Quantum computing in drug discovery - Frontiers

REVIEW articleFront. Drug Discov., 07 September 2026 Sec. Technologies and Strategies to Enable Drug DiscoveryVolume 6 - 2026 | https://doi.org/10.3389/fddsv.2026.1815176Quantum computing in drug discoveryHGHarshraj Gadbail 1*RRRajendra Rewatkar 2NJNishant Jumde 1MMMrinal Manker 1PNPratik Nagre 1SZSujal Zade 11. Department of Artificial Intelligence and Machine Learning, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India2. Department of Computer Science and Medical Engineering, Faculty of Engineering and Technology, Datta Meghe Institute of Higher Education and Research, Wardha, Maharashtra, India Article metrics View detailsAbstractEarly-stage decisions in the pharmaceutical development process carry outsized consequences for eventual clinical success, yet the computational tools underpinning these decisions remain fundamentally constrained. Computer-aided drug design (CADD) has transformed how researchers navigate chemical space and predict ligand–target interactions, but classical implementations rely on mechanical force-field approximations that fail to capture polarisation, charge transfer, and electron correlation effects central to molecular recognition and reactivity. Quantum-mechanical treatments that correctly describe these phenomena scale exponentially with system size on classical hardware, rendering them impractical for drug-relevant biomolecules. Quantum computing offers a physically motivated path beyond this scaling barrier: by exploiting superposition, entanglement, and interference, quantum algorithms can in principle simulate electronic structure with polynomial resource requirements for targeted problem classes. This article provides a theoretical review of how quantum computation integrates throughout the drug discovery pipeline, from target identification to lead optimisation. Its primary contribution is a pipeline-level mapping of quantum methods—including the Variational Quantum Eigensolver (VQE), quantum machine learning, and quantum-enhanced optimisation—to specific drug development stages. The review critically distinguishes near-term NISQ (Noisy Intermediate-Scale Quantum: current devices with 50–1,000 noisy qubits operating without full error correction) capabilities from fault-tolerant quantum computing (FTQC) requirements, quantifies current resource gaps through a worked CYP3A4 case study, and identifies algorithmic limitations (barren plateaus, ansatz expressibility, measurement overhead), hardware scalability, and error correction overhead as the principal barriers to practical deployment.1 IntroductionPharmaceutical development proceeds through the following sequential stages: (1) target identification and biological validation; (2) hit discovery and selection; (3) lead optimisation; and (4) preclinical and clinical evaluation. Despite decades of methodological progress across experimental biology, medicinal chemistry, and high-throughput screening, the aggregate probability of a candidate entering clinical trials eventually gaining regulatory approval remains below 10%. This low success rate reflects, in large part, the extraordinary cost burden of the pipeline (exceeding USD 2 billion per approved medicine on average) and timelines spanning more than 10 years from discovery to market (Hughes et al., 2011; Mullard, 2016) and relies on computational methods with limited predictive fidelity when extrapolated beyond their training domains. Computer-aided drug design (CADD) has been adopted broadly to make the early pipeline more tractable, enabling systematic exploration of chemical space, prediction of ligand binding geometries, and multi-parameter optimisation of pharmacokinetic and ADMET profiles. Nevertheless, the classical computational engines that underpin CADD—molecular mechanics force fields and simplified semi-empirical treatments—are constructed on assumptions that deliberately sacrifice electronic accuracy for computational tractability. This trade-off limits their ability to faithfully represent the quantum mechanical phenomena—charge redistribution, polarisation, van der Waals dispersion, and electron correlation—that ultimately govern binding selectivity, enzymatic reactivity, and off-target interactions. A physically accurate, non-approximate description of drug–target interactions requires quantum-mechanical treatment of the electronic structure, particularly in systems involving transition-metal centres, reactive covalent warheads, and charge-transfer interactions at enzyme active sites. High-level wavefunction methods and hybrid DFT approaches can deliver the required accuracy, but their computational cost grows steeply—as O(N7) or worse for coupled-cluster methods—with the number of electrons, making them impractical for all but small model systems on classical hardware. As a result, practitioners routinely accept approximate molecular-mechanics descriptions to preserve computational feasibility, introducing systematic errors in binding affinity prediction that propagate through hit identification and lead optimisation. Overcoming this accuracy–scalability dilemma is arguably the defining unsolved