Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

Hao Zhou; Hongbo Ma; Jie Yu; Keyue Qiu; Mingyue Zheng; Wei-Ying Ma; Yushuai Wu; Yuxuan Song; Zhilong Zhang; Ziyao Cao

REVIEW 6 major objections 5 minor 12 references

Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks

T0 review · 6 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read MolJO guides molecule optimization on the Bayesian belief space of a flow network, jointly steering continuous coordinates and discrete atom types, and reports state-of-the-art success on CrossDocked2020.

desk verdict Solid new guidance mechanism for BFN-based molecule optimization; the SOTA claim hinges on an unstated regressor train/test split that must be fixed before believing it. read the letter →

arxiv 2411.13280 v4 pith:5ZJFWMGO submitted 2024-11-20 q-bio.BM cs.AI

Keyue Qiu , Yuxuan Song , Jie Yu , Hongbo Ma , Ziyao Cao , Zhilong Zhang , Yushuai Wu , Mingyue Zheng

show 2 more authors

Hao Zhou Wei-Ying Ma

This is my paper · ORCID

classification q-bio.BMcs.AI

keywords structure-basedmoleculeoptimizationBayesianflownetworksgradientguidancejointcontinuous-discreteSE(3)-equivariantsamplingbackwardcorrectionCrossDocked2020scaffoldhopping

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper aims to make gradient-guided optimization work for structure-based molecule design, where a molecule's atoms have both continuous 3D coordinates and discrete atom types. It claims that Bayesian Flow Networks offer a continuous, differentiable 'belief' space, the posterior parameters θ, on which one can compute joint gradients over both modalities at once, without relaxing the discrete atom types into Gaussians or guiding only coordinates. The resulting method, MolJO, reaches state-of-the-art results on the CrossDocked2020 benchmark: 51.3% success rate, Vina Dock -9.05, SA 0.78, a fourfold improvement in success rate over the coordinate-only gradient baseline TAGMol, and a 2x higher fraction of 'me-better' molecules that improve affinity, drug-likeness, and synthesizability together. The paper also introduces a backward-correction sampling strategy that re-optimizes a sliding window of past belief states, which it argues aligns guidance gradients across steps and balances exploration with exploitation. If these claims hold, MolJO would be the first gradient-based SBMO method that jointly and equivariantly guides both modalities, and it extends naturally to constrained tasks such as R-group optimization and scaffold hopping.

What carries the argument

The central object is the Bayesian belief θ, the receiver's posterior parameters over continuous coordinates θ^x and discrete atom-type logits θ^v, updated through the Bayesian update function h(θ_{i-1}, y, α_i). Guidance is exerted on θ via the score ∇_θ log p_E(θ) with p_E ∝ exp[-sE(θ,p,t)], and Proposition 4.1 reduces the guided transition kernel to shifting the Gaussian of θ^x by σ^x g_{θx} and the Gaussian of y^v by σ^v g_{yv}, where the gradients come from a learned SE(3)-equivariant energy regressor. The second mechanism is backward correction: rather than sampling θ_i only from θ_{i-1}, the update aggregates accuracy over a window of k past steps, p_U(θ_n | θ_{n-k}, x̂_n; Σ_{i=n-k+1}^n α_i), so the optimized reconstruction x̂_n replaces earlier, suboptimal estimates. Together, these two mechanisms, belief-space gradient guidance and windowed backward correction, constitute MolJO.

What would settle it

Replace the learned regressor with the exact oracle (Vina docking score, QED, and SA computed on decoded molecules) at each guidance step, or measure the gradient-field smoothness of the learned regressor on beliefs sampled from the guided trajectory at scales 50–100; if the success rate collapses or the gradient field is demonstrably non-smooth on those out-of-distribution beliefs, the guidance mechanism is the bottleneck.

