The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors

Abstract

Targeting the signaling pathway of the Vascular endothelial growth factor receptor-2 is a promising approach that has drawn attention in the quest to develop novel anti-cancer drugs and cardiovascular disease treatments. We construct a screening pipeline using machine learning classification integrated with similarity checks of approved drugs to find new inhibitors. The statistical metrics reveal that the random forest approach has slightly better performance. By further similarity screening against several approved drugs, two candidates are selected. Analysis of absorption, distribution, metabolism, excretion, and toxicity, along with molecular docking and dynamics are performed for the two candidates with regorafenib as a reference. The binding energies of molecule1, molecule2, and regorafenib are − 89.1, − 95.3, and − 87.4 (kJ/mol), respectively which suggest candidate compounds have strong binding to the target. Meanwhile, the median lethal dose and maximum tolerated dose for regorafenib, molecule1, and molecule2 are predicted to be 800, 1600, and 393 mg/kg, and 0.257, 0.527, and 0.428 log mg/kg/day, respectively. Also, the inhibitory activity of these compounds is predicted to be 7.23 and 7.31, which is comparable with the activity of pazopanib and sorafenib drugs. In light of these findings, the two compounds could be further investigated as potential candidates for anti-angiogenesis therapy.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

“…179 The fragment-to-lead optimization process can be aided by in silico strategies that include molecular docking and dynamics simulations to assess fragment binding modes, machine learning algorithms to predict binding affinity and selectivity, and virtual screening of fragment libraries to identify potential hits. 180 3.1.2.2. Computational Approaches.…”

“…Methods like fragment growing, fragment linking, and fragment merging can be used to improve a fragment hit into a more potent compound . The fragment-to-lead optimization process can be aided by in silico strategies that include molecular docking and dynamics simulations to assess fragment binding modes, machine learning algorithms to predict binding affinity and selectivity, and virtual screening of fragment libraries to identify potential hits …”

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex

“…It employs molecular docking, molecular dynamics simulations, and machine learning algorithms. 15 These methods enable the prediction of compound−target interactions, assessment of physicochemical and pharmacological properties, and identification of compounds with potential therapeutic effects. 16 De novo drug design (molecular generation) explores the chemical space to generate novel molecules with desirable properties.…”

“…Virtual screening involves the use of computational methods to virtually screen large databases of compounds against specific target structures. It employs molecular docking, molecular dynamics simulations, and machine learning algorithms . These methods enable the prediction of compound–target interactions, assessment of physicochemical and pharmacological properties, and identification of compounds with potential therapeutic effects …”

Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition

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