A structure-based virtual screening approach to identify novel anaplastic lymphoma kinase inhibitors.

contextAnaplastic lymphoma kinase (ALK) is a receptor tyrosine kinase that plays a crucial role in cellular signaling and is implicated in several cancers, including anaplastic large cell lymphoma (ALCL), non-small cell lung cancer (NSCLC), and neuroblastoma. ALK's catalytic activity is driven by its intracellular kinase domain, which activates key signaling pathways, making it an important target for small-molecule inhibitors. Although FDA (Food and Drug Administration) approved inhibitors such as crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib have improved clinical outcomes, their efficacy is often challenged by resistance mechanisms, including secondary kinase domain mutations and activation of bypass pathways. As a step towards overcoming the challenges associated with the existing inhibitors, we conducted virtual screening of the ZINC database to discover novel and alternative compounds with potential ALK inhibitory activity. The top hits obtained following a three-tiered virtual screening process namely high throughput virtual screening, standard precision, and extra precision, were further analyzed using molecular dynamics simulations to evaluate the stability of ALK-small molecule complexes under physiological conditions. Free energy calculations and binding affinity prediction were conducted to estimate the binding affinities along with per-residue energy decomposition of the most stable complexes. Principal component analysis further revealed dominant motions in apo and ligand-bound ALK, underscoring the role of key residues in conformational changes and complex stabilization. This integrative computational approach identified ZINC97743494, ZINC55325417, and ZINC83408527 as promising ALK inhibitors with potential to improve therapeutic strategies for ALK-positive cancers.

methodThe ZINC12 library was processed using LigPrep to generate optimized 3D structures, followed by multi-tiered virtual screening against the ALK kinase domain (PDB: 2XP2), prepared using the Schrödinger Suite. MMGBSA (Molecular Mechanics/Generalized Born Surface Area) binding free energy calculations and ADME-based pharmacokinetic analyses were performed for the top hits. MD simulations were conducted using Desmond to evaluate complex stability. Binding free energies and per-residue contributions were computed using MMGBSA. Boltz-2 machine learning platform to predict KD values and the top three hits were validated using PCA and free energy landscape.