Watch

Extended reading notes

Core claim

The central claim is that the correct object to guide in a Bayesian Flow Network is not the noisy latent y nor the clean molecule, but the aggregated Bayesian posterior belief θ, which lives in continuous space for coordinates and on the probability simplex for atom types. Proposition 4.1 derives, via a first-order Taylor expansion of an energy function E(θ,p,t), a guided transition kernel in which the Gaussian mean of the coordinate belief shifts by the coordinate gradient and the Gaussian latent for discrete counts shifts by the type gradient, so both modalities are steered simultaneously while keeping the discrete belief on the simplex. Proposition 4.4 shows that this guided sampling is SE(3)-equivariant whenever the backbone and energy function are equivariant and the protein is centered. The paper further claims that a backward-correction update, which aggregates the accuracy over a sliding window of past steps and conditions the current belief on the optimized reconstruction, unifies previous BFN sampling strategies and empirically improves both unguided sampling and the effectiveness of guidance. On CrossDocked2020, MolJO reports the best success rate (51.3%), Vina Dock (-9.05), and SA (0.78) among compared optimization methods, with 39.8% of generated molecules improving affinity, QED, and SA simultaneously.

Load-bearing premise

The load-bearing premise is that the learned property regressor E(θ,p,t), trained on noised BFN beliefs from CrossDocked, gives accurate and smooth gradients across the whole guided trajectory, including out-of-distribution beliefs reached at guidance scale 50; if the regressor is inaccurate or non-smooth there, the first-order Taylor approximation in Proposition 4.1 will steer sampling incorrectly.

Editorial extensions

If this is right

If the reported numbers hold, gradient guidance becomes a viable plug-and-play optimizer for structure-based drug design without per-target oracle simulations or RL fine-tuning of the backbone.
Joint guidance over coordinates and atom types resolves the modality imbalance that limited coordinate-only guidance, so optimized molecules can improve drug-likeness and synthesizability rather than affinity alone.
The backward-correction window size k gives a single knob that interpolates between exploration and exploitation, letting practitioners trade sample diversity against optimization intensity.
The framework extends to constrained settings such as R-group optimization, fragment growing, and scaffold hopping through infilling, meaning the same gradient-guided sampler handles lead-optimization tasks beyond de novo design.
Equipping MolJO with top-of-N oracle selection raises success rate to 70.3% on the same benchmark, indicating that the gradient signal and a small screening budget are complementary.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The paper leaves implicit that the same belief-space guidance recipe should transfer to other BFN-based generative models beyond molecules, since the derivation only uses the Gaussian or simplex structure of θ and an energy function; materials, graphs, or unified 3D models trained as BFNs could inherit MolJO-style joint guidance by swapping in a suitable regressor.
A testable extension is to penalize the learned regressor for non-smooth gradients on out-of-distribution beliefs reached at guidance scales 50–100; if the first-order Taylor approximation is the bottleneck, such a penalty should push the reported success rate higher.
The backward-correction update can be read as a sliding-window proximal optimizer over the belief chain, suggesting that the window size k tunes a bias-variance trade-off between error accumulation (small k) and stale, over-smoothed updates (large k), which would predict an optimal window near the value 130 used here.
If joint θ-guidance is the causal driver of the 2x 'me-better' ratio, then coordinate-only guidance applied to the same backbone should reproduce the failure mode of good Vina but poor SA/QED, while type-only guidance should produce the opposite, a contrast the ablations hint at but do not fully disentangle.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Short version: this is a genuine methods contribution, and the central guidance idea is real. The reported 51.3% Success Rate is the one number to check, and the paper currently leaves an ambiguity that makes it impossible to verify from the text alone.

What's new: MolJO is the first to push gradient guidance through the BFN belief parameters for both continuous coordinates and discrete atom types jointly, instead of guiding only coordinates (TAGMol) or assuming a Gaussian classifier (DiGress). The first-order Taylor product-of-experts derivation in Prop. 4.1 is clean. The backward-correction sampling with a sliding window is a sensible extension that unifies the k=1 and k=n samplers and empirically stabilizes guidance; the ablation is convincing, and they provide 95% confidence intervals plus paired t-tests. The SE(3)-equivariance argument is standard but correctly transferred. Credit is also due for extending to R-group optimization and scaffold hopping.