problem of computational drug discovery. Quantum computation exploits the physical principles of quantum mechanics at the hardware level: qubits—unlike classical binary switches—can be maintained in coherent superpositions and mutually entangled, allowing quantum processors to manipulate exponentially large state spaces with polynomial numbers of physical operations for certain problem classes. This architectural difference underpins the prospective advantage of quantum computers for electronic structure simulation: a quantum processor can naturally encode a molecular wavefunction in a way that would require exponentially many classical bits to represent explicitly. For drug discovery, this suggests a path toward binding affinity calculations, reaction pathway analysis, and molecular property prediction at accuracy levels currently unattainable within practical classical runtimes. Progress in superconducting and trapped-ion hardware has produced NISQ processors with tens to hundreds of noisy qubits, sufficient for hybrid quantum-classical variational algorithms such as VQE to tackle toy systems (H2, LiH, BeH2) far smaller than any drug-like molecule. These early demonstrations show that quantum computation can contribute to chemistry without the overhead of full error correction, but it remains an open and vigorously debated question whether NISQ-scale devices will ever deliver practically useful results for drug-sized molecules (Preskill, 2018). The appropriate framing is therefore one of gradual, modular integration: quantum subroutines addressing specific electronic-structure bottlenecks within otherwise classical CADD workflows, rather than wholesale quantum replacement of the pipeline. Despite growing research output, the current body of literature on quantum computing for drug discovery remains fragmented: most studies address isolated algorithms on small benchmark molecules or provide theoretical resource estimates without connection to realistic pharmaceutical workflows. A coherent framework that maps quantum methods to each stage of the drug discovery pipeline and critically evaluates near-term versus long-term prospects is largely absent from the literature. The most closely related work is Santagati et al. (Santagati et al., 2024), which provides an excellent overview of quantum algorithms for drug design; the present article complements that work with a detailed NISQ–FTQC comparison, explicit CYP3A4 resource estimates, and a classical–quantum benchmarking table. The present article addresses this gap. Its principal contributions are: (i) a critical analysis of how quantum algorithms may reduce the accuracy–scalability trade-off in molecular modelling; (ii) a systematic mapping of quantum methods to specific drug discovery stages, from target identification to lead optimisation; (iii) a comparative framework (Table 1) benchmarking quantum against classical approaches with explicit resource estimates; and (iv) honest evaluation of current limitations, including scalability, error mitigation, and the gap between NISQ-era research and fault-tolerant quantum computing targets.2 Overview of computational methods in drug discoveryThe pharmaceutical development pipeline progresses through four broad phases: (1) target identification and characterisation, where a disease-relevant biological macromolecule is selected and validated; (2) hit discovery, where chemical compounds showing initial activity against the target are identified; (3) lead optimisation, where promising hits are refined for potency, selectivity, and drug-likeness; and (4) preclinical and clinical evaluation, where safety and efficacy are tested in animal models and human volunteers. CADD has its greatest impact in phases (1)–(3), where quantum methods also offer the most significant prospective advantages. In the later clinical stages, molecular modelling plays a minimal role, since ADMET optimisation is properly addressed during lead optimisation (phase 3), well before regulatory submission. CADD methodologies applied in hit discovery and lead optimisation divide broadly into structure-based approaches, which exploit the known three-dimensional architecture of the biological target, and ligand-based approaches, which reason from patterns in known active compounds (Sliwoski et al., 2014). Structure-based approaches require a three-dimensional protein structure obtained via X-ray crystallography, cryo-EM, or computational prediction (Zhang, 2008), and use molecular docking to identify optimal ligand conformations in the binding pocket (Kalyaanamoorthy and Chen, 2011; Anderson, 2003). Ligand-based approaches rank candidates by similarity to known actives or via QSAR models correlating activity to molecular descriptors (Golbraikh et al., 2012; Bajorath, 2015). Both families share a fundamental limitation: inability to capture polarisation, charge transfer, and electron correlation accurately. These electronic effects require quantum-mechanical treatment, motivating the quantum computing approaches discussed in Sections 4,5.2.1 Target identification and characterizationTarget identification establishes which biological entity—most often