Deep Learning‐Guided Molecular Docking for Rapid Identification of Novel HER2 Inhibitors in Breast Cancer Therapy

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Abstract This study targeted human epidermal growth factor 2 (HER2) using in silico approaches. The inhibitor library, including 4945 compounds, was screened using in silico approaches. A known inhibitor, SYR127063, was used as a control. Three diverse compounds, 10202642 , 25127414 , and 46911863 , had strong binding affinity with HER2. Further, molecular dynamics simulation showed that 46911863 and 10202642 had stable root mean square deviation (RMSD) with no significant conformational changes during the 100 ns simulation. 10202642 had comparatively the highest number of hydrogen bonds during the 100 ns simulation. 46911863 and 10202642 showed hydrogen bond interaction with critical residues Met801 and Thr862, respectively. Further, the molecular mechanics generalized born surface area (MM/GBSA) technique showed that 10202642 had the highest negative binding free energy of −41.45 kcal/mol and 46911863 had the binding free energy of −30.07 kcal/mol, which was comparable to the control (−39.24 kcal/mol). The 500 ns triplicate MD simulations indicate that 46911863 and 10202642 provide the greatest structural stability to the protein, with consistently low RMSD fluctuations. Steered molecular dynamics simulations reveal that 46911863 and 10202642 demonstrate the highest binding stability, requiring the most force for displacement. Overall, this study identified two possible candidates ( 46911863 and 10202642) as potential inhibitors of HER2 that could be further used for experimental analysis and verification.

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