Soft spots, in order of size. First, the property regressor E(θ,p,t) is the engine of the method, but Appendix D.1 does not state whether it was trained on the 100k training split or on the full CrossDocked set including the 100 test proteins. If it saw test proteins, the gradient guidance could exploit test-reference-like solutions, and the '4x' claim would reflect memorization rather than optimization. The stress-test note lands here. I do not see positive evidence of leakage, but the omission is exactly what a referee should force into the next version. Second, the headline comparison to TAGMol is not apples-to-apples: different backbones (MolCRAFT vs TargetDiff). The 4x Success Rate is plausible but not a clean attribution of the guidance mechanism; the joint-modality ablation helps but does not fix the cross-backbone confound. Third, the code link points to the MolCRAFT repository; the MolJO-specific artifacts are not clearly there, so the main result is not independently reproducible from the link. Fourth, the backward correction is a heuristic without a rigorous bias or convergence argument, and the guidance scale/window are tuned on the benchmark. Minor: the size restriction in Table 1 is defensible but complicates direct comparison with methods that explore larger molecules.

Who this is for: anyone working on gradient guidance for structured generative models, or on structure-based lead optimization. The method deserves a serious referee; the main empirical claim needs the split confirmed and ideally code cleanup, but the work is worth engaging seriously.

Recommendation: send to peer review with requests to clarify the regressor train/test split, release MolJO code with the trained regressor, and provide a same-backbone comparison with TAGMol if possible.

Referee Report

6 major / 5 minor

Summary. The paper proposes MolJO, a gradient-based structure-based molecule optimization framework built on Bayesian Flow Networks (BFNs). The method derives a joint guidance signal over continuous atomic coordinates and discrete atom types by applying product-of-experts sampling with a first-order Taylor expansion of a learned property energy function (Prop. 4.1), and introduces a 'backward correction' sampling strategy that updates the current belief from a sliding window of past history rather than only the previous step. The authors claim state-of-the-art results on CrossDocked2020 (Success Rate 51.3%, Vina Dock -9.05, SA 0.78), a 4x improvement over the gradient-based TAGMol baseline, a 2x 'Me-Better' ratio relative to 3D baselines, and additional results for multi-objective optimization, R-group optimization, and scaffold hopping.

Significance. If the empirical claims hold, MolJO would be a strong contribution: it extends classifier-guidance ideas to the continuous-discrete belief space of BFNs, provides an equivariance proof, and demonstrates broad applicability beyond unconstrained optimization. The paper includes useful empirical safeguards: 95% confidence intervals for the main metrics (Table 6), paired t-tests for the backward-correction ablation (Table 12), an inference-time comparison (Table 16), and a thoughtful analysis of molecular-size confounds (Appendix E). The backward-correction ablation is informative and shows a clear performance gain. However, the central SOTA and comparison claims rest on several details that are either underspecified or framed in a way that overstates the contribution, notably the training split of the property regressor, the backbone mismatch in the 4x claim, and the lack of algorithmic detail for constrained optimization. These issues are fixable but are load-bearing for the paper's headline conclusions.

major comments (6)

