Quantitative structure–activity relationship: promising advances in drug discovery platforms

Wang, Tao; Wu, Mianbin; Lin, Johnson; Yang, Lirong

doi:10.1517/17460441.2015.1083006

Cited by 104 publications

(50 citation statements)

References 135 publications

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“…In the process of drug discovery, machine intelligence methods have mostly been used in the above-mentioned computational methods over the past few decades [73]. With the booming era of "big" data, machine learning methods have developed into deep learning approaches, which are a more efficient way for drug designers to deal with important biological properties from large amount of compound databases.…”

Section: Machine Learning Methods Accelerate Drug Developmentmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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Drug development is one of the most significant processes in the pharmaceutical industry. Various computational methods have dramatically reduced the time and cost of drug discovery. In this review, we firstly discussed roles of multiscale biomolecular simulations in identifying drug binding sites on the target macromolecule and elucidating drug action mechanisms. Then, virtual screening methods (e.g., molecular docking, pharmacophore modeling, and QSAR) as well as structure-and ligand-based classical/de novo drug design were introduced and discussed. Last, we explored the development of machine learning methods and their applications in aforementioned computational methods to speed up the drug discovery process. Also, several application examples of combining various methods was discussed. A combination of different methods to jointly solve the tough problem at different scales and dimensions will be an inevitable trend in drug screening and design.Molecules 2020, 25, 1375 2 of 17 Biomolecular Simulations in Drug Screening and DesignThe Nobel Prize in Chemistry 2013 was awarded jointly to Martin Karplus, Michael Levitt, and Arieh Warshel for their pioneering contributions in the development of multiscale models for complex biochemical systems, recognizing the important role that theory and computational methods play as a direct and necessary complement to experiments [12]. Further, applications of biomolecular simulations in identifying drug binding sites and elucidating the molecular mechanisms of diseases have been subject to rapid developments [13][14][15].Depending on the biological problems to be studied, multiscale models will be used in biomolecular simulations. The combination of quantum mechanics (QM) and molecular mechanics (MM) (QM/MM) can be used to study the electronic properties [16], simulate chemical reactions (e.g., the enzyme catalysis mechanism [17]), and calculate spectra [18] in a single simulation, which can be used to elucidate the action mechanism of certain drugs. Another widely used biomolecular method is molecular dynamics (MD) simulation [19][20][21][22][23][24], which applies empirical molecular mechanics (MM) force fields and is based on classical Newtonian mechanics. According to different accuracy requirements, all-atom (AA), united-atom (UA), and coarse-grained (CG) MD simulations as well as explicit/implicit solvent models, which allow simulations of temporal and spatial scales, can be used to facilitate the drug discovery [25,26]. Generally, MD simulations have been used for the identification of potential drug binding sites on target proteins, the calculation of binding free energy between target proteins and drug molecules, the action mechanism of drug molecules, etc. [27,28]. However, it is worth mentioning that MD simulations are also limited to time and length scales. Currently, AAMD simulations can explore time-dependent phenomena of large systems such as viral capsids in atomic detail as long as microseconds or even milliseconds [21,29,30]. Therefore, different types of ...

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Section: Machine Learning Methods Accelerate Drug Developmentmentioning

confidence: 99%

A Review on Applications of Computational Methods in Drug Screening and Design

2020

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“…With fast development on computational chemistry and bioinformatics, it could be possible to use models to predict the optimal working conditions for enzymatic reactions with newly designed enzymes. This is plausible because currently a lot of information on primary, secondary, tertiary, and quaternary structures is readily available, and many studies have been done on account of structure-function relationship of proteins [1,2]. Actually the optimal working conditions are adjusted in order to be suitable for enzymatic function, more exactly for enzyme structure, therefore we could assume that there is a certain relationship between enzyme structure and working conditions in enzymatic reaction.…”

Section: Introductionmentioning

confidence: 99%

Predicting pH Optimum for Activity of Beta-Glucosidases

Yan¹,

Wu²

2019

JBiSE

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The working conditions for enzymatic reaction are elegant, but not many optimal conditions are documented in literatures. For newly mutated and newly found enzymes, the optimal working conditions can only be extrapolated from our previous experience. Therefore a question raised here is whether we can use the knowledge on enzyme structure to predict the optimal working conditions. Although working conditions for enzymes can be easily measured in experiments, the predictions of working conditions for enzymes are still important because they can minimize the experimental cost and time. In this study, we develop a 20-1 feedforward backpropagation neural network with information on amino acid sequence to predict the pH optimum for the activity of beta-glucosidase, because this enzyme has drawn much attention for its role in bio-fuel industries. Among 25 features of amino acids being screened, the results show that 11 features can be used as predictors in this model and the amino-acid distribution probability is the best in predicting the pH optimum for the activity of beta-glucosidases. Our study paves the way for predicting the optimal working conditions of enzymes based on the amino-acid features.

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“…This approach, resulting in a structure–activity relation (SAR), can reveal which functional groups have beneficial interactions with the target. The SAR approach can be extended to include 3D information about the drug-like molecules in the series in a quantitative manner (3D-QSAR) [2,3]. Based on the assumption that the molecules bind in the same pose, one can deduce which functional groups at what locations interact best with what parts of the target binding site, and this approach can be particularly powerful given a known binding pose in the context of the target [4].…”

Section: Experimental Fgmmentioning

confidence: 99%

Computational functional group mapping for drug discovery

Guvench

2016

Drug Discovery Today

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Computational functional group mapping (cFGM) is emerging as a high-impact complement to existing widely used experimental and computational structure-based drug discovery methods. cFGM provides comprehensive atomic-resolution 3D maps of the affinity of functional groups that can constitute drug-like molecules for a given target, typically a protein. These 3D maps can be intuitively and interactively visualized by medicinal chemists to rapidly design synthetically accessible ligands. Given that the maps can inform selection of functional groups for affinity, specificity, and pharmacokinetic properties, they are of utility for both the optimization of existing drug candidates and creating novel ones. Here, I review recent advances in cFGM with emphasis on the unique information content in the approach that offers the potential of broadly facilitating structure-based ligand design.

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Quantitative structure–activity relationship: promising advances in drug discovery platforms

Cited by 104 publications

References 135 publications

A Review on Applications of Computational Methods in Drug Screening and Design

A Review on Applications of Computational Methods in Drug Screening and Design

Predicting pH Optimum for Activity of Beta-Glucosidases

Computational functional group mapping for drug discovery

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