Precise Hapten Design of Sulfonamides by Combining Machine Learning and 3D-QSAR Approaches

2022 ◽  
Author(s):  
Yan-ni Chen ◽  
Jie Qian ◽  
Rui Liang ◽  
Wen-bin Zeng ◽  
Jie Dong ◽  
...  
Keyword(s):  
Machine Learning ◽  
3D Qsar ◽  
Hapten Design

Integrated machine learning, molecular docking and 3D-QSAR based approach for identification of potential inhibitors of trypanosomal N-myristoyltransferase

2016 ◽  
Vol 12 (12) ◽  
pp. 3711-3723 ◽  
Author(s):  
Nidhi Singh ◽  
Priyanka Shah ◽  
Hemlata Dwivedi ◽  
Shikha Mishra ◽  
Renu Tripathi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Docking ◽  
In Silico ◽  
3D Qsar ◽  
Potential Inhibitors

Integrated in silico approaches for the identification of antitrypanosomal inhibitors.

scholarly journals Machine learning vs. field 3D-QSAR models for serotonin 2A receptor psychoactive substances identification

RSC Advances ◽  
2021 ◽  
Vol 11 (24) ◽  
pp. 14587-14595
Author(s):  
Giuseppe Floresta ◽  
Vincenzo Abbate
Keyword(s):  
Machine Learning ◽  
3D Qsar ◽  
New Psychoactive Substances ◽  
Psychoactive Substances ◽  
Serotonin 2A Receptor ◽  
Qsar Models ◽  
Serotonin 2A

Five QSAR models for predicting the affinity of 5-HT2AR ligands have been developed. The resulting models generate a useful tool for the investigation and identification of unclassified new psychoactive substances (NPS).

Development and application of a comprehensive machine learning program for predicting molecular biochemical and pharmacological properties

2019 ◽  
Vol 21 (9) ◽  
pp. 5189-5199 ◽  
Author(s):  
Hwanho Choi ◽  
Hongsuk Kang ◽  
Kee-Choo Chung ◽  
Hwangseo Park
Keyword(s):  
Machine Learning ◽  
Organic Molecules ◽  
3D Qsar ◽  
Qsar Model ◽  
Pharmacological Properties ◽  
Learning Program

We have developed and validated a comprehensive 3D-QSAR model for predicting various biochemical and pharmacological properties of organic molecules.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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