Oral Bioavailability of Naturally Occurring Anticancer Phytomolecules

Sharma, Shilpa; Gupta, Mansi; Sharma, Ashish; Agarwal, Subhash Mohan

doi:10.2174/1570180815666180109161014

Cited by 5 publications

(2 citation statements)

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“…∼400 000 NPs were retrieved from the COCONUT database, which was neutralized, desalted, and minimized using the LigPrep module of Schrodinger. Thereafter, the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of the NPs were calculated using QikProp to identify molecules with structural features within the acceptable range. , A total of 24 pharmaceutically relevant properties and descriptors that determine the suitability of the molecule as a potential lead molecule were calculated. The properties are molecular weight, number of rotatable bonds, number of hydrogen bond donors and acceptors, dipole moment, ionization potential, electron affinity, various components of solvent accessible surface area (SASA, FOSA, FISA, PISA, WPSA), van der Waals surface area, total solvent accessible volume, globularity, polarizability, multiple measures of the partition coefficient (QPlogPC16, QPlogPoct, QPlogPw, QPlogPo/w), aqueous solubility, human serum albumin binding, blood/brain barrier partition coefficient, and metabolic descriptors.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer

Das,

Mathur,

Agarwal

2024

ACS Omega

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Lung cancer is the most prevalent cause of cancer deaths worldwide. However, its treatment faces a significant hurdle due to the development of resistance. Phytomolecules are an important source of new chemical entities due to their rich chemical diversity. Therefore, a machine learning (ML) model was developed to computationally identify potential inhibitors using a curated data set of 649 phytomolecules with inhibitory activity against lung cancer cell lines. Four distinct ML approaches, including k-nearest neighbor, random forest, support vector machine, and extreme gradient boosting, were used in conjugation with MACCS and Morgan2 fingerprints to generate the models. It was observed that the random forest model developed by using the MACCS fingerprint shows the best performance. To further explore the chemical space and feature importance, k-means clustering, t-SNE analysis, and mean decrease in impurity had been calculated. Simultaneously, ∼400 000 natural products (NPs) retrieved from the COCONUT database were filtered for pharmacokinetic properties and taken for a multistep screening using docking against epidermal growth factor receptor (EGFR) mutant, a therapeutic drug target of lung cancer. Thereafter, the best-performing random forest model was used to predict the antilung cancer potential of the NPs having binding affinity better than the cocrystal ligand. This allowed the identification of 205 potential inhibitors, wherein the molecules with an indolocarbazole scaffold were enriched in top-scoring molecules. The top three indolocarbazole molecules with the lowest binding energy were further evaluated through 100 ns molecular dynamics (MD) simulations, which suggested that these molecules are strong binders. Also, structural similarity analysis against known drugs revealed that these NPs are similar to staurosporine, which demonstrates potent and selective activity against EGFR mutants. Thereby, the consensus analysis employing ML, molecular docking, and dynamics revealed that the molecules having an indolocarbazole scaffold are the most promising NPs that can act as potential inhibitors against lung cancer.

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Section: Methodsmentioning

confidence: 99%

Machine Learning, Molecular Docking, and Dynamics-Based Computational Identification of Potential Inhibitors against Lung Cancer

Das,

Mathur,

Agarwal

2024

ACS Omega

Self Cite

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“…As poor pharmacokinetics oen results in failure in later stages of drug discovery, screening libraries for their ADMET properties before taking them for virtual screening studies is considered necessary. 16,17,29 In the last few years, various in silico rules to evaluate the pharmacological properties and identify optimal molecules have been designed. 30 The routinely used approach to screen for drug-like molecules is by estimating Lipinski's "Rule of Five" (Ro5).…”

Section: Screening Of Natural Product Libraries For Drug-likenessmentioning

confidence: 99%