a protein (enzyme, receptor, ion channel, or transporter)—plays a causal role in a disease pathway such that its modulation by a small molecule (NCE) or biologic (NBE) produces a therapeutically useful effect. Proteins are the dominant target class because their three-dimensional binding sites are amenable to small-molecule design; nucleic acid targets represent an expanding but distinct area not covered in depth here. A practically druggable target must be capable of binding small molecules with sufficient affinity and selectivity to elicit a biologically significant response both in vitro and in vivo. While all proteins are theoretically tractable, practical druggability is constrained by binding site geometry, conformational flexibility, and current technological limitations — as exemplified by historically challenging targets such as KRAS, which required multi-mechanism approaches (Hughes et al., 2011). Precise target identification and validation greatly increases the probability of success in subsequent discovery steps as well as aids in predicting side effects of target modulation (Hughes et al., 2011).Classical target identification tools include affinity chromatography (Lomenick et al., 2010; Burdine and Kodadek, 2004; Chan et al., 2010; Schenone et al., 2013), genetic screening, gene expression profiling, and systems biology network analysis (Czodrowski et al., 2009; Yang et al., 2012; Katsila et al., 2016; Nidhi et al., 2006). Each approach has limitations in specificity or throughput. Quantum computing could provide solutions at this stage because the bottleneck is the dimensionality and noise of multi-omics datasets: classical machine learning struggles with high-dimensional genomic feature spaces where sample sizes are small relative to feature count. Quantum-enhanced kernel methods and QML classifiers could in principle better exploit such data regimes for target prioritisation, as discussed in Section 7.2.Genetic screening, gene expression profiling, and systems biology network analysis complement biochemical target identification, allowing causal disease genes and regulatory pathway nodes to be identified from omics datasets (Schenone et al., 2013; Zheng et al., 2004; Czodrowski et al., 2009; Yang et al., 2012; Katsila et al., 2016; Nidhi et al., 2006). Machine learning models including Bayesian classifiers assist in target prioritisation across databases and literature (Yang et al., 2012; Katsila et al., 2016; Nidhi et al., 2006). The quantum relevance of these approaches is discussed in Section 7.2.For structure-based drug development, structural characterisation of the target protein is crucial after target identification. Protein purification and crystallization difficulties frequently limit the utilization of experimental methods like X-ray crystallography and NMR spectroscopy (Grey and Thompson, 2010). When no experimental structure is available, computational tools such as threading, comparison modelling and ab initio structure prediction are employed. Comparative modeling is the most common method to build 3-D protein structures by utilizing empirical structural models having known sequence similarities in the protein Data Bank (PDB) (Zhang, 2008) and (Bernstein et al., 1977). Regions where the structures do not have suitable templates are refined using molecular dynamics simulations, Monte Carlo minimization or genetic algorithms (Sliwoski et al., 2014).When appropriate templates are not available, free modeling methods can be used, including knowledge-based fragment assembly and physically justified methods like molecular dynamics simulations. Physical-based methods can offer information about the routes of protein folding, but are numerically intensive and require accurate force fields and extensive conformational sampling (Zhang, 2008; Bradley et al., 2005). The difficulties underline the capability of quantum simulation and optimization techniques, that can prove helpful in solving complex issues such as protein-folding. The identification of ligand binding and therapeutic pockets of the protein is called target characterization (Kalyaanamoorthy and Chen, 2011). It can be used directly if the binding site is known, as in the case of a co-crystallized complex of protein and ligand. If the binding site is not known, computational tools like Ligsite, Qsite Finder and CASTp are used to predict the likely binding site by using geometry and energy considerations (Laurie et al., 2006; Henri et al., 2010). The computational tools such as QSiteFinder and CASTp predict binding pockets from geometry and electrostatics. The bottleneck here is accuracy for flexible or novel targets; quantum simulation of protein electronic structure could in principle improve pocket identification where classical force fields fail (Section 5).2.2 Hit search and lead identificationHit identification finding compounds with measurable target activity has shifted from costly physical high-throughput screening toward virtual screening, which computationally prioritises candidates using either binding-site structure or statistical models derived from known actives, before any compound is synthesised.Ligand-based