Heuristic molecular lipophilicity potential (HMLP): A 2D‐QSAR study to LADH of molecular family pyrazole and derivatives

Du, Qiang; Mezey, Paul G.; Chou, Kuo‐Chen

doi:10.1002/jcc.20174

Cited by 84 publications

(52 citation statements)

References 57 publications

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“…The molecular lipophilic index LM, molecular hydrophilic index HM, lipophilic indices and hydrophilic indices of the substitutes (fragments) and atomic lipophilicity indices are constructed and used in QSAR study. HMLP indices are correlated with bioactivities of 18 pyrazole derivatives according to the 2D QSAR procedure [48].…”

Section: Qsar Studies For Dynein Pyrazole and Their Derivativesmentioning

confidence: 99%

QSAR and its Role in Target-Ligand Interaction

Singh¹,

Singh²

2013

TOBIOIJ

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Each molecule has its own specialty, structure and function and when these molecules are combined together they form a compound. Structure and function of a molecule are related to each other and QSARs (Quantitative StructureActivity relationships) are based on the criteria that the structure of a molecule must contain the features responsible for its physical, chemical, and biological properties, and on the ability to represent the chemical by one, or more, numerical descriptor(s). By QSAR models, the biological activity of a new or untested chemical can be inferred from the molecular structure of similar compounds whose activities have already been assessed. QSARs attempt to relate physical and chemical properties of molecules to their biological activities. For this there are so many descriptors (for example, molecular weight, number of rotatable bonds, Log P) and simple statistical methods such as Multiple Linear Regression (MLR) are used to predict a model. These models describe the activity of the data set and can predict activities for further sets of (untested) compounds. These types of descriptors are simple to calculate and allow for a relatively fast analysis. 3D-QSAR uses probe-based sampling within a molecular lattice to determine three-dimensional properties of molecules (particularly steric and electrostatic values) and can then correlate these 3D descriptors with biological activity. Physicochemical descriptors, include hydrophobicity, topology, electronic properties, and steric effects etc. These descriptors can be calculated empirically, statistically or through more recent computational methods. QSARs are currently being applied in many disciplines, with many pertaining to drug design and environmental risk assessment.

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Section: Qsar Studies For Dynein Pyrazole and Their Derivativesmentioning

confidence: 99%

QSAR and its Role in Target-Ligand Interaction

Singh¹,

Singh²

2013

TOBIOIJ

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“…The quantitative structure-activity relationship (QSAR) was the oldest and the most frequently used tool for this purpose. [1][2][3][4] The QSAR was firstly noted in the 1930s by Hammett and refined by Hansch and Fujita in the mid-1960s. 1 Two-dimensional quantitative structure-activity relationship (2D-QSAR) was the first practical QSAR method originally proposed by Hansch and Fujita.…”

Section: Introductionmentioning

confidence: 99%

“…1 Two-dimensional quantitative structure-activity relationship (2D-QSAR) was the first practical QSAR method originally proposed by Hansch and Fujita. 1 Typically, the 2D-QSAR approach is represented by a linear free energy equation, [1][2][3][4] logðAÞ ¼ a h f hydr ðx h Þ þ a e f elec ðx e Þ þ a st f st ðx s Þ þ Á Á Á ¼ X M l¼1 a l f l ðx l Þ (1) where A is related to either the receptor binding affinity or a specific biological activity. Each of the terms in eq.…”

Section: Introductionmentioning

confidence: 99%

Fragment‐based quantitative structure–activity relationship (FB‐QSAR) for fragment‐based drug design

Huang

Wei

et al. 2008

J Comput Chem

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In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

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“…QSAR methodology has been applied successfully in different situations, with small molecules and proteins, which validate the goodness of the method used in this paper. Partial Least Square (PLS) was used to establish a relationship between Heuristic Molecular Lipophilicity Potential indices and the bioactivity of pyrazole derivatives (Du et al, 2005). New QSAR methods were proposed, such as Multiple Field 3D-QSAR (a combination of classical 2D-QSAR and 3D-QSAR) and Fragment-Based QSAR applied to neuraminidase inhibitors (Du et al, 2008a;Du et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

Vilar

González-Dı́az

Santana

et al. 2009

Journal of Theoretical Biology

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The combination of the network theory and the calculation of Topological Indices (TIs) allow establishing relationships between the molecular structure of large molecules like the genes and proteins and their properties at a biological level. This type of models can be considered Quantitative Structure-Activity Relationships (QSAR) for biopolymers. In the present work a QSAR model is reported for proteins, related to Human Colorectal Cancer (HCC) and codified by different genes that have been identified experimentally by Sjöblom et al.(Science, 314 (2006)

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Heuristic molecular lipophilicity potential (HMLP): A 2D‐QSAR study to LADH of molecular family pyrazole and derivatives

Cited by 84 publications

References 57 publications

QSAR and its Role in Target-Ligand Interaction

QSAR and its Role in Target-Ligand Interaction

Fragment‐based quantitative structure–activity relationship (FB‐QSAR) for fragment‐based drug design

A network-QSAR model for prediction of genetic-component biomarkers in human colorectal cancer

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