#CoupledCluster

[govindhtech]

Coupled Cluster, DFT: Accuracy Cost Paradox In Drug Design

Explore the dilemma of quantum mechanical techniques like Coupled Cluster and DFT: they provide unique molecular insights but are too expensive for current drug research.

Overview: Drug Development Needs Innovation

Over the past 50 years, pharmaceutical drug development has become increasingly expensive and time-consuming, costing billions of dollars. Drug development must be improved to fulfil unmet medical requirements.

Computational approaches are already critical to pharmaceutical development. Quantum mechanical computations, molecular dynamics, and machine learning are examples. One bottleneck is designing and optimising chemicals to attach to a disease-related target protein. Computational methods predict binding affinity, a key drug candidate efficacy indicator.

However, simulating chemical systems with thousands of atoms in a cellular environment at restricted temperatures is computationally intensive. Current methods, such as molecular simulations with classical force fields, often fail to anticipate binding affinity. Quantum mechanical methods like Coupled Cluster (CC) and Density Functional Theory (DFT) describe molecular interactions better, but their high processing cost makes them unsuitable for drug creation. Because slight inaccuracies might produce considerable dosage prediction errors, great precision, ideally within 1.0 kcal/mol of experimental results, is desired.

Promise of Quantum Computers

Due to its quantum mechanical properties, quantum computers are being studied as a technique to model quantum systems. The promise of precise and effective quantum chemical computations is a major rationale for financing the study.

Quantum computers are expected to improve molecular system ground state energy determination. Traditional methods fail in systems with high correlations.

Strong electronic correlation is indicated by multi-reference wavefunctions, essential spin-symmetry breaking, cluster expansion failure sites, and near-degenerate natural orbitals. Multi-metal systems may require expensive multi-reference treatment.

Potential Quantum Computer Uses: Phase Estimation

Quantum Phase Estimation (QPE) is the typical electronic structure computation approach on fault-tolerant quantum computers. Creating the error-corrected quantum circuit, determining an initial quantum state, and fine-tuning the chemical system geometry usually begin on a classical computer.

Quantum computing then prepares this conventionally defined starting state. The ground state energy is then calculated using QPE. The efficiency of QPE depends on how closely the starting state matches the ground state. With adjustments to this approach, molecular forces and other important properties may be calculated.

Important Challenges Remain

There are many barriers to using quantum computers for large-scale drug discovery, notwithstanding their theoretical benefits.

Limits on Technology:

Noisy Intermediate Scale Quantum (NISQ) technology, with few qubits and noise, is now in use. To gain a quantum advantage for complex chemical calculations, Fault-Tolerant Quantum Computers (FTQCs) must exponentially minimise mistakes. FTQCs are a major engineering difficulty.

Even the iron-molybdenum complex (FeMoco), a difficult chemical, would require 200 logical qubits and millions of physical qubits after error correction. This is much larger than existing hardware can handle. Quantum error correction is a major run-time and qubit count overhead. To lower these overheads, quantum error correction codes and algorithms, hardware with lower error rates, and qubit connection must improve.

Problems with algorithms

Major algorithmic issues remain. Effectively preparing the initial quantum state is difficult. Despite heuristic approaches, more research is needed because the overlap of this starting state with the planned ground state directly influences QPE runtime. Another necessity for minimising processing cost is finding more compact Hamiltonian representations.

Drug design challenges include calculating thermodynamic characteristics like binding affinity, which may be the most significant impediment. Obtaining ensemble properties may involve billions of single-point calculations. Even if quantum computers could speed up individual computations, the sheer volume of calculations necessary makes it impossible to generate conclusions in a timely manner compared to well optimised experiments (current run-time estimates for sophisticated systems are days).

Adding an explicit solvent like water increases computing complexity and requirements. Drug design requires efficient computing of thermodynamic parameters, even when single-point simulations yield insights. Two methods are directly modelling electrons and classical nuclei or building thermal ensembles of geometries on a quantum computer.

Potential Effects and Use Cases

Quantum computing may have other uses in drug development, but its largest impact is predicted to be on lead optimisation computations. These comprise NMR and IR molecular spectra for structure identification and drug manufacturing reaction process refinement. Compared to speeding up core lead optimisation, these regions are expected to have less influence.

Quantum computers are best for precise computations on densely linked systems that classical methods cannot handle. Advanced approaches like DFT and Coupled Cluster would likely have the greatest impact on the pharmaceutical industry, even if used less accurately. Quantum computers may offer new ways to improve classical approaches, such as DFT functionals, even if they cannot speed linear-scaling classical methods like DFT or Hartree-Fock. Coupled Cluster approaches on quantum computers could triple optimisation speed.

Conclusion

The limits of quantum chemistry for drug development are either the high cost of DFT calculations for large biomolecular ensembles or the lack of accuracy for complex systems. Quantum computers may solve the accuracy problem for strongly correlated systems, but ensemble calculations to derive thermodynamic parameters are still expensive.

Quantum methods for electronic structure problems have reduced computational costs in recent decades. In addition to hardware breakthroughs and error correction codes (such as state preparation), more algorithmic advances are needed to go beyond single-point energy calculations and impact the pharmaceutical industry.

Despite the challenges, open research between academia and business may yield the fundamental advances needed to make quantum computing a critical tool for generating better drugs faster. Some of these issues are addressed. For computational drug design to be truly predictive and more broadly applicable, quantum computers must deliver the accuracy and robustness for strongly and weakly correlated systems at rates comparable to lower-precision conventional approaches.

One ambitious future goal that will require massive quantum computers is applying quantum machine learning to quantum calculations to predict pharmacokinetics.

[planetmatsci-blog]

PRL on cc methods, looks interesting: Converging High-Level Coupled-Cluster Energetics by Monte Carlo Sampling and Moment Expansions, J. E. Deustua, J. Shen, and P. Piecuch, Phys. Rev. Lett. 119, 223003 (2017).