screening ranks candidates by similarity to known actives or via QSAR models mapping activity to molecular descriptors including quantum-mechanically derived charge and orbital representations (Acharya et al., 2011; Marrero-Ponce et al., 2012; Auer and Bajorath, 2008; Bajorath, 2002; Ekins et al., 2007; Zhang, 2011). Both approaches degrade sharply outside the chemical domain of the training data and offer no mechanistic rationale for activity trends, a gap that electronic-structure descriptors computed via quantum chemistry could in principle narrow. Early linear QSAR models (MLR, PCA, PLS) have largely been superseded by machine learning methods (ANN, SVM, random forests) that better capture the nonlinear structure–activity relationship (Xiang et al., 2012; Zhang et al., 2017; Colwell, 2018; Lima et al., 2016; Chen et al., 2018; Popova et al., 2018; Gomez-Bombarelli et al., 2018; Luechtefeld et al., 2018).Structure-based hit search instead predicts ligand–protein binding via molecular docking and scoring (Sliwoski et al., 2014; Halperin et al., 2002; Connolly, 1983; Ryde and Soderhjelm, 2016; Halgren, 1996; OBoyle et al., 2009). Its principal limitation is well established: standard molecular-mechanics (MM) scoring functions cannot fully capture polarisation and charge transfer, which are decisive in enzyme catalysis and metalloprotein binding (Ryde and Soderhjelm, 2016; van der Vaart and Merz, 1999; van der Vaart and Merz, 2000; Friesner, 2005; Bryce, 2011; Raha et al., 2007). QM/MM hybrid scoring addresses this by treating the ligand and key residues quantum-mechanically, but its computational cost confines it to the late-stage lead optimisation funnel rather than large-scale screening (Ryde and Soderhjelm, 2016; Rao et al., 2013; Zhou and Caflisch, 2010). This cost barrier — not a lack of theoretical accuracy is precisely the bottleneck that quantum-hardware acceleration of the QM region could address, in principle (Section 5).2.3 Lead discovery and optimizationAfter hit compounds are identified, they undergo a lead discovery and optimization phase aimed at generating a smaller set of candidates with improved drug-like properties. In order to improve pharmacokinetic and ADMET profiles in addition to biological activity, this step entails iterative cycles of CADD-guided design, chemical synthesis, and in vitro and in vivo testing (Sliwoski et al., 2014). Computational guidelines are particularly useful since it is assumed that successive chemical transformations will lead to ongoing changes in the properties of medications. Because they enable the rapid assessment of structural changes and the determination of the influence of structural modifications on activity and drug-likeness, particularly when comprehensive target structural data is lacking, QSAR models constructed with smaller, more targeted datasets are an important component of lead optimization. However, as optimization continues, structure-based approaches become more important, since the correct prediction of binding affinity is an important intermediate step that allows lead candidates to be prioritized (Raha et al., 2007). The effect of even small binding energy differences on the binding equilibrium constants is magnified orders of magnitude and thus has a significant impact on biological efficacy.Most binding affinity predictions methods rely on molecular mechanics (MM) force fields, which are not designed to capture important quantum mechanical effects such as polarisation, charge transfer, and electron correlation. These effects are particularly important in reactive binding processes, metalloproteins, and enzyme catalysis. Although these phenomena are naturally incorporated in quantum mechanical (QM) approaches, they are not commonly exploited in drug development because they are computationally impractical on conventional computers, particularly in large ligand-protein complexes. Finding more efficient techniques for quantum simulation is crucial since approximation QM approaches are typically restricted to tiny subsystems of the system and can contain significant mistakes depending on the level of theory used.Existing QM-based binding-affinity methods fall into three tiers of increasing rigour and cost: single-structure calculations on a fixed MM-optimised geometry (cheap but inaccurate); end-point methods that sample multiple conformations with a partial QM/MM treatment (intermediate cost and accuracy); and full free-energy simulations (FES/FEP) that exhaustively sample the binding pathway at high precision but at prohibitive computational cost for routine use (Ryde and Soderhjelm, 2016; Zhang and Zhang, 2003; Fedorov and Kitaura; Cavasotto et al., 2018; Luzhkov and Warshel, 1992; Rod and Ryde, 2005; Duarte et al., 2015; Abel et al., 2017). The progression from semiempirical methods through DFT to wavefunction-based CCSD(T) tracks steadily improving accuracy at steeply increasing cost (Ryde and Soderhjelm, 2016). This cost–accuracy ceiling is the key computational limitation that a quantum algorithm capable of efficiently simulating the QM active site could, in principle, overcome consistent with the language convention stated