Big Data over Cloud: Enabling Drug Design Under Cellular Environment

2021 ◽  
pp. 279-295
Author(s):  
B. S. Sanjeev ◽  
Dheeraj Chitara
Keyword(s):  
Big Data ◽  
Drug Design ◽  
Cellular Environment

scholarly journals Correction to: Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

2018 ◽  
Vol 20 (4) ◽  
Author(s):  
Yankang Jing ◽  
Yuemin Bian ◽  
Ziheng Hu ◽  
Lirong Wang ◽  
Xiang-Qun Xie
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design

scholarly journals Integration of Cassandra and Spark in Computer Aided Drug Design

2021 ◽  
pp. 68-73
Author(s):  
Nitha V R
Keyword(s):  
Big Data ◽  
Molecular Modeling ◽  
Drug Design ◽  
Programming Languages ◽  
New Drugs ◽  
Java Virtual Machine ◽  
Management Tool ◽  
Computer Aided Drug Design ◽  
Modeling Tool ◽  
Computer Aided

The primary purpose of this paper is to provide feasibility study of Cassandra and spark in Computer Aided Drug Design (CADD). The Apache Cassandra database is a big data management tool which can be used to store huge amount of data in different file formats. A huge database can be designed with details of all known molecules or compounds that are existing on earth. The information regarding the compounds such as selectivity, solubility, synthetic viability, affinity, adverse reactions, metabolism and environmental toxicity along with the 3 D structure of molecule can be stored in this big database. A data analytics tool “spark” can be efficiently used in mining and managing huge data stored in the database. Integrating big data in CADD helps in identifying the candidate drugs within minutes, not years. It may take eight to fifteen years to develop a new drug traditionally. Spark is written in Scala Programming Language which runs on Java Virtual Machine (JVM) and it supports Scala, Java and Python Programming languages .Cassandra can provide connectors to different programming languages, hence it’s very easy to integrate any other molecular modeling tool with Spark. A python based molecular modeling tool called Pymol can be easily implemented with Spark. CADD helps in identifying new drugs by computational means thus eliminating unnecessary cost incurred in chemical testing of drugs.

scholarly journals Animal simulations facilitate smart drug design through prediction of nanomaterial transport to individual tissue cells

2020 ◽  
Vol 6 (4) ◽  
pp. eaax2642 ◽  
Author(s):  
Edward Price ◽  
Andre J. Gesquiere
Keyword(s):  
Drug Design ◽  
Fluid Dynamic ◽  
Single Cells ◽  
Whole Body ◽  
Target Tissue ◽  
Vitro Assay ◽  
Cellular Environment ◽  
Tissue Cells ◽  
Smart Drug

Smart drug design for antibody and nanomaterial-based therapies allows optimization of drug efficacy and more efficient early-stage preclinical trials. The ideal drug must display maximum efficacy at target tissue sites, with transport from tissue vasculature to the cellular environment being critical. Biological simulations, when coupled with in vitro approaches, can predict this exposure in a rapid and efficient manner. As a result, it becomes possible to predict drug biodistribution within single cells of live animal tissue without the need for animal studies. Here, we successfully utilized an in vitro assay and a computational fluid dynamic model to translate in vitro cell kinetics (accounting for cell-induced degradation) to whole-body simulations for multiple species as well as nanomaterial types to predict drug distribution into individual tissue cells. We expect this work to assist in refining, reducing, and replacing animal testing, while providing scientists with a new perspective during the drug development process.

scholarly journals Data-Driven Molecular Dynamics: A Multifaceted Challenge

2020 ◽  
Vol 13 (9) ◽  
pp. 253
Author(s):  
Mattia Bernetti ◽  
Martina Bertazzo ◽  
Matteo Masetti
Keyword(s):  
Molecular Dynamics ◽  
Big Data ◽  
Drug Discovery ◽  
Drug Design ◽  
Md Simulations ◽  
Biomolecular Systems ◽  
Biomolecular Simulations ◽  
The Impact ◽  
Opening Up ◽  
Computational Drug Discovery

The big data concept is currently revolutionizing several fields of science including drug discovery and development. While opening up new perspectives for better drug design and related strategies, big data analysis strongly challenges our current ability to manage and exploit an extraordinarily large and possibly diverse amount of information. The recent renewal of machine learning (ML)-based algorithms is key in providing the proper framework for addressing this issue. In this respect, the impact on the exploitation of molecular dynamics (MD) simulations, which have recently reached mainstream status in computational drug discovery, can be remarkable. Here, we review the recent progress in the use of ML methods coupled to biomolecular simulations with potentially relevant implications for drug design. Specifically, we show how different ML-based strategies can be applied to the outcome of MD simulations for gaining knowledge and enhancing sampling. Finally, we discuss how intrinsic limitations of MD in accurately modeling biomolecular systems can be alleviated by including information coming from experimental data.

Artificial intelligence facilitates drug design in the big data era

2019 ◽  
Vol 194 ◽  
pp. 103850 ◽  
Author(s):  
Liangliang Wang ◽  
Junjie Ding ◽  
Li Pan ◽  
Dongsheng Cao ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Drug Design

scholarly journals Deep Learning for Drug Design: an Artificial Intelligence Paradigm for Drug Discovery in the Big Data Era

2018 ◽  
Vol 20 (3) ◽  
Author(s):  
Yankang Jing ◽  
Yuemin Bian ◽  
Ziheng Hu ◽  
Lirong Wang ◽  
Xiang-Qun Sean Xie
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Design
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