Use of three-dimensional descriptors in molecular design for biologically active compounds

Mapari, Shweta; Camarda, Kyle V.

doi:10.1016/j.coche.2019.11.011

Cited by 9 publications

(5 citation statements)

References 42 publications

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“…66 Other descriptors include stats on their simplified molecular-input line-entry system (SMILES). [67][68][69] One of the first tasks when developing an ML algorithm for drug discovery is determining which combination of descriptors work most effectively with given datasets. These data are available for both ligands and the receptors they bind to, to train the ML algorithm(s) to find any underlying patterns between which molecules are likely to bind to each other, and then to test the efficacy of the trained algorithm using methods such as cross validation.…”

Section: Machine Learning and Drug Discoverymentioning

confidence: 99%

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

Boswell¹,

Verga²,

Mackle³

et al. 2023

IDR

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The urgent need for SARS-CoV-2 controls has led to a reassessment of approaches to identify and develop natural product inhibitors of zoonotic, highly virulent, and rapidly emerging viruses. There are yet no clinically approved broad-spectrum antivirals available for beta-coronaviruses. Discovery pipelines for pan-virus medications against a broad range of betacoronaviruses are therefore a priority. A variety of marine natural product (MNP) small molecules have shown inhibitory activity against viral species. Access to large data caches of small molecule structural information is vital to finding new pharmaceuticals. Increasingly, molecular docking simulations are being used to narrow the space of possibilities and generate drug leads. Combining in-silico methods, augmented by metaheuristic optimization and machine learning (ML) allows the generation of hits from within a virtual MNP library to narrow screens for novel targets against coronaviruses. In this review article, we explore current insights and techniques that can be leveraged to generate broad-spectrum antivirals against betacoronaviruses using in-silico optimization and ML. ML approaches are capable of simultaneously evaluating different features for predicting inhibitory activity. Many also provide a semi-quantitative measure of feature relevance and can guide in selecting a subset of features relevant for inhibition of SARS-CoV-2.

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Section: Machine Learning and Drug Discoverymentioning

confidence: 99%

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

Boswell¹,

Verga²,

Mackle³

et al. 2023

IDR

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“…3D matrix-based descriptors combine topological information given by the molecular graph with three-dimensional information given by geometric interatomic distances. These descriptors are widely used for lipophilicity, electrophilicity, and biological activity prediction [63][64][65][66][67][68][69].…”

Section: Interpretation Of the Best Descriptorsmentioning

confidence: 99%

QSPR analysis to predict some quantum chemical properties of 2‐phenylindol derivatives as anticancer drugs using molecular descriptor and genetic algorithm multiple linear regression

Bahrami,

Shafiei,

Marjani

et al. 2023

Int J of Quantum Chemistry

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Thermodynamic properties of molecules provide fundamental information for binding in fragment‐based drug discovery programs and for understanding complex interactions. Experimental measurements of some thermodynamic properties are not always feasible. Quantum mechanical methods and group contribution methods are the major toolboxes to obtain some theoretical quantities. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational methods have been widely used to optimize promising new ligands by estimating their binding affinity to proteins using the thermodynamic ligand binding parameters (Gibbs energy, enthalpy, entropy, and heat capacity). In this article, the relationship between molecular descriptors and quantum chemical properties, such as the Gibbs free energy (ΔG kJ mol−1) and enthalpy of formation (ΔHf kJ mol−1) of 2‐phenylindole derivatives as anticancer drugs is studied. These properties were calculated at the DFT‐B3LYP/6‐311G (d,p) level of quantum chemistry by Gaussian 09 software. Molecular descriptors were calculated by Dragon 5.4 software, and the stepwise multiple linear regression (MLR) and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSPR models. To evaluate the predictive ability of developed quantitative structure‐property relationship (QSPR) models, different internal and external validation methods were adopted. The predictive powers of the models were found to be satisfactory. The models revealed that 3D matrix‐based descriptors (H3D, SEig) are useful to predict the Gibbs free energy and enthalpy of formation of the investigated compounds respectively.

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“…An interesting way to make such a choice is to rely on the molecular descriptors that signify a molecule’s physicochemical attributes 35 —in entirety or partially. With advanced in silico tools, a wide range of 1D, 2D, 3D, and 4D molecular descriptors can be extracted from the analyses of molecular structures in the form of numerical readouts.…”

Section: Introductionmentioning

confidence: 99%

Molecular Descriptors as a Facile Tool toward Designing Surface-Functionalized Nanoparticles for Drug Delivery

Bhattacharjee

2022

Mol. Pharmaceutics

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Modulating the surface chemistry of nanoparticles, often by grafting hydrophilic polymer brushes (e.g., polyethylene glycol) to prepare nanoformulations that can resist opsonization in a hematic environment and negotiate with the mucus barrier, is a popular strategy toward developing biocompatible and effective nano-drug delivery systems. However, there is a need for tools that can screen multiple surface ligands and cluster them based on both structural similarity and physicochemical attributes. Molecular descriptors offer numerical readouts based on molecular properties and provide a fertile ground for developing quick screening platforms. Thus, a study was conducted with 14 monomers/repeating blocks of polymeric chains, namely, oxazoline, acrylamide, vinylpyrrolidone, glycerol, acryloyl morpholine, dimethyl acrylamide, hydroxypropyl methacrylamide, hydroxyethyl methacrylamide, sialic acid, carboxybetaine acrylamide, carboxybetaine methacrylate, sulfobetaine methacrylate, methacryloyloxyethyl phosphorylcholine, and vinyl-pyridinio propanesulfonate, capable of imparting hydrophilicity to a surface when assembled as polymeric brushes. Employing free, Web-based, and user-friendly platforms, such as SwissADME and ChemMine tools, a series of molecular descriptors and Tanimoto coefficient of molecular pairs were determined, followed by hierarchical clustering analyses. Molecular pairs of oxazoline/dimethyl acrylamide, hydroxypropyl methacrylamide/hydroxyethyl methacrylamide, acrylamide/glycerol, carboxybetaine acrylamide/vinyl-pyridinio propanesulfonate, and sulfobetaine methacrylate/methacryloyloxyethyl phosphorylcholine were clustered together. Similarly, the molecular pair of hydroxypropyl methacrylamide/hydroxyethyl methacrylamide demonstrated a high Tanimoto coefficient of >0.9, whereas the pairs oxazoline/vinylpyrrolidone, acrylamide/dimethyl acrylamide, acryloyl morpholine/dimethyl acrylamide, acryloyl morpholine/hydroxypropyl methacrylamide, acryloyl morpholine/hydroxyethyl methacrylamide, carboxybetaine methacrylate/sulfobetaine methacrylate, and glycerol/hydroxypropyl methacrylamide had a Tanimoto coefficient of >0.8. The analyzed data not only demonstrated the ability of such in silico tools as a facile technique in clustering molecules of interest based on their structure and physicochemical characteristics but also provided vital information on their behavior within biological systems, including the ability to engage an array of possible molecular targets when the monomers are self-assembled on nanoparticulate surfaces.

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Use of three-dimensional descriptors in molecular design for biologically active compounds

Cited by 9 publications

References 42 publications

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

In-Silico Approaches for the Screening and Discovery of Broad-Spectrum Marine Natural Product Antiviral Agents Against Coronaviruses

QSPR analysis to predict some quantum chemical properties of 2‐phenylindol derivatives as anticancer drugs using molecular descriptor and genetic algorithm multiple linear regression

Molecular Descriptors as a Facile Tool toward Designing Surface-Functionalized Nanoparticles for Drug Delivery

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