Comparative Molecular Field Analysis Using Molecular Integral Equation Theory

Ansari, Samiul M.; Palmer, David Scott

doi:10.1021/acs.jcim.7b00600

Cited by 3 publications

(2 citation statements)

References 58 publications

(148 reference statements)

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“…Even though CARMa uses a statistical mechanics solvent model, the electrostatic and steric effects implemented in CoMFA cannot be directly captured. This issue has been recently addressed by solving 3D-RISM equations for a solvent comprising CoMFA probes in aqueous solution, this extension being referred to as CARMa(electrolyte) [72]. The analysis performed for six protein-ligand systems reveals a small but consistent increase in prediction accuracy compared to CoMFA.…”

Section: = + Eqmentioning

confidence: 99%

Lipophilicity in Drug Design: An Overview of Lipophilicity Descriptors in 3D-QSAR Studies

Ginex

Vázquez

Gilbert³

et al. 2019

Future Med. Chem.

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The pharmacophore concept is a fundamental cornerstone in drug discovery, playing a critical role in determining the success of in silico techniques, such as virtual screening and 3D-QSAR studies. The reliability of these approaches is influenced by the quality of the physicochemical descriptors used to characterize the chemical entities. In this context, a pivotal role is exerted by lipophilicity, which is a major contribution to host-guest interaction and ligand binding affinity. Several approaches have been undertaken to account for the descriptive and predictive capabilities of lipophilicity in 3D-QSAR modelling. Recent efforts encode the use of quantum mechanical-based descriptors derived from continuum solvation models, which open novel avenues for gaining insight into structure-activity relationships studies.

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Section: = + Eqmentioning

confidence: 99%

Lipophilicity in Drug Design: An Overview of Lipophilicity Descriptors in 3D-QSAR Studies

Ginex

Vázquez

Gilbert³

et al. 2019

Future Med. Chem.

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“…Additionally, Table 3 shows the highest, lowest, and average

{{Q}_{test}^{2}}

values achieved by the best 30 models created on each benchmarking dataset (see Table SI1), as well as the best

{{Q}_{test}^{2}}

values reported in the literature by several QSAR models [26a, c, d, f–j]. In this comparison, the generalization ability of the built models regarding several models reported in the literature is analyzed.…”

Section: Resultsmentioning

confidence: 99%

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

García-González

Marrero-Ponce

Brizuela

et al. 2023

Molecular Informatics

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Predicting the likely biological activity (or property) of compounds is a fundamental and challenging task in the drug discovery process. Current computational methodologies aim to improve their predictive accuracies by using deep learning (DL) approaches. However, non‐DL based approaches for small‐ and medium‐sized chemical datasets have demonstrated to be most suitable for. In this approach, an initial universe of molecular descriptors (MDs) is first calculated, then different feature selection algorithms are applied, and finally, one or several predictive models are built. Herein we demonstrate that this traditional approach may miss relevant information by assuming that the initial universe of MDs codifies all relevant aspects for the respective learning task. We argue that this limitation is mainly because of the constrained intervals of the parameters used in the algorithms that compute MDs, parameters that define the Descriptor Configuration Space (DCS). We propose to relax these constraints in an open CDS approach, so that a larger universe of MDs can be initially considered. We model the generation of MDs as a multicriteria optimization problem and tackle it with a variant of the standard genetic algorithm. As a novel component, the fitness function is computed by aggregating four criteria via the Choquet integral. Experimental results show that the proposed approach generates a meaningful DCS by improving state‐of‐the‐art approaches in most of the benchmarking chemical datasets accounted for.

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The HYBOT 3D MF Application and it Integration with CoMFA

Skvortsov

Mikurova

Raevsky

2018

BMCRM

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The program “HYBOT 3D MF” based on the potential “6-8” modified by using HYBOT hydrogen bonding factors has been developed. An integration with “QSAR and Advanced CoMFA” module of the SYBYL-X is performed by set of macros written in SPL. A full working version of the program is available at http://www.ibmc.msk.ru/HYBOT3D. For a number of sets of chemical compounds having pronounced differences in groups with the hydrogen bonding potential, the use of HYBOT 3D MF fields makes it possible to improve the predictive power of models created by the CoMFA technology.

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Comparative Molecular Field Analysis Using Molecular Integral Equation Theory

Cited by 3 publications

References 58 publications

Lipophilicity in Drug Design: An Overview of Lipophilicity Descriptors in 3D-QSAR Studies

Lipophilicity in Drug Design: An Overview of Lipophilicity Descriptors in 3D-QSAR Studies

Overproduce and select, or determine optimal molecular descriptor subset via configuration space optimization? Application to the prediction of ecotoxicological endpoints

The HYBOT 3D MF Application and it Integration with CoMFA

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