Q-Score: A Quantum-Native Scoring Function for Molecular Docking

BCover: An Electronic Structure-Based Scoring Suite for Reaction-Aware Covalent Docking

Covalent virtual screening requires ranking compounds according to both noncovalent recognition and their ability to adopt a reaction-competent geometry with an appropriately reactive warhead. Here, we introduce BCover, a reaction-aware scoring suite that combines pre-reactive docking with quantum-chemistry-derived ligand reactivity descriptors, the electrostatic properties of the protein pocket. Quantum-chemical descriptors are aggregated using a nonlinear tree-based scoring model. BCover was evaluated retrospectively on the COValid benchmark, comprising nine targets and ten reactive sites, and compared with AutoDock, DOCK6, DOCKovalent, AlphaFold3 with Rosetta rescoring, and AlphaFold3 confidence-based ranking. BCover achieved an average adjusted LogAUC of 31% (57% max), an average ROC-AUC of 0.88 (0.96 max), an average EF1 of 18 (33 max). Its average LogAUC exceeded those of the classical docking methods and AlphaFold3-Rosetta, although AlphaFold3-mPAE provided the strongest overall enrichment. At an average runtime of about 10~s per ligand, BCover was approximately 25-fold faster than the evaluated AlphaFold3 workflows and achieved the highest average time-adjusted virtual-screening productivity index. Redocking experiments further showed that the method recovered near-native ligand conformations. These results demonstrate that combining docking-derived geometry with ligand local electronic reactivity and pocket electrostatics provides an efficient and interpretable strategy for covalent ligand prioritization. BCover is intended as a high-throughput screening method that complements more computationally demanding QM/MM and free-energy calculations during subsequent lead optimization.

Native Contact Ratio as a Topological Metric for Machine Learning Based Molecular Docking

Accurate identification of near-native ligand binding poses is a central challenge in structure-based drug design. From a physical point of view, the successful construction of a protein–ligand complex structure is dependent on whether protein and ligand can form enough atomically pairwise interactions that result in a global energy minimum. In this work, we report a machine learning scoring strategy for protein–ligand screening which explicitly considers the Native Contact Ratio (NCR), a topology inspired metric that quantifies the preservation of protein–ligand interfacial contacts as well as interaction energy. This physics-awared supervision strategy provides a simple but efficient gradient field that faithfully reflects the complicated protein energy landscape than conventional 3D coordinate-based objectives. Building on this principle, we present DeepNCR, an energy-informed Transformer framework that encodes approximate Coulombic and dispersive interaction potentials across the protein–ligand binding interface. Furthermore, we introduce a feature pruning step that compresses the interaction tensor from 1470 to 868 dimensions, further improving signal-to-noise ratio and directing model attention toward the interaction motifs critical for binding specificity. The model optimizes topological objectives and at inference drives pose refinement through a differentiable hybrid gradient field integrating predicted NCR and AutoDock Vina energetics. Extensive evaluation on the CASF-2016 benchmark and the 3D-DISCO cross-docking data set demonstrates consistently high performance: a Top-1 docking success rate of 94.7%, a 1% Enrichment Factor of 21.21 in virtual screening, and a Top-1 cross-docking success rate of 34.8%. Mechanistic analysis reveals that NCR-guided optimization enables decoy escaping from local energy minima and drives the recovery of disrupted native interactions, confirming that NCR captures the physical determinants of binding rather than mere geometric proximity.

Quantum-Driven High-Throughput Docking: Advances in Molecular Scoring and Drug Discovery.

Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer

Molecular docking is a vital computational task in drug discovery, wherein the objective is to efficiently identify optimal binding poses between a ligand and a target receptor protein. Due to the combinatorial explosion of possible binding configurations, docking of large and flexible molecules remains a computationally intensive problem, especially at scale. Early studies have revealed that the molecular docking can be re-cast as a maximum vertex-weighted clique problem (MVWCP) problem on a compatibility graph to be solved classically. In this work, we proposed a hybrid quantum-classical approach for molecular docking leveraging the MVWCP formalism with a variational full-basis encoding (FBE) strategy, which enables efficient encoding of classical binary variables with Bloch sphere vectors. We further prove that a global minimizer of the FBE objective can always be chosen to be a pure product state, thereby providing a rigorous justification for its optimization using a unitary variational circuit. The molecular docking problem is first mapped to a cost Hamiltonian that is minimized within a variational framework, optimized via a randomized imaginary time evolution (ITE)-inspired warm start, and gradient-based techniques. Finally, we also executed the circuit on an IBM quantum computer, underlying the feasibility and of quantum-assisted optimization for structure-based drug design and point towards the broader utility of advanced encoding techniques in quantum optimization for computational biology.

Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction

For low-data and resource-constrained regimes typical of quantum chemistry, parameter-efficient learning is a key objective. Here, we propose a topology-aligned inductive bias in which the model architecture mirrors the molecular bond graph: atoms map to a fixed register of computational units, and bonds determine which pairs interact through shared learnable parameters. This principle is instantiated in two architectures: a variational quantum circuit (Iso-QGNN) and a parameter-matched classical message-passing network (Iso-CGNN). The models are benchmarked on HOMO-LUMO and dipole moment binary classification tasks over the QM9 benchmark. With 64 trainable parameters, the implementations achieve test AUCs of approximately 0.89 (quantum) and 0.92 (classical) on the gap task, and close to 0.78 (both) on the dipole task. The models reach 90% of asymptotic performance within about 300 training molecules and gradient norms remain stable throughout training. These results indicate that the topology-aligned inductive bias is the active ingredient driving parameter efficiency at QM9 scale, with implications for matched-baseline benchmarking in quantum machine learning.

Evaluating Electrostatic Embedding MLIP/MM for Relative Binding Free Energy Calculations

Alchemical relative binding free energy (RBFE) calculations are limited by the fixed-charge approximation of classical force fields. Hybrid machine learning interatomic potential/molecular mechanics (MLIP/MM) schemes correct ligand strain, but under mechanical embedding still describe ligand--environment electrostatics with static point charges. Electrostatic embedding schemes coupling machine-learned charges to the MM environment have been proposed and validated against QM/MM for simple systems, but not tested in a production alchemical workflow. We take the electrostatic embedding scheme of Semelak et al.\ and evaluate it on protein--ligand RBFE. We trained a TensorNet2 model, \texttt{AceFF-2-RESP-1}, on $10^{6}$ conformations from the AceFF dataset, jointly predicting energies, forces and Restrained Electrostatic Potential (RESP) charges. We chose RESP over MBIS for commensurability with the AMBER-family force field it couples to. The predicted charges enter the short-range direct-space part of the particle mesh Ewald sum, with Thole damping to prevent polarization catastrophes during alchemical transformations. We tested the scheme across five targets from the Wang et al.\ benchmark set, fixed in advance by a prior study, with three replicates per edge and matched protocols. Electrostatic embedding improved every accuracy and correlation metric for TYK2 ($\Delta\Delta G$ RMSE $0.86 \rightarrow 0.45$~kcal/mol against GAFF2), but performed comparably to the classical and mechanical-embedding baselines for CDK2, thrombin, p38 and JNK1. Standard single-molecule energy and charge benchmarks were not good predictors of this target-dependent outcome. TYK2 combined good $\Delta\Delta G$ accuracy with the lowest force error on the Schr\"odinger benchmark, but this pattern did not hold for the other targets.