On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction

Shunzhou Wan; Xibei Zhang; Xiao Xue; Peter V. Coveney

physics.chem-phMar 2, 2026

On the Reliability of AI Methods in Drug Discovery: Evaluation of Boltz-2 for Structure and Binding Affinity Prediction

Authors: Shunzhou Wan, Xibei Zhang, Xiao Xue, Peter V. Coveney

Organizations: Centre for Computational Science, University College London, London, WC1H 0AJ, U.K. · Advanced Research Computing Centre, University College London, London, WC1H 0AJ, U.K.

Abstract

Despite continuing hype about the role of AI in drug discovery, no "AI-discovered drugs" have so far received regulatory approval. Here we assess one of the latest AI based tools in this domain. The ability to rapidly predict protein-ligand structures and binding affinities is pivotal for accelerating drug discovery. Boltz-2, a recently developed biomolecular foundation model, aims to bridge the gap between AI efficiency and physics-based precision through a joint "co-folding" approach. In this study, we provide an extensive evaluation of Boltz-2 using two large-scale datasets: 16,780 compounds for 3CLPro and 21,702 compounds for TNKS2. We compare Boltz-2 predicted structures with traditional docking and binding affinities with binding free energies derived from the physics-based ESMACS protocol. Structural analysis reveals significant global RMSD variations, indicating that Boltz-2 predicts multiple protein conformations and ligand binding positions rather than a single converged pose. Energetic evaluations exhibit only weak to moderate correlations across the global datasets. Furthermore, a focused analysis of the top 100 compounds yields no significant correlation between the Boltz-2 predictions and the binding free energies from fine-grained ESMACS, alongside observed saturation difference in ligand structures. Our results show that while Boltz-2 offers substantial speed for initial screening, it lacks the energetic resolution required for lead identification. These findings highlight the necessity of employing physics-based methods for the reliability and refinement of AI-derived models.

Figures & tables

Figure 1: Structural comparison of Boltz-2 and docking predictions for 3CLPro (a-c) and TNKS2 (d-f). Distributions are shown for pairwise RMSDs of the protein structures (a, d) and ligand poses (b, e), alongside LDDT scores (c, f) evaluating the preservation of the local atomic environment and binding site interactions.

Figure 2: Representative binding sites predicted by Boltz-2 co-folding for a) 3CLPro and b) TNKS2. The protein (cartoon) and a bound compound (yellow sticks) are from PDB 6W63 and 4UI5, respectively. Five binding sites (i-vi) are predicted for 3CLPro, with LDDT metric values of 1.8 Å, 6.4 Å, 7.8 Å, 13.0 Å, 13.2 Å and 14.3 Å (Fig. 1 c), respectively. For TNKS2, all but one compounds bind at or near the x-ray identified binding site (i, ii), with the exception at a different site (iii), with LDDT values (Fig. 1 f) of 0.8 Å, 3.6 Å and 6.9 Å, respectively.

Figure 3: The distribution of confidence scores from the Boltz-2 predictions of both 3CLPro and TNKS2 systems. Percentages are calculated relative to the total number of ligands in each protein system.

Figure 4: Correlation between binding free energies predicted by Boltz-2 ( $\Delta G_{\text{Boltz}}$ ) and calculated via ESMACS ( $\Delta G_{\text{ESMACS}}$ ) for a) 3CLPro and b) TNKS2. Dashed lines indicate standardised major axis (SMA) regressions. Data points are coloured by Boltz-2 predicted binding probabilities.

Figure 5: Binding affinity consistency between two independent runs of Boltz-2 for a) 3CLPro and b) TNKS2.

Figure 6: Comparison of saturation state deviations. Boltz-2 saturation is derived from hydrogen placement on predicted heavy-atom coordinates. Representative deviations of the saturation states from Boltz-2 predictions are shown for rings (a) and aliphatic chains (b), alongside their SMILES string counterparts (c, d).

Figure 7: Comparison of binding free energies from Boltz-2 prediction ( $\Delta G_{Boltz}$ ) and ESMACS simulations. Three sets of ESMACS simulations are performed, denoted as $\Delta G_{Boltz}^{ESMACS\_1}$ , $\Delta G_{Boltz}^{ESMACS\_2}$ and $\Delta G_{Docked}^{ESMACS}$ , respectively, initiated from structures derived directly from Boltz-2 predicted structures, derived from SMILES strings with positions of heavy atoms from Boltz-2 predictions, and from docked approach. Some compounds have different saturation or protonation states for $\Delta G_{Boltz}^{ESMACS\_1}$ and $\Delta G_{Boltz}^{ESMACS\_2}$ calculations; they are shown as red dots.

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DBMol: Design of High-Affinity, Target-Specific Small Molecules through Structure Prediction Models

Yiming Qin, Kai Yi, Miruna Cretu, Sjors H. W. Scheres+2

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Protein-Ligand Binding Affinity Prediction Molecular Optimization Structure-Based Drug Design Molecular Generation Drug Discovery

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Designing small molecule ligands that bind with high affinity to specific protein pockets is a fundamental goal in drug discovery, as small molecules constitute a major fraction of approved therapeutics. Recent breakthroughs in structure prediction, such as AlphaFold-3 and Boltz-2, enable accurate biomolecular interaction prediction and show promise as foundation models for downstream tasks, including binding affinity prediction. We propose to leverage these models and introduce DBMol, a new structure predictor-guided framework for de novo small molecule design. DBMol formulates an alternating optimization and projection process. In the optimization stage, DBMol starts from an initial molecule and uses gradient-based optimization to improve pocket-specific interactions and predicted binding affinity using a structure prediction model. In the projection stage, a flow-matching model maps the optimized molecular graph to discrete and chemically valid molecules. Experiments show that DBMol effectively optimizes the Boltz-2 affinity proxy and generates molecules with strong predicted affinity and specificity under Boltz-2 evaluation. To reduce self-confirmation bias, we further evaluate generated molecules using held-out metrics, including AF3-based evaluation. DBMol substantially improves pocket coverage while maintaining molecular diversity over unconditional generation, and is competitive under held-out metrics despite the absence of reference-ligand supervision. These results support the promise of structure prediction models as effective optimization signals for de novo molecular design.

Abstract

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