Quantum Reactivity-Guided Optimization of ATP-Competitive Ligands through Multiscale Simulation

Area of Science:

• Computational Chemistry
• Drug Discovery
• Molecular Modeling

Background:

• ATP-binding pockets present significant geometric and electronic constraints for small-molecule inhibitors.
• Optimizing ATP-competitive inhibitors requires balancing electronic reactivity, metabolic stability, and binding affinity.
• The receptor tyrosine kinase-like orphan receptor 1 (ROR1) pseudokinase domain is an underexplored cancer target with a druggable ATP-binding pocket.

Purpose of the Study:

• To develop and apply a multiscale, quantum-informed computational strategy for designing novel ATP-competitive inhibitors.
• To identify modifications in the ROR1 pseudokinase domain's ATP-binding pocket that maintain key interactions while improving drug properties.
• To evaluate the binding affinity, stability, and developability of designed analogs.

Main Methods:

• Density functional theory (DFT) analyses, including charge partitioning (Mulliken, Löwdin, Hirshfeld) and Fukui function mapping, were used to guide analog design.
• All-atom molecular dynamics (MD) simulations (1 μs for apo and bound ROR1, 250 ns for analogs) established energetic and dynamic baselines.
• Relative binding free-energy calculations (MM-PBSA) and in silico ADME profiling were employed to assess analog performance.

Main Results:

• Eight Fukui-guided analogs (DM1-DM8) were designed, with modifications at C10, C11, C32, and C35 preserving essential anchoring motifs.
• Designed analogs exhibited binding free energies comparable to Ponatinib (ΔG_bind ≈ -37 to -45 kcal mol⁻¹).
• Interaction fingerprint analysis confirmed conserved interactions with key residues (Glu71, Ile103, Asp181, Phe100, Tyr102).
• In silico ADME profiling identified DM3-DM5 as the most promising candidates due to their balanced properties.

Conclusions:

• Quantum-mechanical reactivity descriptors effectively identify feasible modification sites within ATP-binding pockets.
• This computational strategy enables scaffold modifications that are tolerated without disrupting critical pharmacophore interactions.
• The findings provide a complementary approach to classical scoring methods for rational drug design targeting kinase domains.