[Appendix D.1 / Eq. (20)] The training split for the property regressor E(theta,p,t) is not specified: Eq. (20) trains on 'the molecules and their properties ... in CrossDocked dataset' without stating that p_data is restricted to the 100,000 training poses and excludes the 100 test proteins and their reference ligands. If the regressor saw test-pocket ligand complexes, the guidance gradients could steer toward test-reference-like solutions, and the reported Success Rate 51.3% and Vina Dock -9.05 would not demonstrate generalization. Please state the split explicitly and release MolJO-specific code (the current link points to the MolCRAFT repository) so this can be verified.
[Abstract, Sec. 5.2, Table 1] The headline 'more than 4x improvement in Success Rate compared to the gradient-based counterpart' compares MolJO (51.3%) with TAGMol (11.1%), but the two methods use different generative backbones (MolCRAFT vs TargetDiff). The within-backbone gain is 26.8% (MolCRAFT) to 51.3% (MolJO), i.e., 1.9x, so the 4x framing overstates the contribution of the guidance method itself. Please either restrict the claim to a same-backbone comparison or clearly state that the comparison is across different generative backbones.
[Sec. 4.1-4.2, Prop. 4.1, Algorithm 1] There is a mismatch between the derived guidance and the implemented guidance for the discrete modality. Prop. 4.1 defines g_yv = g_theta_v * d(theta_v)/d(y_v), but Sec. 4.2 states that 'We further bypass the derivative d(theta_v)/d(y_v) = theta_v(1-theta_v) to stabilize the gradient flow.' Algorithm 1 line 9 then samples y_v using Eq. 5 without the Jacobian term. Please clarify whether Eq. 5 is intended to include the chain-rule gradient or the bypassed gradient, and adjust the proposition or the algorithm accordingly.
[Sec. 5.2, Fig. 1B] The claim that MolJO is '2x as effective' in proposing 'me-better' candidates (39.8% vs below 17% for other baselines) is not backed by a table. Please define the exact criterion for 'all-better' (which properties must improve, and compared against which reference), report the ratio for every baseline, and provide confidence intervals.
[Sec. 5.3, Appendix D.2] The constrained optimization experiments are said to be 'achieved by infilling (details in Appendix D.2)', but Appendix D.2 only describes R-group and scaffold hopping task definitions and RDKit fragmentation; it does not explain how the masked or infilled regions are integrated into the guided BFN sampling of Algorithm 1. Please provide the algorithmic details (masking, conditional update of theta, and any changes to backward correction) needed to reproduce these results.
[Appendix D.1] No validation accuracy or gradient sanity check is reported for the learned property regressor E(theta,p,t). Since guidance scale 50 can drive samples outside the training distribution of theta, the first-order Taylor expansion in Prop. 4.1 may be inaccurate in those regions; please report held-out regression error (e.g., R^2 or MAE on a validation split of noised BFN beliefs) and, ideally, an analysis of how the surrogate gradient behaves under large guidance.

minor comments (5)

[Algorithm 1, line 8] The instruction 'Sample theta_x according to Eq. 4 and 13' is ambiguous; Eq. 4 is the guided one-step transition while Eq. 13 is the backward-corrected update. Please clarify how the two equations combine in the sampling loop.
[Eq. 4, Prop. 4.1, Eq. 13] The notation sigma_x is used inconsistently: in Eq. 4 and Prop. 4.1 it appears as a variance parameter, while Eq. 13 gives the variance as Delta_beta / rho_n^2. Please define sigma_x and sigma_v unambiguously in terms of the BFN schedules.
[Throughout] There are several typos and infelicities, including 'optimziation' in the Impact Statement, 'synthesizeability' in Sec. 1, 'utlizing' and 'unsatifactory' in Appendix G, and 'Cummulative' in the Fig. 9 caption. A careful proofread is recommended.
[Table 1 vs Table 6] The FLAG row reports Vina Score 45.85 in Table 1 but 45.978 in Table 6; please reconcile these numbers or explain the difference in evaluation.
[Appendix D.1] Please specify whether the property regressor uses the same atom featurization and kNN graph construction as TargetDiff, and how the noised BFN belief theta is featurized before being passed to that network.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found: the guided-sampling update is a standard product-of-experts Taylor expansion and headline metrics are computed by external Vina/RDKit oracles; score 2 reflects only the non-load-bearing reuse of the authors' MolCRAFT backbone.

full rationale

MolJO's core guidance law (Prop. 4.1, Eqs. 4-5) is derived in-paper from a first-order Taylor expansion of log p_E under the product-of-experts kernel, with the BFN transition forms taken from Graves et al. (2023); this is a standard classifier-guidance construction and does not define E in terms of the reported metrics. The backward-correction update (Eqs. 11-14) follows from the BFN additive-accuracy property and interpolates between the k=1 sampler of Graves et al. and the k=n sampler of the authors' own MolCRAFT (Qu et al. 2024); its benefit is tested empirically by ablations and t-tests rather than assumed. The headline results (Table 1) are computed with AutoDock Vina, RDKit QED/SA and the standard CrossDocked Success Rate threshold, i.e., with external oracles, not with the learned energy E, so no fitted parameter is renamed as a prediction. Reuse of the MolCRAFT pretrained checkpoint is a same-group citation, but MolCRAFT is an externally published ICML model and is itself listed as a baseline (row 9 vs row 14), so the joint-guidance contribution is independently checkable. Two non-circularity caveats remain: Appendix D.1 (Eq. 20) does not explicitly state whether the property-regressor training set is restricted to the 100,000 training poses or includes the 100 test proteins, and the code URL points to the MolCRAFT repository rather than to MolJO artifacts; these are reproducibility/leakage risks that would not, even if true, make the derivation circular by construction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The method rests on the BFN formalism and the pretrained MolCRAFT backbone, both from prior work (including the authors' own), plus a learned property regressor. The guidance scale and correction window are tuned on the benchmark. No new physical entities are introduced; the 'backward correction' is an algorithmic heuristic, not a new entity.

