Rough Sets for Selection of Molecular Descriptors to Predict Biological Activity of Molecules

2010 ◽  
Vol 40 (6) ◽  
pp. 639-648 ◽  
Author(s):  
Pradipta Maji ◽  
Sushmita Paul
Keyword(s):  
Biological Activity ◽  
Rough Sets ◽  
Molecular Descriptors ◽  
Selection Of

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

2020 ◽  
Vol 10 (1) ◽  
pp. 44-60
Author(s):  
Mohamed E.I. Badawy ◽  
Entsar I. Rabea ◽  
Samir A.M. Abdelgaleil
Keyword(s):  
Biological Activity ◽  
Plant Pathogens ◽  
Molecular Descriptors ◽  
Microbial Pathogens ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Structure Activity ◽  
Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

scholarly journals Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elisabeth J. Schiessler ◽  
Tim Würger ◽  
Sviatlana V. Lamaka ◽  
Robert H. Meißner ◽  
Christian J. Cyron ◽  
...  
Keyword(s):  
Corrosion Inhibition ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Automatic Identification ◽  
Organic Additives ◽  
Statistical Tool ◽  
Degradation Behaviour ◽  
Corrosion Inhibition Efficiency ◽  
Selection Of

AbstractThe degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

scholarly journals Linear Diophantine Fuzzy Soft Rough Sets for the Selection of Sustainable Material Handling Equipment

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1215
Author(s):  
Muhammad Riaz ◽  
Masooma Raza Hashmi ◽  
Humaira Kalsoom ◽  
Dragan Pamucar ◽  
Yu-Ming Chu
Keyword(s):  
Fuzzy Sets ◽  
Rough Sets ◽  
Material Handling ◽  
Optimal Decision ◽  
Soft Sets ◽  
Sustainable Material ◽  
Material Handling Equipment ◽  
Core Set ◽  
Modeling Uncertainties ◽  
Selection Of

The concept of linear Diophantine fuzzy sets (LDFSs) is a new approach for modeling uncertainties in decision analysis. Due to the addition of reference or control parameters with membership and non-membership grades, LDFS is more flexible and reliable than existing concepts of intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and q-rung orthopair fuzzy sets (q-ROFSs). In this paper, the notions of linear Diophantine fuzzy soft rough sets (LDFSRSs) and soft rough linear Diophantine fuzzy sets (SRLDFSs) are proposed as new hybrid models of soft sets, rough sets, and LDFS. The suggested models of LDFSRSs and SRLDFSs are more flexible to discuss fuzziness and roughness in terms of upper and lower approximation operators. Certain operations on LDFSRSs and SRLDFSs have been established to discuss robust multi-criteria decision making (MCDM) for the selection of sustainable material handling equipment. For these objectives, some algorithms are developed for the ranking of feasible alternatives and deriving an optimal decision. Meanwhile, the ideas of the upper reduct, lower reduct, and core set are defined as key factors in the proposed MCDM technique. An application of MCDM is illustrated by a numerical example, and the final ranking in the selection of sustainable material handling equipment is computed by the proposed algorithms. Finally, a comparison analysis is given to justify the feasibility, reliability, and superiority of the proposed models.

Improving the Performance of an Associative Classifier by Gamma Rough Sets Based Instance Selection

2017 ◽  
Vol 32 (01) ◽  
pp. 1860009 ◽  
Author(s):  
Jarvin A. Antón-Vargas ◽  
Yenny Villuendas-Rey ◽  
Cornelio Yáñez-Márquez ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Nearest Neighbor ◽  
Management Information Systems ◽  
Computational Cost ◽  
Instance Selection ◽  
Management Information ◽  
Similarity Relations ◽  
The Universe ◽  
Selection Of

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT.

Affinity of Calciferol Analogues for 25-Hydroxycholecalciferol Receptors

1974 ◽  
Vol 46 (1) ◽  
pp. 143-147 ◽  
Author(s):  
P. B. Greenberg ◽  
Carmel J. Hillyard ◽  
Leonora S. Galante ◽  
Kay W. Colston ◽  
Imogen M. A. Evans ◽  
...  
Keyword(s):  
Biological Activity ◽  
Competitive Binding ◽  
Binding Assays ◽  
Differential Measurement ◽  
Chicken Kidney ◽  
Competitive Binding Assays ◽  
Selection Of ◽  
25 Hydroxycholecalciferol

1. The affinity of cholecalciferol, ergocalciferol and 25-hydroxyergocalciferol for 25-hydroxycholecalciferol receptors in human, rat and chicken kidney cytosol fractions is related to the biological activity of these analogues in the three species. 2. By selection of appropriate kidney cytosol receptors for competitive binding assays, the differential measurement of cholecalciferol and ergocalciferol derivatives is possible.

Redox Biotransformation and Delivery of Anthracycline Anticancer Antibiotics: How Interpretable Structure-activity Relationships of Lethality Using Electrophilicity and the London Formula for Dispersion Interaction Work

2018 ◽  
Vol 18 (6) ◽  
pp. 600-607
Author(s):  
Siu-Kwong Pang
Keyword(s):  
Biological Activity ◽  
Drug Metabolism ◽  
Quantum Chemical ◽  
Molecular Descriptors ◽  
Molecular Level ◽  
Dispersion Interaction ◽  
Chemical Methods ◽  
Structure Activity ◽  
Quantum Chemical Methods ◽  
Metabolism Study

Background: Quantum chemical methods and molecular mechanics approaches face a lot of challenges in drug metabolism study because of either insufficient accuracy, huge computational cost, or lack of clear molecular level pictures for building computational models. Low-cost QSAR methods can often be carried out, even though molecular level pictures are not well defined; however, they show difficulty in identifying the mechanisms of drug metabolism and delineating the effects of chemical structures on drug toxicity because a certain amount of molecular descriptors are difficult to be interpreted. Objective: In order to make a breakthrough of QSAR, mechanistically interpretable molecular descriptors were used to correlate with biological activity to establish structure-activity plots. The biological activity is the lethality of anthracycline anticancer antibiotics denoted as log LD50. The mechanistically interpretable molecular descriptors include electrophilicity and the mathematical function in the London formula for dispersion interaction. Method: The descriptors were calculated using quantum chemical methods. Results: The plots for electrophilicity, which is interpreted as redox reactivity of anthracyclines, can describe oxidative degradation for detoxification and reductive bioactivation for toxicity induction. The plots for the dispersion interaction function, which represents the attraction between anthracyclines and biomolecules, can describe efflux from and influx into the target cells of toxicity. The plots can also identify three structural scaffolds of anthracyclines that have different metabolic pathways, resulting in their different toxicity behavior. Conclusion: This structure-dependent toxicity behavior revealed in the plots can provide perspectives on drug design and drug metabolism study.

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