Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

Varela, Rocco; Walters, W. Patrick; Goldman, Brian B.; Jain, Ajay N.

doi:10.1021/jm301210j

Cited by 27 publications

(42 citation statements)

References 22 publications

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“…55 Two approaches that use a complementary biological target field to refine ligand-based 3D QSAR models are COMBINE 56 and AFMoC 57 programs; two other pioneering 3D QSAR methodologies, HASSLE 58 and MSTD 59 also deserve mentioning. Finally, though more "pseudo-receptor" –like rather than 3D QSAR approach (the latest incarnation of the pioneering COMPASS 3D QSAR methodology) QMOD, 60 shows noteworthy promise.…”

Section: History and Evolution Of Qsarmentioning

confidence: 99%

QSAR Modeling: Where Have You Been? Where Are You Going To?

et al. 2014

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Quantitative Structure-Activity Relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss: (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists towards collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR studies, as well as encourage the use of high quality, validated QSARs for regulatory decision making.

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Section: History and Evolution Of Qsarmentioning

confidence: 99%

QSAR Modeling: Where Have You Been? Where Are You Going To?

et al. 2014

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“…As in our previous study involving gyrase [17], here we applied descriptor-based QSAR using the random forest learning (RF) algorithm (see “Experimental section” for details).…”

Section: Resultsmentioning

confidence: 99%

“…For this work, default parameters were used, employing Surflex-QMOD version 1.5. There were two significant algorithmic variations investigated here, compared with that reported in the most recent study [17]. First, an initialization protocol was added that incorporates multi-structure docking and data integration that uses bound ligand poses to guide the generation of an alignment hypothesis.…”

Section: Methodsmentioning

confidence: 99%

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A structure-guided approach for protein pocket modeling and affinity prediction

Varela

Cleves

Spitzer³

et al. 2013

J Comput Aided Mol Des

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Binding affinity prediction is frequently addressed using computational models constructed solely with molecular structure and activity data. We present a hybrid structure-guided strategy that combines molecular similarity, docking, and multiple-instance learning such that information from protein structures can be used to inform models of structure–activity relationships. The Surflex-QMOD approach has been shown to produce accurate predictions of binding affinity by constructing an interpretable physical model of a binding site with no experimental binding site structural information. We introduce a method to integrate protein structure information into the model induction process in order to construct more robust physical models. The structure-guided models accurately predict binding affinities over a broad range of compounds while producing more accurate representations of the protein pockets and ligand binding modes. Structure-guidance for the QMOD method yielded significant performance improvements, both for affinity and pose prediction, especially in cases where predictions were made on ligands very different from those used for model induction.

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“…The GRIDs are used as independent variables for 3D-QSAR modeling, pharmacophore study, and drug design [43][44][45]. Some very promising novel rational drug discovery methodologies have been developed as combinations of 3D-QSAR modeling and complementary drug target fields [46][47][48][49].…”

Section: Qsar Studies In the Rational Design Of Antineoplastic Drugsmentioning

confidence: 99%

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

Vucicevic

Nikolić

Mitchell

2019

CMC

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Background: Computer-Aided Drug Design has strongly accelerated the development of novel antineoplastic agents by helping in the hit identification, optimization, and evaluation. Results: Computational approaches such as cheminformatic search, virtual screening, pharmacophore modeling, molecular docking and dynamics have been developed and applied to explain the activity of bioactive molecules, design novel agents, increase the success rate of drug research, and decrease the total costs of drug discovery. Similarity, searches and virtual screening are used to identify molecules with an increased probability to interact with drug targets of interest, while the other computational approaches are applied for the design and evaluation of molecules with enhanced activity and improved safety profile. Conclusion: In this review are described the main in silico techniques used in rational drug design of antineoplastic agents and presented optimal combinations of computational methods for design of more efficient antineoplastic drugs.

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Iterative Refinement of a Binding Pocket Model: Active Computational Steering of Lead Optimization

Cited by 27 publications

References 22 publications

QSAR Modeling: Where Have You Been? Where Are You Going To?

QSAR Modeling: Where Have You Been? Where Are You Going To?

A structure-guided approach for protein pocket modeling and affinity prediction

Rational Drug Design of Antineoplastic Agents Using 3D-QSAR, Cheminformatic, and Virtual Screening Approaches

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