QSAR by Minimal Topological Difference[s]: Post-Modern Perspectives

2020 ◽  
Vol 27 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Corina Duda-Seiman ◽  
Daniel Duda-Seiman ◽  
Dan Ciubotariu ◽  
Mihai V. Putz
Keyword(s):  
Molecular Design ◽  
Three Dimensional ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Organic Chemical ◽  
Biological Interaction ◽  
Structure Activity ◽  
Receptor Sites ◽  
Key Features ◽  
Quantitative Treatment

In the context of reconsidering the Quantitative Structure-Activity Relationship (QSAR) methods at the economical level, namely the optimization rules of OECD, the present review unfolds the key features of Minimal Sterical, Monte-Carlo and Minimal Topological Difference (MTD) methods, developed for quantitative treatment of the relations between biological activity of organic chemical compounds (drugs, pesticides, and so on) and their structures. The initial Minimal Steric Difference (MSD) is completed by the three-dimensional variant of the MTD method, being the last one referred to here, while the main principles of validating and guiding a viable QSAR method verified by the analytical-automated MTD, thus enlarging the perspectives of understanding the chemical-biological interaction at the level of ligand-receptor sites, cavity, and walls, with a true service to the future adaptive molecular design.

scholarly journals Prediction Models for Agonists and Antagonists of Molecular Initiation Events for Toxicity Pathways Using an Improved Deep-Learning-Based Quantitative Structure–Activity Relationship System

2021 ◽  
Vol 22 (19) ◽  
pp. 10821
Author(s):  
Yasunari Matsuzaka ◽  
Shin Totoki ◽  
Kentaro Handa ◽  
Tetsuyoshi Shiota ◽  
Kota Kurosaki ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Throughput ◽  
Prediction Models ◽  
Three Dimensional ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
Antagonist Activity ◽  
Structure Activity ◽  
Agonist And Antagonist

In silico approaches have been studied intensively to assess the toxicological risk of various chemical compounds as alternatives to traditional in vivo animal tests. Among these approaches, quantitative structure–activity relationship (QSAR) analysis has the advantages that it is able to construct models to predict the biological properties of chemicals based on structural information. Previously, we reported a deep learning (DL) algorithm-based QSAR approach called DeepSnap-DL for high-performance prediction modeling of the agonist and antagonist activity of key molecules in molecular initiating events in toxicological pathways using optimized hyperparameters. In the present study, to achieve high throughput in the DeepSnap-DL system–which consists of the preparation of three-dimensional molecular structures of chemical compounds, the generation of snapshot images from the three-dimensional chemical structures, DL, and statistical calculations—we propose an improved DeepSnap-DL approach. Using this improved system, we constructed 59 prediction models for the agonist and antagonist activity of key molecules in the Tox21 10K library. The results indicate that modeling of the agonist and antagonist activity with high prediction performance and high throughput can be achieved by optimizing suitable parameters in the improved DeepSnap-DL system.

scholarly journals Molecular Shape Descriptors. 2. Quantitative Structure-Activity Relationships Based Upon Three-Dimensional Molecular Shape Descriptor

1985 ◽  
Vol 40 (11) ◽  
pp. 1114-1120
Author(s):  
loan Motoc ◽  
Garland R. Marshall
Keyword(s):  
Enzyme Inhibitor ◽  
Three Dimensional ◽  
Shape Descriptor ◽  
Molecular Shape ◽  
Quantitative Structure Activity Relationship ◽  
Quantitative Structure ◽  
E Coli ◽  
Interactional Pattern ◽  
Structure Activity ◽  
Quantitative Structure Activity Relationships

A methodology to incorporate the three-dimensional molecular shape descriptor (3 D-MSD) into a quantitative structure-activity relationship is discussed in detail. The 3 D-MSD is calculated and correlated with Kiapp values for a set of 2,4-diamino-5-benzylpyrimidines which inhibit E. coli DHFR. The correlation (n = 22, r = 0.95, s = 0.214, F = 55.10) indicates that the polarization interaction dominates the enzyme-inhibitor interactional pattern.

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