What question did this study set out to answer?

The study aims to identify novel inhibitors for the BRAF(V600E) mutation in melanoma using computational methods.

June 3, 2026

Structure‐Based Pharmacophore Modeling, Virtual Screening, and Molecular Dynamics Simulation for the Discovery of Novel BRAF(V600E) Inhibitors in Melanoma Cancer

Key Points

The study aims to identify novel inhibitors for the BRAF(V600E) mutation in melanoma using computational methods.
Developed a structure-based e-pharmacophore model derived from the BRAF(V600E) co-crystallized complex.
Screened 37,118 compounds from the PubChem database, resulting in 8995 top hits after filtering.
Conducted docking protocols, induced-fit docking, and ADMET predictions for selected compounds.
Identified three compounds (CID_18440762, CID_142655149, CID_18440769) meeting all computational criteria.
Promising docking scores ranged from −12.69 to −11.70, indicating strong potential affinity.
Successful molecular dynamics simulations confirmed high reactivity and stability of the identified compounds.

Abstract

ABSTRACT Malignant melanoma is one of the most aggressive forms of skin cancer, largely driven by mutations in the BRAF gene, with the V600E mutation accounting for a significant fraction of cases. In this study, we present an integrated in silico strategy to identify potential inhibitors of BRAF (V600E) using advanced computational methods. A structure‐based e‑pharmacophore model (ARRRR), derived from the co‑crystallized complex of BRAF (V600E) (PDB ID: 2FB8) with its ligand, was developed to capture key receptor–ligand interactions such as hydrogen bonding and aromatic rings. This model was used to screen a focused library of 37, 118 compounds from the PubChem database after filtering based on Lipinski's rule of five, resulting in 8995 top hits. Following the successive docking protocols (HTVS, SP, and XP), six promising compounds were improved XP docking scores which ranged from −12. 69 to −11. 70. These hits underwent extensive validation, including induced‑fit docking (IFD) to account for receptor flexibility, MM‑GBSA calculations to estimate binding free energies. In addition, comprehensive in silico ADMET predictions confirmed that these compounds possess favorable pharmacokinetic and toxicity profiles. Notably, compounds CID₁8440762, CID₁42655149, and CID₁8440769 met all computational criteria, as well as demonstrating high reactivity and stability in DFT and molecular dynamics simulations. Collectively, these results demonstrate that our integrated computational workflow effectively identified three potential BRAF (V600E) inhibitors with promising drug‐like attributes, paving the way for further experimental validation and lead optimization.

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