Development of robust QSAR model using rapid overlay of crystal structures (ROCS) based alignment: a test case of Tubulin inhibitors

Uddin, Reaz; Naz, Afshan; Akhtar, Nahid; Ul-Haq, Zaheer

doi:10.1007/s00044-012-0327-0

Cited by 9 publications

(4 citation statements)

References 34 publications

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“…The molecules of the library were aligned using the ligand alignment tool available in maestro. The molecules were aligned flexibly by sketching the structure of the basic scaffold i. e. TZD as a reference by employing user defined mode [36,37] …”

Section: Materials and Methodologymentioning

confidence: 99%

“…The molecules were aligned flexibly by sketching the structure of the basic scaffold i. e. TZD as a reference by employing user defined mode. [36,37] Generation of Gaussian Based 3D QSAR Model 3D QSAR is a robust computational technique which relates the biological activity to the spatial fingerprints of the molecule, employed for the rational drug discovery. [38] The Gaussian based 3D QSAR model was generated using the 3D field based QSAR tool available in the Phase module of the maestro by employing library of aligned TZD based molecules.…”

Section: Alignment Of Moleculesmentioning

confidence: 99%

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Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

Khator,

Singh,

Asati

et al. 2024

ChemistrySelect

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Accumulation of excessive fat in the body is an indication of obesity, which is becoming an alarming health concern around the world. Pancreatic lipase has been found to have a promising role in the digestion of dietary fats thereby inhibition of pancreatic lipase can be a therapeutic strategy for the management of obesity. Literature showed the anti‐pancreatic lipase potential of thiazolidinediones thereby the current study is designed to identify substituted thiazolidinedione‐based molecules as pancreatic lipase inhibitors. A 3D QSAR model with R2 value of 0.7283 and Stability of 0.843 was generated to determine the nature of substitution necessary for the inhibitory activity. Based on the generated 3D QSAR, the activity of 352 novel‐designed molecules was predicted and further screened through pharmacokinetic parameters. These compounds were docked into the binding site of the pancreatic lipase which led to the identification of 14 hit molecules with dock score between −9.523 to −7.604 kcal/mol. The molecular simulation studies and free energy calculation of the hit compounds i. e. 87 and 80 revealed good stability and binding in the active site of pancreatic lipase. The current study provides 14 hit molecules, which can be synthesized and further evaluated to explore their anti‐pancreatic lipase inhibitory activity.

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Section: Materials and Methodologymentioning

confidence: 99%

Section: Alignment Of Moleculesmentioning

confidence: 99%

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

Khator,

Singh,

Asati

et al. 2024

ChemistrySelect

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“…Since there are no strict rules to unambiguously align a set of molecules, the final models depend on the user's choice of the alignment method. In the literature, different alignment procedures have been reported including but not limited to dockingbased [21][22][23][24][25], maximum substructure-based [21,26,27], pharmacophore-based [26,28,29], co-crystal-based [24,25] and shape-based [30,31]. Indeed, CoMFA-like 3D-QSAR models developed for the same dataset using different alignment techniques resulted in QSAR models with different predictiveness [25].…”

Section: Introductionmentioning

confidence: 99%

A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations

Senderowitz

Bahia

Kaspi

et al. 2023

Molecular Informatics

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QSAR models are widely and successfully used in many research areas. The success of such models highly depends on molecular descriptors typically classified as 1D, 2D, 3D, or 4D. While 3D information is likely important, e. g., for modeling ligand-protein binding, previous comparisons between the performances of 2D and 3D descriptors were inconclusive. Yet in such comparisons the modeled ligands were not necessarily represented by their bioactive conformations. With this in mind, we mined the PDB for sets of protein-ligand complexes sharing the same protein for which uniform activity data were reported. The results, totaling 461 structures spread across six series were compiled into a carefully curated, first of its kind dataset in which each ligand is represented by its bioactive conformation. Next, each set was characterized by 2D, 3D and 2D + 3D descriptors and modeled using three machine learning algorithms, namely, k-Nearest Neighbors, Random Forest and Lasso Regression. Models' performances were evaluated on external test sets derived from the parent datasets either randomly or in a rational manner. We found that many more significant models were obtained when combining 2D and 3D descriptors. We attribute these improvements to the ability of 2D and 3D descriptors to code for different, yet complementary molecular properties.

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“…Moreover, the nonbonding interaction (between drugs and receptor) and three‐dimensional futures could be easily included in 3D‐QSAR techniques 18 . Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) are two common variants of 3D‐QSAR methods for predicting the activity of a designed drug from its 3D structure 18–20 . These two methods have been used in many of recent studies related to the study of biochemical features of pharmaceutical compounds 21–23 …”

Section: Introductionmentioning

confidence: 99%

Developing a variation of 3D‐QSAR/MD method in drug design

Haghshenas

Kaviani

Firouzeh³

et al. 2021

J Comput Chem

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In continuation of the previous reports on a combination of 3D‐quantitative structure–activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D‐QSAR/MD method has been employed for drug‐design as an alternative or supplementary for the typical experimental methods. The presented method is more cost‐effective and less time‐consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)‐containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D‐QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD‐extracted ligand conformations and their corresponding MM/GBSA BRD4‐binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D‐QSAR‐MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

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Development of robust QSAR model using rapid overlay of crystal structures (ROCS) based alignment: a test case of Tubulin inhibitors

Cited by 9 publications

References 34 publications

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

Identification of Novel Thiazolidinedione Based Pancreatic Lipase Inhibitors for the Management of Obesity Using In Silico Approach

A comparison between 2D and 3D descriptors in QSAR modeling based on bio‐active conformations

Developing a variation of 3D‐QSAR/MD method in drug design

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