Classiq and AWS Power Quantum-Classical Chemistry Innovation in Singapore with Hatch

In biochemical process development and analysis, binding energy—the energy released when a small molecule docks into a protein's active site—determines how strongly a compound, such as a ligand, interacts with its protein target. Early-stage computational prediction of this quantity helps research teams prioritize candidates before committing to resource-intensive laboratory testing. Conventional methods in chemistry handle this well for small systems but face known accuracy limitations in strongly correlated molecular environments and incur growing computational costs as system size increases. Addressing both challenges is the motivation for this work.

This post describes a project by Classiq to build and validate a complete quantum-classical pipeline. The project was conducted as part of Dimension X, an open innovation challenge by Hatch, an innovation center in Singapore. The goal of the project was to demonstrate a quantum-classical pipeline for computational chemistry and binding energy estimation. The calculations were conducted on AWS cloud infrastructure using Amazon Elastic Compute Cloud (Amazon EC2) on a c6i.16xlarge instance with 64 vCPUs and 128 GB of memory. The pipeline combines high-performance parallelized Density Functional Theory (DFT) calculations with a variational quantum eigensolver (VQE), made accessible through the Classiq platform. The result is a workflow that uses AWS resources to handle the heavy classical computation and quantum computing to account for quantum correlations in the calculations, increasing accuracy beyond what DFT alone provides.

How ligand-protein interactions and binding energy estimation are formulated as a quantum chemistry problem How Classiq built and ran a hybrid classical–quantum workflow using AWS compute infrastructure. How the fragment-environment embedding approach keeps the quantum problem tractable as system size grows

A central mechanism in many biochemical processes is molecular recognition: a small molecule, or ligand, binds to a specific site on a larger biomolecule, usually a protein, and changes its activity. This interaction is fundamental across applications such as enzyme modulation, molecular screening, target engagement analysis, and biochemical process optimization. This same binding mechanism also underlies the therapeutic effects of many medicines; a small molecule binds to a target protein, modulating its activity to produce a desired biological effect.

The region of the protein where the ligand binds is called the binding pocket or active site. A key step in analyzing ligand–protein interactions is estimating the compound's binding affinity for relevant biological targets and candidate binding pockets. Stronger binding energy generally indicates tighter ligand–protein binding at the active site, which can affect how strongly the molecule modulates the target's function.

Evaluating the binding energy, therefore, requires three separate quantum chemistry calculations. Each calculation is performed on a system that, in a realistic target, can contain 100+ atoms and hundreds of interacting electrons, even when modeling only the local binding pocket.

Figure 1: Conceptual schematic representation of a ligand-protein binding pocket. The fragment region (active space) is treated quantum mechanically via VQE, while the surrounding protein environment is handled with DFT. This partition keeps the quantum problem tractable regardless of the full system size. The figure is illustrative of the computational partitioning used in the workflow.

Classical approaches face a fundamental trade-off between computational cost and physical accuracy. Molecular mechanics methods such as Molecular Mechanics Generalized Born Surface Area (MM-GBSA) are computationally efficient, but they rely on parameterized classical force fields and do not explicitly model the electronic structure. As a result, quantum effects are captured only indirectly through force-field parameterization and approximations. DFT offers a better balance: it scales polynomially with system size (number of electrons) and provides accurate results for many molecular systems. However, it still has known limitations for noncovalent interactions, charge transfer, dispersion, transition-metal centers, and strongly correlated electronic structures. Wavefunction methods such as Full Configuration Interaction (FCI) are formally exact within a chosen basis, but scale exponentially with system size, making them infeasible for realistic molecules.

Quantum computing offers a potential path through this bottleneck. In this proof-of-concept, we use a standard VQE to demonstrate the feasibility of the hybrid quantum-classical approach. While this initial implementation deliberately uses standard VQE, the architecture is modular by design and can support more advanced quantum algorithms as hardware and methods mature. In the noisy intermediate-scale quantum (NISQ) regime, this includes VQE-based methods for quantum chemistry (Rossmannek et al., 2023 [1]), which have shown promising resource-efficient performance for strongly correlated fragments. In the fault-tolerant regime, the same framework could be extended toward algorithms such as Quantum Phase Estimation (QPE), enabling higher-precision energy estimation. The present work should be viewed as a proof-of-concept step toward future quantum-enhanced evaluation of binding energies in chemically active sites, where high-accuracy classical treatments can become computationally expensive.

The challenge is that current quantum hardware has limited qubit counts, so the quantum calculation must target a small, carefully chosen subset of the molecule. The subset of atoms defines the active space where the most important electronic correlations occur. Even after the active space is chosen, researchers still face the circuit-engineering challenge of translating the quantum chemistry problem into an efficient circuit that fits hardware constraints. The Classiq platform addresses the challenge directly: researchers specify the quantum chemistry problem, using the Classiq SDK, and the platform synthesizes optimized quantum circuits without requiring gate-level programming. This abstraction is especially important as hardware matures. The same high-level model can be retargeted, optimized, and scaled to larger active spaces and deeper circuits, reducing a major practical barrier to experimenting with VQE today while preparing workflows for more capable quantum machines.

Classiq built a pipeline that is validated progressively against molecular systems of increasing complexity. The approach is grounded in projection-based Wavefunction-in-DFT embedding (Lee et al., 2019 [2]) and its extension to quantum computers via VQE (Rossmannek et al., 2023).

Figure 2: A hybrid quantum-classical pipeline for binding energy calculation. Classical DFT runs on HPC resources to characterize the full molecular system, embedding reduces the problem to a tractable active space, and VQE evaluates the ground-state energy of the resulting qubit Hamiltonian. The three system energies are then combined to yield the final binding energy.

The Hamiltonian embedding pipeline partitions the full molecular system into two coupled regions: a chemically active fragment and a surrounding environment. This allows the fragment, where the most important electronic correlations occur, to be treated with a quantum method such as VQE, while the larger environment is treated with DFT. The two energy contributions are then combined in a physically consistent way, improving accuracy without requiring a full quantum calculation of the entire protein system.