--- title: PoseBusters Passing Rate url: https://www.emergentmind.com/topics/posebusters-passing-rate type: topic --- # PoseBusters Passing Rate The PoseBusters passing rate is a quantitative metric that evaluates the proportion of predicted protein–ligand poses that conform to a stringent suite of 20 geometric, chemical, and energetic quality-control checks. It is principally used to assess the physical plausibility and chemical feasibility of molecular docking outputs, especially in the context of deep generative models and structure-based drug design (SBDD). Unlike traditional RMSD-based criteria, PoseBusters passing rate (designated PBValid or PR) directly measures whether a pose is chemically valid, geometrically sound, and free of low-level steric and energetic pathologies as defined by established computational chemistry toolkits such as RDKit. ## 1. Definition and Mathematical Formulation The PoseBusters passing rate is defined for a set of $N$ candidate docked poses $\{M_i\}$ as the fraction of poses passing all specified quality checks. Each generated pose $M$ is assigned a binary label: \[ \mathrm{Pass}(M) = \begin{cases} 1 & \text{if } M \text{ passes all PoseBusters checks} \\ 0 & \text{otherwise} \end{cases} \] The aggregate passing rate is: \[ \mathrm{PBValid} = \frac{1}{N}\sum_{i=1}^N \mathrm{Pass}(M_i) \times 100\% \] This definition applies regardless of method or dataset. PoseBusters tests are distributed across three main categories: - **Chemical validity and consistency:** Encompasses sanitization, InChI-convertibility, and molecular formula/bonding checks. - **Intramolecular geometric and energetic correctness:** Includes bond length and angle bounds ($0.75\, d_\text{LB}(i,j) \leq d_{ij} \leq 1.25\, d_\text{UB}(i,j)$), ring planarity ($\max_i\, \mathrm{dist}_\text{plane}(i) \leq 0.25$ Å), internal steric clashes, and strain energy ($E_{\rm pred}/\bar{E} \leq 100$). - **Intermolecular plausibility:** Minimum protein–ligand/cofactor/water atom distances, and pocket volume overlap constraints ($V_\text{overlap}/V_\text{lig} < 7.5\%$). For specific recent benchmarks, further submetrics have been introduced (e.g., PB-Valid-Mol for ligand-only checks and PB-Valid-Dock for protein–ligand interface checks), and for extensive SBDD tasks PBValid is applied to thousands of predicted poses per test set [2308.05777, 2505.07286, 2601.22466]. ## 2. PoseBusters Check Pipeline and Criteria PoseBusters employs a sequential pipeline, automating 20 discrete quality-control filters: **A. Chemical Validity:** - RDKit molecular sanitization: passes only if atom valencies, aromaticity, hybridization are chemically plausible. - InChI comparison: exact match to reference connectivity, hydrogens, tetrahedral centers, and double-bond E/Z stereochemistry. **B. Geometric and Energetic Checks:** - Bond distances and angles: each must lie within ±25% ideal bounds. - Aromatic ring and C=C planarity: deviation $<$ 0.25 Å. - Internal non-bonded atom pairs: minimum separation per scaled distance geometry. - Strain energy of docked pose compared to conformational ensemble, ratio capped at 100. **C. Intermolecular Constraints:** - Minimum separation (weighted van der Waals/covalent radii) from protein, cofactors, waters. - Pocket overlap: ligand volume overlapping with protein and cofactors $<$ 7.5%. All criteria are strictly binary; a pose failing any check is discarded, and only entirely "PB-valid" poses contribute to PR. ## 3. Benchmark Datasets and Evaluation Protocols The metric is typically applied on standardized protein–ligand benchmarks: - **Astex Diverse (85 cases):** Classical and deep-learning docking methods compared head-to-head. - **CrossDocked2020 and PoseBusters Bench (hundreds to $>10^4$ cases):** Data filtered for crystal poses RMSD $<$ 1 Å, with train/test splits controlled for sequence identity ($\leq$ 30%) to eliminate leakage [2601.22466, 2505.07286]. - **Held-out OOD sets:** Complexes not seen in training, used to measure generalization. Evaluation workflow involves: (1) de novo ligand generation (100 candidates per protein target is typical), (2) rigid docking to predicted or reference pockets (frequently with AutoDock Vina), (3) batch PB-valid checks, and (4) computation and comparison of passing rates against benchmarks and prior methods. ## 4. Comparative Performance Across Methods PoseBusters passing rates vary widely across algorithmic paradigms: | Method | PBValid (%) | |-------------------|-----------------| | AR (Luo et al.) | 59.0 | | Pocket2Mol | 72.3 | | TargetDiff | 50.5 | | DecompDiff | 71.7 | | MolCRAFT | 84.6 | | EvoEGF-Mol | 93.4 | | MolPilot (VOS) | 95.9 | | AutoDock Vina | 51.0 (*PB-Bench*)| | CCDC Gold | 48.1 (*PB-Bench*)| | DiffDock | 14.0 (*PB-Bench*)| | DeepDock | 6.8 (*PB-Bench*)| State-of-the-art generative SBDD models based on information geometry (EvoEGF-Mol), Bayesian flow networks (MolPilot), and optimal schedule design have pushed PBValid to reference or even super-reference levels (93–96%), dramatically exceeding both classical docking tools and earlier deep-learning baselines [2601.22466, 2505.07286, 2308.05777]. A plausible implication is that these geometric and probabilistic advances are addressing modality mismatch and training instability in previous protocols. Conversely, deep-learning methods lacking robust inductive bias frequently report sub-20% on novel benchmarks unless post-docking force-field minimization is applied, which can "rescue" many error modes. ## 5. Methodological Advances Underlying High Passing Rates Establishment of high PBValid rates in recent models relies on explicit technical innovations: - **Unified Exponential-family modeling:** Jointly modeling atomic coordinates and chemical types as a single composite exponential-family distribution under Fisher–Rao metric prevents geometric–chemical mismatch, supporting co-refinement of 3D structure and 2D identity [2601.22466]. - **Evolving Exponential Geodesic Flow (EvoEGF):** Dynamically concentrating endpoint distributions avoid trajectory collapse and maintain stable variance/support at each generation time step, resulting in more realistic samples and smooth training [2601.22466]. - **VLB-Optimal Scheduling (VOS):** Interpreting the Variational Lower Bound as a line integral in noise-schedule space, and optimizing the denoising path jointly across continuous and discrete variables, yields superior molecular and interaction fidelity [2505.07286]. - **Post-docking minimization:** Application of classical force fields (AMBER/Sage via OpenMM) to deep-learning outputs substantially increases PBValid (e.g., DiffDock from 14% to ≈50%), indicating current models do not fully encode all relevant physical constraints [2308.05777]. These advances reduce artifacts such as twisted geometries, bond strain, and steric clashes, yielding low Jensen–Shannon divergences in bond/angle/torsion distributions, strain energies comparable to crystal structures, and improved fingerprint similarity for critical protein–ligand interactions. ## 6. Interpretations, Limitations, and Implications High PoseBusters passing rates are correlated with chemically plausible, strain-free, and physically sensible candidate ligands. Recent models demonstrate almost perfect conformance on in-distribution CrossDock benchmarks, and maintain robust passing rates ($\sim$80%) on challenging out-of-distribution held-out tests. However, the metric is fundamentally binary and does not measure degree-of-error for near-failing poses, nor does it subsume criteria such as bioactivity, affinity, or specific pharmacophore matching. All-or-nothing filtering may thus discard marginally non-ideal candidates that remain viable after minimal energetic relaxation. Empirical evidence from the effect of force-field minimization suggests that incorporating classical physics elements may be critical for further progress. A plausible implication is that future research in SBDD—particularly with generative neural methods—must combine statistical manifold modeling with classical physical restraints to ensure both speed and generalizability of physically valid outputs. ## 7. Impact and Future Directions PoseBusters passing rate has become a key metric for benchmarking SBDD pipelines, given its strict composite filtering of chemical, geometric, and energetic criteria. It enables direct comparison of diverse methods’ outputs, independent of RMSD-related errors and dataset-dependent noise, and exposes limitations of both classical docking software and contemporary deep generative models. As information-geometric and schedule-based methodologies continue to increase PBValid towards “reference-level” rates, the metric will remain central in algorithmic assessment, error analysis, and the identification of robust inductive biases for next-generation drug design platforms [2601.22466, 2505.07286, 2308.05777]. Source: https://www.emergentmind.com/topics/posebusters-passing-rate