above.3 Quantum computingQuantum computation encodes information in qubits rather than classical bits. A qubit can occupy a coherent superposition of |0⟩ and |1⟩, and n entangled qubits span a 2n-dimensional Hilbert space that grows exponentially with n. Quantum algorithms exploit this structure for provable speedups: Shor’s algorithm factors integers in polynomial time, Grover’s search achieves quadratic speedup, and quantum simulation algorithms encode molecular Hamiltonians into qubit operators in a way that mirrors the physical state space naturally.Multiple physical hardware platforms have reached the NISQ regime, including superconducting transmon circuits, trapped ytterbium and barium ions, neutral Rydberg atom arrays, and photonic processors. In fact, proof-of-principle experiments performed on these platforms have shown that existing devices are capable of creating quantum states that are classically intractable (Preskill, 2018; Aaronson and Chen, 2016; Boixo et al., 2018) and thus demonstrate the principle of quantum computational advantage. However, all current processors remain vulnerable to gate errors, qubit decoherence, and readout infidelity that accumulate with circuit depth. Full quantum error correction via the surface code or related topological schemes can suppress logical error rates below any target threshold, but the required ratio of physical-to-logical qubits estimated at roughly 1,000:1 at current physical error rates places genuinely fault-tolerant operation well beyond present hardware.NISQ processors have been deployed within hybrid quantum-classical frameworks for variational algorithms, of which VQE is the primary example (McClean et al., 2016). A parameterised quantum circuit prepares a trial wavefunction; the quantum processor measures the energy expectation value; and a classical optimiser adjusts the circuit parameters iteratively. It must be emphasised, however, that no NISQ-based hybrid workflow has yet solved a chemically or pharmaceutically industrially relevant problem — all published demonstrations remain on toy systems (H2, LiH, BeH2) with active spaces of 12–16 orbitals (mapped to 12–16 qubits via Jordan–Wigner encoding) (Preskill, 2018; McClean et al., 2016; Goings et al., 2022). Whether NISQ devices can scale beyond these toy systems to drug-relevant electronic structure problems at chemical accuracy is an open and actively debated question (Preskill, 2018; Bharti et al., 2022). The hybrid framework is therefore best understood as a research direction: it has not been deployed in any industrial pharmaceutical workflow, no drug-relevant problem has been solved using it, and all published demonstrations remain on toy systems far smaller than any drug candidate. It is a promising methodological approach that requires significant hardware and algorithmic advances before practical deployment becomes feasible.4 Quantum computing techniques for drug discovery4.1 Quantum algorithms for drug and compound designQuantum algorithms address the fundamental computational bottleneck of classical chemistry methods: the exponential scaling of exact methods such as Full Configuration Interaction (FCI), whose cost grows as O(N!) with system size, making them intractable for molecules beyond a few tens of electrons on classical hardware (Montanaro, 2016). It is essential to distinguish between NISQ-compatible near-term algorithms and those requiring fault-tolerant quantum computing (FTQC). The quantum chemistry methods underpinning these algorithms—including DFT, ab initio wavefunction theory, and VQE—are treated in detail in Section 5. Grover’s search algorithm (Grover, 1996) achieves a quadratic speedup for searching unsorted databases and is conceptually relevant for screening chemical libraries against molecular specifications. But practical implementations will rely on the Quantum Phase Estimation (QPE) subroutine, which will require fully error corrected qubits, which are not present in existing NISQ devices. Grover’s algorithm is thus not a near-term NISQ application, but rather an FTQC target that requires fully error-corrected logical qubits. The most widely used algorithms from the NISQ era are the Variational Quantum Eigensolver (VQE) which is used for approximating the ground state energy of a molecular Hamiltonian with short-depth parameterized circuits optimized classically (Peruzzo et al., 2014). VQE is especially well adapted to the calculation of the lowest electronic states of molecules that are key to prediction of binding geometry, reactivity, and pharmacological activity. 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Drug Discov. 6:1815176. doi: 10.3389/fddsv.2026.1815176Received22 February 2026Revised29 July 2026Accepted10 August 2026Published07 September 2026Volume6 - 2026Edited byRuby Srivastava, Indian Institute of Technology Bombay, IndiaReviewed byEugene Radchenko, Lomonosov Moscow State University, RussiaNagaraju Regonda, Malla Reddy University, IndiaUpdatesCheck for updates Copyright © 2026 Gadbail, Rewatkar, Jumde, Manker, Nagre and Zade. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. 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