Quantum Inner Product Scoring with Grid-Based Maps for Structure-Based Virtual Screening.

Area of Science:

• Computational chemistry
• Structural biology
• Drug discovery

Background:

• Structure-based virtual screening (SBVS) is computationally intensive due to large configurational spaces.
• Accurate calculation of protein-ligand interaction energies is crucial for SBVS efficacy.

Purpose of the Study:

• To develop a novel computational framework for protein-ligand interaction energy calculation.
• To enhance the efficiency and scalability of structure-based virtual screening.

Main Methods:

• Reformulated interaction energy calculation as a linear-algebraic problem on shared Cartesian grids.
• Represented electrostatic and van der Waals energies as inner products.
• Utilized unitary operations for ligand transformations and the Hadamard test for inner-product estimation.

Main Results:

• The proposed method preserves energetic ordering in the low-energy regime relevant for SBVS.
• Demonstrated robustness under finite-sampling conditions across multiple receptor-ligand systems.
• Validated the formulation through comparisons with classical energy evaluation methods.

Conclusions:

• The new representation-level formulation enables efficient map-based virtual screening.
• This approach is compatible with both classical and quantum computational paradigms.
• Offers a pathway to overcome limitations in current SBVS methods.