Structure-based molecular modeling in SAR analysis and lead optimization

Temml, Veronika; Kutil, Zsófia

doi:10.1016/j.csbj.2021.02.018

Cited by 35 publications

(23 citation statements)

References 163 publications

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“…Previously, several waves of virtual screening algorithms or scoring functions have already been developed; however, they mostly focus on protein orthosteric sites. 233,[236][237][238][239][240] Recent biophysical analyses uncovered stark contrast between orthosteric and allosteric binding sites. For example, interactions between allosteric modulators and their sites are mainly mediated by hydrophobic interactions, which are conspicuously distinct from hydrophilic-driven orthosteric binding.…”

Section: Hybrid Modelsmentioning

confidence: 99%

“…Within this process, considerable amounts of time and resources can be saved through the use of in silico analysis, such as high‐throughput virtual screening and ligand‐target interaction scoring, 230–235 which can substantially help to prioritize and identify potential candidate compounds, and shed light on further lead optimization and structure‐based drug design. Previously, several waves of virtual screening algorithms or scoring functions have already been developed; however, they mostly focus on protein orthosteric sites 233,236–240 . Recent biophysical analyses uncovered stark contrast between orthosteric and allosteric binding sites.…”

Section: Characterization Of Allosteric Modulatorsmentioning

confidence: 99%

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Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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Allostery is a universal, biological phenomenon in which orthosteric sites are finetuned by topologically distal allosteric sites triggered by perturbations, such as ligand binding, residue mutations, or post-translational modifications. Allosteric regulation is implicated in a variety of physiological and pathological conditions and is thus emerging as a novel avenue for drug discovery. Allosteric drugs have traditionally been discovered by serendipity through large-scale experimental screening. Recently, we have witnessed significant progress in biophysics, particularly in structural bioinformatics, which has facilitated the in-depth characterization of allosteric effects and the accurate detection of allosteric residues and exosites. These advances improve our understanding of allosterism and promote allosteric drug discovery, thereby revolutionizing the shift from the traditional serendipitous route used to discover allosteric drugs to the updated path centered on rational structure-based design. In this review, recent advances in computational methods applied to allosteric drug discovery are summarized. We comprehensively review these achievements along various levels of allosteric events, from the construction of allosteric databases to the identification and analysis of allosteric residues, signals, sites, and modulators. We expect to increase the awareness of the discovery of allosteric drugs using structure-based computational methods.

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Section: Hybrid Modelsmentioning

confidence: 99%

Section: Characterization Of Allosteric Modulatorsmentioning

confidence: 99%

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Chai

Wang

et al. 2021

WIREs Comput Mol Sci

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“…Many examples in the literature present pharmacophore models obtained by QSAR and common feature pharmacophore approaches. In addition to articles and reviews published in the past that report on successful applications in pharmacophore modeling and virtual screening [ 70 , 71 , 72 , 73 , 74 ], we highlight some specific example of studies.…”

Section: Examples and Case Studiesmentioning

confidence: 99%

Drug Design by Pharmacophore and Virtual Screening Approach

et al. 2022

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Computer-aided drug discovery techniques reduce the time and the costs needed to develop novel drugs. Their relevance becomes more and more evident with the needs due to health emergencies as well as to the diffusion of personalized medicine. Pharmacophore approaches represent one of the most interesting tools developed, by defining the molecular functional features needed for the binding of a molecule to a given receptor, and then directing the virtual screening of large collections of compounds for the selection of optimal candidates. Computational tools to create the pharmacophore model and to perform virtual screening are available and generated successful studies. This article describes the procedure of pharmacophore modelling followed by virtual screening, the most used software, possible limitations of the approach, and some applications reported in the literature.

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“…Prediction of lipophilicity is an important physicochemical property in the drug discovery process because lipophilicity stands in modulating several key pharmacokinetic properties. Lipophilicity of lead molecules explicitly affects the membrane permeability of lead molecules and impacts ADME behaviours [ 172 – 177 ]. Traditionally, octanol–water partition coefficient/pH-dependent distribution coefficient (log D ) and alternative method liposome/water partitioning and immobilized artificial membrane (IAM) methods are used as a standard gold method for predicting quantitative characterization lipophilicity.…”

Section: Artificial Intelligence and Machine Learning For Adme/t Propertiesmentioning

confidence: 99%

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

Selvaraj

Chandra

2021

Mol Divers

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The global spread of COVID-19 has raised the importance of pharmaceutical drug development as intractable and hot research. Developing new drug molecules to overcome any disease is a costly and lengthy process, but the process continues uninterrupted. The critical point to consider the drug design is to use the available data resources and to find new and novel leads. Once the drug target is identified, several interdisciplinary areas work together with artificial intelligence (AI) and machine learning (ML) methods to get enriched drugs. These AI and ML methods are applied in every step of the computer-aided drug design, and integrating these AI and ML methods results in a high success rate of hit compounds. In addition, this AI and ML integration with high-dimension data and its powerful capacity have taken a step forward. Clinical trials output prediction through the AI/ML integrated models could further decrease the clinical trials cost by also improving the success rate. Through this review, we discuss the backend of AI and ML methods in supporting the computer-aided drug design, along with its challenge and opportunity for the pharmaceutical industry. Graphic abstract From the available information or data, the AI and ML based prediction for the high throughput virtual screening. After this integration of AI and ML, the success rate of hit identification has gained a momentum with huge success by providing novel drugs.

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Structure-based molecular modeling in SAR analysis and lead optimization

Abstract: Graphical abstract

Cited by 35 publications

References 163 publications

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Along the allostery stream: Recent advances in computational methods for allosteric drug discovery

Drug Design by Pharmacophore and Virtual Screening Approach

Artificial intelligence and machine learning approaches for drug design: challenges and opportunities for the pharmaceutical industries

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