An Epistemological Analysis of QSPR/QSAR Models

2011 ◽  
pp. 318-332
Author(s):  
Jordi Vallverdú
Keyword(s):  
Drug Design ◽  
Computer Science ◽  
In Silico ◽  
Research Field ◽  
Research Results ◽  
Qsar Models ◽  
Epistemological Analysis ◽  
Scientific Value ◽  
The Way

Computer sciences have deeply changed the way by which we make science or produce knowledge. With the era of computers and the development of computer science, quantum chemists were among the first scientists to explore the potentialities of the new tool, and even to collaborate in its development. In this way, they also became participants in what many dubbed as the Second Instrumental Revolution in chemistry. Deeply involved into this research field, QSAR methods are powerful tools to create knowledge on toxicology and drug design, among others. There are several epistemological questions to be analyzed in order to understand the truth and scientific value of their research results (from in silico to wet laboratories and vice versa).

An Epistemological Analysis of QSPR/QSAR Models

2014 ◽  
pp. 1326-1341
Author(s):  
Jordi Vallverdú
Keyword(s):  
Drug Design ◽  
Computer Science ◽  
In Silico ◽  
Research Field ◽  
Research Results ◽  
Qsar Models ◽  
Epistemological Analysis ◽  
Scientific Value ◽  
The Way

Computer sciences have deeply changed the way by which we make science or produce knowledge. With the era of computers and the development of computer science, quantum chemists were among the first scientists to explore the potentialities of the new tool, and even to collaborate in its development. In this way, they also became participants in what many dubbed as the Second Instrumental Revolution in chemistry. Deeply involved into this research field, QSAR methods are powerful tools to create knowledge on toxicology and drug design, among others. There are several epistemological questions to be analyzed in order to understand the truth and scientific value of their research results (from in silico to wet laboratories and vice versa).

Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents

2012 ◽  
Vol 20 (15) ◽  
pp. 4848-4855 ◽  
Author(s):  
Alejandro Speck-Planche ◽  
Valeria V. Kleandrova ◽  
Feng Luan ◽  
M. Natália D.S. Cordeiro
Keyword(s):  
Colorectal Cancer ◽  
Drug Design ◽  
Cancer Chemotherapy ◽  
In Silico ◽  
Rational Drug Design ◽  
Rational Drug ◽  
Anti Cancer ◽  
Qsar Models

Design and development of novel antibiotics based on FtsZ inhibition – in silico studies

2018 ◽  
Vol 42 (13) ◽  
pp. 10976-10982 ◽  
Author(s):  
Aleksandar M. Veselinović ◽  
Andrey Toropov ◽  
Alla Toropova ◽  
Dobrila Stanković-Đorđević ◽  
Jovana B. Veselinović
Keyword(s):  
Molecular Docking ◽  
Drug Design ◽  
In Silico ◽  
Design And Development ◽  
Computer Aided Drug Design ◽  
In Silico Studies ◽  
Computer Aided ◽  
Qsar Models ◽  
Ftsz Inhibitors ◽  
Novel Antibiotics

QSAR models, computer-aided drug design and the application of molecular docking were used to evaluate benzamide analogues as FtsZ inhibitors.

scholarly journals Multi-dimensional database technology based on artificial intelligence

2021 ◽  
Vol 336 ◽  
pp. 05001
Author(s):  
Yurong Liu ◽  
Yang Bai ◽  
Xiongjun Li ◽  
Hao Chen
Keyword(s):  
Artificial Intelligence ◽  
Computer Science ◽  
Research Direction ◽  
Research Field ◽  
Computer Application ◽  
Combination Strategy ◽  
Research Results ◽  
Multidimensional Database ◽  
Database Technology

Artificial intelligence and database technology are both important fields of computer science. The research results show that more and more industries have a growing demand for artificial intelligence and database. The combination of these two technologies will certainly bring a broader prospect to computer application. This paper mainly discusses the necessity, importance and development of multi-dimensional database technology based on artificial intelligence, introduces the combination strategy of artificial intelligence and multidimensional database and the current research field, and gives the research direction.

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

Appraisal of the role of In silico Methods in Pyrazole based drug design

2020 ◽  
Vol 20 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Drug Design ◽  
In Silico ◽  
Md Simulations ◽  
Quantitative Structure Activity Relationship ◽  
Field Analysis ◽  
Computational Techniques ◽  
Neuroprotective Drugs ◽  
Pharmacological Potential ◽  
Available Information ◽  
Anti Fungal

: Pyrazole and its derivatives are a pharmacologically significant active scaffold that have innumerable physiological and pharmacological activities. They can be very good targets for the discovery of novel anti-bacterial, anticancer, anti-inflammatory, anti-fungal, anti-tubercular, antiviral, antioxidant, antidepressant, anti-convulsant and neuroprotective drugs. This review focuses on the importance of in silico manipulations of pyrazole and its derivatives for medicinal chemistry. The authors have discussed currently available information on the use of computational techniques like molecular docking, structure-based virtual screening (SBVS), molecular dynamics (MD) simulations, quantitative structure activity relationship (QSAR), comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) to drug design using pyrazole moieties. Pyrazole based drug design is mainly dependent on the integration of experimental and computational approaches. The authors feel that more studies need to be done to fully explore the pharmacological potential of the pyrazole moiety and in silico method can be of great help.

A Comparative Study of 1D Descriptors Supported CoMFA and CoMSIA QSAR Models to Gain Novel Insights into 1,2,4-Triazoles Acting As Antitubercular Agents

2020 ◽  
Vol 16 ◽  
Author(s):  
Rajdeep Ray ◽  
Gautham Shenoy ◽  
N V Ganesh Kumar Tummalapalli
Keyword(s):  
External Validation ◽  
Antitubercular Activity ◽  
New Drugs ◽  
Rational Approach ◽  
Antitubercular Drugs ◽  
Comfa Model ◽  
Qsar Models ◽  
Structural Insights ◽  
The Way

: Tuberculosis is one of the leading cause for deaths due to infectious disease worldwide. There is an urgent need for developing new drugs due to the rising incidents of drug resistance. Triazoles have previously been reported to show antitubercular activity. Various computational tools pave the way for a rational approach in understanding the structural importance of these compounds in inhibiting Mycobacterium tuberculosis growth. The aim of this study is to develop and compare two different QSAR models based on a set of previously reported molecules and use the best one for gaining structural insights in to the Triazole molecules. In the current study, two separate models were generated with CoMFA and CoMSIA descriptors respectively based on a dataset of triazole molecules showing antitubercular activity. Several one dimensional (1D) descriptors were added to each of the models and the validation results and the contour data generated from them were compared. The best model was studied to give a detailed understanding of the triazole molecules and their role in the antitubercular activity.The r2, q2, predicted r2 and SEP (Standard error of prediction) for the CoMFA model were 0.866, 0.573, 0.119 and 0.736 respectively and for the CoMSIA model the r2, q2, predicted r2 and SEP were calculated to be 0.998, 0.634, 0.013 and 0.869 respectively. Although both the QSAR models produced acceptable internal and external validation scores but the CoMSIA results were significantly better. The CoMSIA contours also provided a better match than CoMFA with most of the features of the active compound 30b. Hence, the CoMSIA model was chosen and its contours were explored for gaining structural insights on the triazole molecules. The CoMSIA contours helped us to understand the role of several atoms and groups of the triazole molecules in their biological activity. The possibilities for substitution in the triazole compounds that would enhance the activity were also analysed. Thus, this study paves the way for designing new antitubercular drugs in future.

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