free parameters (3)

guidance scale s = 50
Selected by grid search over {0.1,1,10,20,50,100} on CrossDocked2020 (Table 13); scale 50 gives best optimization while keeping connected ratio acceptable; used in all main experiments.
backward correction window k = 130
Selected from ablation sweep over k in (0,200] (Fig. 8); robust in (50,200], final value 130; trades exploration vs exploitation.
sampling steps n = 200
Number of sampling steps during guided generation; set to 200 following prior BFN practice (MolCRAFT), not tuned in this paper.

assumptions (5)

domain assumption BFN posterior update rules (Eq. 6 and 8) as defined by Graves et al. (2023)
MolJO operates within the BFN framework; the correctness of the belief updates under guidance depends on these update rules being accurate for the generative process.
domain assumption Pretrained MolCRAFT backbone Phi is SE(3)-equivariant
Required for Prop 4.4; inherited from MolCRAFT (Qu et al., 2024), not proven in this paper.
domain assumption Energy function E is SE(3)-equivariant and differentiable
The guidance gradients and the equivariance proof assume this; the paper uses a TargetDiff-style GNN, which is designed to be equivariant.
standard math First-order Taylor expansion of E (Eq. 18) is a good approximation
Standard in classifier guidance; the paper adopts it without error analysis.
standard math Additive accuracy property of BFN updates (used in Eq. 11-12)
Used to combine accuracies over the sliding window; valid for the Gaussian/categorical BFN updates when the same clean estimate is used, but the substitution of the current estimate is an algorithmic heuristic.

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Cite this review

Pith. "Pith review of Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks." pith.science (2026). https://pith.science/paper/5ZJFWMGO

@misc{pith2026241113280,
  author       = {Pith},
  title        = {Pith review of: Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5ZJFWMGO}},
  note         = {Machine review of arXiv:2411.13280}
}

read the original abstract

Structure-Based molecule optimization (SBMO) aims to optimize molecules with both continuous coordinates and discrete types against protein targets. A promising direction is to exert gradient guidance on generative models given its remarkable success in images, but it is challenging to guide discrete data and risks inconsistencies between modalities. To this end, we leverage a continuous and differentiable space derived through Bayesian inference, presenting Molecule Joint Optimization (MolJO), the gradient-based SBMO framework that facilitates joint guidance signals across different modalities while preserving SE(3)-equivariance. We introduce a novel backward correction strategy that optimizes within a sliding window of the past histories, allowing for a seamless trade-off between explore-and-exploit during optimization. MolJO achieves state-of-the-art performance on CrossDocked2020 benchmark (Success Rate 51.3%, Vina Dock -9.05 and SA 0.78), more than 4x improvement in Success Rate compared to the gradient-based counterpart, and 2x "Me-Better" Ratio as much as 3D baselines. Furthermore, we extend MolJO to a wide range of optimization settings, including multi-objective optimization and challenging tasks in drug design such as R-group optimization and scaffold hopping, further underscoring its versatility. Code is available at https://github.com/AlgoMole/MolCRAFT.

Figures

Figures reproduced from arXiv: 2411.13280 by the authors.

