host cellular alpha enolase and pyruvate kinase.

Mishra, Shruti / Goyal, Priya / Kumar, Deepshikha / Chaudhari, Rajan / Rajala, Maitreyi S

Virology

2020 Volume 549, Page(s) 59–67

Abstract: Influenza A virus, a respiratory pathogen manipulates various host cellular processes to establish a successful infection in a host. We had reported earlier the interaction of influenza A virus nucleoprotein with host glycolytic enzymes; alpha enolase ... ...

Abstract	Influenza A virus, a respiratory pathogen manipulates various host cellular processes to establish a successful infection in a host. We had reported earlier the interaction of influenza A virus nucleoprotein with host glycolytic enzymes; alpha enolase and pyruvate kinase in A549 cells. Matrix protein (M1), another multifunctional protein encoded by genome segment 7 forms the inner layer of the virion and interacts with the ribonucleoprotein complex. Nucleoprotein and matrix protein, major structural components of the virion together contribute to the stability of the capsid. Thus, we have investigated the interaction of viral matrix protein with host glycolytic enzymes; alpha enolase and pyruvate kinase. Results had demonstrated differential expression of these two glycolytic enzymes in response to matrix protein and their interaction with matrix protein by in vitro binding, co-immunoprecipitation and co-localization studies. Our results confirmed that viral matrix protein interacts with host glycolytic enzymes in association with viral nucleoprotein.
MeSH term(s)	A549 Cells ; Cloning, Molecular ; Escherichia coli/genetics ; Escherichia coli/metabolism ; Gene Expression ; Gene Expression Regulation ; Genetic Vectors/chemistry ; Genetic Vectors/metabolism ; Host-Pathogen Interactions/genetics ; Humans ; Influenza A Virus, H1N1 Subtype/genetics ; Influenza A Virus, H1N1 Subtype/metabolism ; Nucleocapsid Proteins/genetics ; Nucleocapsid Proteins/metabolism ; Phosphopyruvate Hydratase/genetics ; Phosphopyruvate Hydratase/metabolism ; Protein Binding ; Pyruvate Kinase/genetics ; Pyruvate Kinase/metabolism ; Recombinant Fusion Proteins/genetics ; Recombinant Fusion Proteins/metabolism ; Signal Transduction ; Viral Matrix Proteins/genetics ; Viral Matrix Proteins/metabolism ; Virion/genetics ; Virion/metabolism
Chemical Substances	M1 protein, Influenza A virus ; Nucleocapsid Proteins ; Recombinant Fusion Proteins ; Viral Matrix Proteins ; Pyruvate Kinase (EC 2.7.1.40) ; Phosphopyruvate Hydratase (EC 4.2.1.11)
Language	English
Publishing date	2020-08-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	200425-2
ISSN	1096-0341 ; 0042-6822
ISSN (online)	1096-0341
ISSN	0042-6822
DOI	10.1016/j.virol.2020.07.019
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Article ; Online: Improved method for preparing Ni(II) complex of (S)-tyrosine Schiff base and its use in the automated synthesis of O-(2'-[

Lakshminarayanan, N / Kumar, Amit / Roy, Sushant / Pawar, Yogita / Chaudhari, Pradip / Rajan, M G R

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine

2017 Volume 127, Page(s) 122–129

Abstract: O-(2'-[ ...

Abstract	O-(2'-[
Language	English
Publishing date	2017-09
Publishing country	England
Document type	Journal Article
ZDB-ID	1142596-9
ISSN	1872-9800 ; 0883-2889 ; 0969-8043
ISSN (online)	1872-9800
ISSN	0883-2889 ; 0969-8043
DOI	10.1016/j.apradiso.2017.05.017
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Article ; Online: Computational polypharmacology: a new paradigm for drug discovery.

Chaudhari, Rajan / Tan, Zhi / Huang, Beibei / Zhang, Shuxing

Expert opinion on drug discovery

2017 Volume 12, Issue 3, Page(s) 279–291

Abstract: Introduction: Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to ... ...

Abstract	Introduction: Over the past couple of years, the cost of drug development has sharply increased along with the high rate of clinical trial failures. Such increase in expenses is partially due to the inability of the "one drug - one target" approach to predict drug side effects and toxicities. To tackle this issue, an alternative approach, known as polypharmacology, is being adopted to study small molecule interactions with multiple targets. Apart from developing more potent and effective drugs, this approach allows for studies of off-target activities and the facilitation of drug repositioning. Although exhaustive polypharmacology studies in-vitro or in-vivo are not practical, computational methods of predicting unknown targets or side effects are being developed. Areas covered: This article describes various computational approaches that have been developed to study polypharmacology profiles of small molecules. It also provides a brief description of the algorithms used in these state-of-the-art methods. Expert opinion: Recent success in computational prediction of multi-targeting drugs has established polypharmacology as a promising alternative approach to tackle some of the daunting complications in drug discovery. This will not only help discover more effective agents, but also present tremendous opportunities to study novel target pharmacology and facilitate drug repositioning efforts in the pharmaceutical industry.
MeSH term(s)	Algorithms ; Drug Design ; Drug Discovery/methods ; Drug Industry/methods ; Drug Repositioning/methods ; Drug-Related Side Effects and Adverse Reactions/diagnosis ; Humans ; Polypharmacology
Language	English
Publishing date	2017-01-23
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2259618-5
ISSN	1746-045X ; 1746-0441
ISSN (online)	1746-045X
ISSN	1746-0441
DOI	10.1080/17460441.2017.1280024
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