scholarly journals MEMES: Machine learning framework for Enhanced MolEcular Screening

2021 ◽  
Author(s):  
Sarvesh Mehta ◽  
Siddhartha Laghuvarapu ◽  
Yashaswi Pathak ◽  
Aaftaab Sethi ◽  
Mallika Alvala ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
Molecular Screening ◽  
Learning Framework ◽  
High Throughput Virtual Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule...

Accelerating the discovery of energetic melt-castable materials by a high-throughput virtual screening and experimental approach

2021 ◽  
Author(s):  
Siwei Song ◽  
Fang Chen ◽  
Yi Wang ◽  
Kangcai Wang ◽  
Mi Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Experimental Approach ◽  
Chemical Data ◽  
Research Paradigm ◽  
New Materials ◽  
High Throughput Virtual Screening

With the growth of chemical data, computation power and algorithms, machine learning-assisted high-throughput virtual screening (ML-assisted HTVS) is revolutionizing the research paradigm of new materials. Herein, a combined ML-assisted HTVS...

scholarly journals Structure-based high throughput virtual screening for drug discovery

2002 ◽  
Vol 58 (s1) ◽  
pp. c67-c67
Author(s):  
H. Jiang ◽  
J. Shen ◽  
X. Luo ◽  
H. Liu ◽  
F. Chen ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Virtual Screening ◽  
High Throughput ◽  
High Throughput Virtual Screening

scholarly journals Accelerating high-throughput virtual screening through molecular pool-based active learning

2021 ◽  
Author(s):  
David E. Graff ◽  
Eugene I. Shakhnovich ◽  
Connor W Coley
Keyword(s):  
Drug Discovery ◽  
Active Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Early Stage ◽  
Target Protein ◽  
Virtual Libraries ◽  
High Throughput Virtual Screening

Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in...

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.

Novel thermally activated delayed fluorescence materials by high-throughput virtual screening: going beyond donor–acceptor design

2021 ◽  
Vol 9 (9) ◽  
pp. 3324-3333 ◽  
Author(s):  
Ke Zhao ◽  
Ömer H. Omar ◽  
Tahereh Nematiaram ◽  
Daniele Padula ◽  
Alessandro Troisi
Keyword(s):  
Virtual Screening ◽  
Quantum Chemistry ◽  
High Throughput ◽  
Delayed Fluorescence ◽  
Thermally Activated Delayed Fluorescence ◽  
Thermally Activated ◽  
Quantum Chemistry Calculations ◽  
Donor Acceptor ◽  
Molecular Semiconductors ◽  
High Throughput Virtual Screening

125 potential TADF candidates are identified through quantum chemistry calculations of 700 molecules derived from a database of 40 000 molecular semiconductors. Most of them are new and some do not belong to the class of donor–acceptor molecules.

A novel compound active against SARS-CoV-2 targeting Uridylate-specific endoribonuclease (NendoU/NSP15): In silico and in vitro investigations

2021 ◽  
Author(s):  
Sumit Kumar ◽  
Yash Gupta ◽  
Samantha Zak ◽  
Charu Upadhyay ◽  
Neha Sharma ◽  
...  
Keyword(s):  
Virtual Screening ◽  
High Throughput ◽  
In Silico ◽  
Specific Enzyme ◽  
Cyclic Phosphates ◽  
High Throughput Virtual Screening

NendoU (NSP15) is an Mn(2+)-dependent, uridylate-specific enzyme, which leaves 2'-3'-cyclic phosphates 5' to the cleaved bond. Our in-house library was subjected to high throughput virtual screening (HTVS) to identify compounds...

scholarly journals High-Throughput Virtual Screening for a New Class of Antagonist Targeting LasR of Pseudomonas aeruginosa

ACS Omega ◽  
2021 ◽  
Author(s):  
Aishwarya Vetrivel ◽  
Santhi Natchimuthu ◽  
Vidyalakshmi Subramanian ◽  
Rajeswari Murugesan
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Virtual Screening ◽  
High Throughput ◽  
New Class ◽  
High Throughput Virtual Screening
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