**Figure 1.** Overview. A. Structure-based molecule optimization, including (1) guiding molecule design by expert-specified objectives, (2) optimizing existing compounds in the structure space. B. Study on the ratio of “me-better” molecules (with improved properties), where all other baselines fall short in the overall improvement. C. Overall illustration of MolJO, utilizing joint gradient signals over continuous-discrete data, w… view at source ↗

**Figure 2.** Gradient cosine similarity, where k denotes the backward correction window size. For 1 < k < 200, the similarity before timestep k is omitted for it overlaps with k = 200, i.e. covering all the past. unifies different sampling strategies proposed by Graves et al. (2023) (k=1) and Qu et al. (2024) (k=n). To understand its effect, we visualize the cosine similarity of gradients at each step w.r.t. the previous… view at source ↗

**Figure 3.** Visualization of the binding modes of the reference molecule (carbons in green) and the optimized molecule (in cyan) within the protein pocket (PDB ID: 2PC8, 2AZY, 1A2G, 2E24). The molecules and key residues (in blue) are shown in stick, while the protein’s main chain is drawn in cartoon (in gray). Dashed lines of various colors indicate different types of non-bonding interactions. Left: R-group optimization results… view at source ↗

Figures from the paper (13 more)

**Figure 4.** Figure 4: Distribution of molecular properties (QED, SA, Vina Score) over the number of atoms for CrossDocked2020. For each size, the mean and error bars are shown in the boxplot [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Distribution shift from test set (Ref), backbone without guidance (Gen) to guided MolJO (Opt). Affinity Analysis. We present the tail distribution of Vina affinities in [PITH_FULL_IMAGE:figures/full_fig_p017_5.png]

**Figure 6.** Figure 6: Non-covalent interaction distributions of reference and optimized molecules [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

**Figure 7.** Figure 7: Pairwise correlation of different properties. On the diagonal are histograms showing single property distributions on CrossDocked2020. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

**Figure 8.** Figure 8: Ablation study of backward correction. Correction Step on the x-axis means the length of history k, and w/o Correction means vanilla update (k = 1) with a Monte-Carlo estimate of y. Additionally, we have conducted pairwise t-tests comparing our guided Backward Correcti…

**Figure 9.** Figure 9: Cummulative density function (CDF) for strain energy distributions of generated molecules and reference molecules. H. Inference Time We report the time cost in [PITH_FULL_IMAGE:figures/full_fig_p023_9.png]

**Figure 10.** Figure 10: Box plot for clash distributions of generated molecules and reference molecules. Reference 0 2 4 6 8 10 12 14 RMSD (Å) AR Pocket2Mol FLAG TargetDiff DecompDiff IPDiff MolCRAFT DecompOpt TAGMol Ours 0 2 4 6 8 10 12 14 [PITH_FULL_IMAGE:figures/full_fig_p024_10.png]

**Figure 11.** Figure 11: Boxplot for RMSD distributions of generated molecules and reference molecules. Reference 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 SA AR Pocket2Mol FLAG TargetDiff DecompDiff IPDiff MolCRAFT DecompOpt TAGMol Ours 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 [PITH_FULL_IMAGE:figures…

**Figure 12.** Figure 12: Violin plot for SA distributions of generated molecules and reference molecules. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_12.png]

**Figure 13.** Figure 13: Violin plot for QED distributions of generated molecules and reference molecules. Reference 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0 Vina Score AR Pocket2Mol FLAG TargetDiff DecompDiff IPDiff MolCRAFT DecompOpt TAGMol Ours 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0 [PI…

**Figure 14.** Figure 14: Violin plot for Vina Score distributions of generated molecules and reference molecules. Reference 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0 Vina Min AR Pocket2Mol FLAG TargetDiff DecompDiff IPDiff MolCRAFT DecompOpt TAGMol Ours 20.0 17.5 15.0 12.5 10.0 7.5 5.0 2.5 0.0…

**Figure 15.** Figure 15: Violin plot for Vina Min distributions of generated molecules and reference molecules. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_15.png]

**Figure 16.** Figure 16: Violin plot for Vina Dock distributions of generated molecules and reference molecules [PITH_FULL_IMAGE:figures/full_fig_p026_16.png]

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me-better

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me-better

and QED, SA calculated by RDKit, we elaborate other metrics as follows: • Diversitymeasures the diversity of generated molecules for each binding site. Following SBDD convention (Luo et al., 2021), it is based on Tanimoto similarity over Morgan fingerprints, and averaged across 100 test proteins. •Connected Ratiois the ratio of complete molecules overall,...

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