Interpolation-Based Conditioning of Flow Matching Models for Bioisosteric Ligand Design
Authors: Yael Ziv
Martin Buttenschoen
Lukas Scheibelberger
Brian Marsden
Charlotte Deane
Venue: ICLR 2026 Poster (2026)
Keywords: drug discovery
3D molecule generation
bioisosteric fragment merging
conditional generation
flow matching
generative models
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Fast, unconditional 3D generative models can now produce high-quality molecules, but adapting them for specific design tasks often requires costly retraining. To address this, we introduce Interpolate-Integrate and Replacement Guidance, two training-free, inference-time conditioning strategies that provide control over E(3)-equivariant flow-matching models. 
Our methods generate bioisosteric 3D molecules by conditioning on seed ligands or fragment sets to preserve key determinants like shape and pharmacophore patterns, without requiring the original fragment atoms to be present. We demonstrate their effectiveness on three drug-relevant tasks: natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging.

Published as a conference paper at ICLR 2026
INTERPOLATION-BASER CONDITIONING OF FLOW MATCHING MODELS FOR BIOISOSTERIC LIGAND DE- SIGN
Yael Zivl., Martin Buttenschoen', Lukas Scheiblerger', BBrin Marsden Charlotte Deane
I Department of Statistics, University of Oxford, St Giles', OXI 3LB, Oxford, United Kingdom
2 Nuffield Department of Medicine, University of Oxford, Old Road, OX3 7BN, Oxford, United Kingdom
3 Centre for Medicines Discovery, Nuffield Department of Medicine, OX3 7FZ, Oxford, United Kingdom
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ABSTRACT
Fast, unconditional 3D generative models molecules, but adapting them for specific design tasks often requires costly re- training. To address this, introduce two training-free, inference-time con- ditioning strategies, Interpolate-Integrate and Replacement Guidance, that pro- vide control over E(3)-equivariant flow-matching models. Our methods generate bioisosteric 3D molecules by conditioning serve key determinants like shape and pharmacophore patterns, without requiring the original fragment atoms to be present. We demonstrate their effectiveness on three drug-relevant tasks: natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging.
produce high-quality
seed ligands or fragment sets to pre-
INTRODUCTION
In drug discovery, the design of effective compounds is guided by information about the biomolec- ular target. This information can direct, as in structure-based drug design (SBDD), where the target's ' 3D structure is known; or it can be indirect, as in ligand-based drug design (LBDD), where knowledge inferred from molecules already known bind the target. The difference can be il- lustrated with an analogy: SBDD is like having the blueprint for lock, while LBDD is like trying to make a new key by only studying existing keys. While SBDD is powerful, the protein struc- ture is often unknown, making LBDD a widely-used approach (Acharya et al., Shhamaa et al. 2021 Palazzesi Pozzan 2022 Zhung et al. 2024). This work focuses on the challenges of LBDD. The central goal in LBDD to preserve key interaction patterns (e.g.. shape or pharmacophores, specific 3D arrangement of interaction features) of a reference ligand while optimizing other prop- erties like synthetic accessibility. This is applied in tasks like natural product ligand hopping, where complex molecules are redesigned into simpler analogues (Morrison & Hergenrother, (014)). An- other application is fragment-based design, where information from multiple small, weakly-binding fragments is used to design a single, more potent molecule (Erlanson et al.] 2204). A key strategy bioisosteric fragment merging, which combines the interaction patterns from multiple fragments into a single new molecule (Wills et al. 2024). In this approach, the final product may discard the exact fragment atoms but must preserve their essential shape and pharmacophores.
Existing conditional generators that operate on ligand information alone involve specific trade-offs. For example, REINVENT (Olivecrona et al.) 2017) not 3D-nnttvv,, instead optimizing SMILES with reinforcement learning toward user-defined objectives. Others, such as SQUID and SHAPE- MOL, generate molecules in 3D but condition only on a reference molecule's s overall shape, not on more granular pharmacophore patterns (Adams & Coley 2023 Chen et al.) (202)). The state-of- the-art SHEPHERD model advances this by conditioning on a combination of shape, electrostatic potential (ESP), and pharmacophore grids (Adams et al.) 2025). However, its application multi- fragment conditioning requires a manual step of constructing the aggregate ESP and interaction profile of the fragments. Looking ahead, large-scale structural efforts such as the UK OpenBind

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Published as a conference paper at ICLR 2026
consortium aiming to produce >500,000 protein- ligand complexes wwill markedly expand frag- ment and pose libraries, highlighting the need for tools that can operate directly in this data-rich regime (OpenBind Consortium 2025).
To address this gap, we adapt a pre-trained, unconditional models for controllable, goal goal-directed tasks. Recent advances in fast, unconditional SE(3)EE())equivarinnn models provide realistic 3D molecule generators that support large-scale sampling (Buttenschoen et al. (022)). For molecule generation, there are already examples of this strategy (also called goal-directed
controllable generation), e.g. (Hoogeboom et al., 2022 Morehead & Cheng [024) Le et al. 2024 Ziv et al,,
2025]. By repurposing these powerful foundational models, we can avoid costly retraining and create flexible, on-the-fly fly conditioning mechanisms.
In this work, we introduce two modular, training- frre conditioning strategies for state-of-the-ar flw--matching model, enabling precise, ligand- ligand-based bioisosteric design at inference time. Our main contributions are:
Two complementary, inference-time conditioning methods. Interpolate-Interrute designed for controlled similarity seed structure and Replacement Guidance for bioisosteric merging with user-controlled degree of relaxation. Interpolate-Integraae is global simi- larity method without being constrained by specific local atoms, while Replacement Guid- ance is intended for "hard" conditioning where strict local preservation is required. A key feature of our framework is the ability automatically condition on a set of multiple fragments.
Both methods are training-frre and modular, operating pre-trained flow-matching model. This efficient approach avoids costly retraining. making the framework adaptable for various ligand-only design scenarios where protein structures unavailable.
We demonstrate the practical effectiveness of our framework across three challenging drug-relevant lacks-naturall product ligand hopping, bioisosteric fragment merging, and pharmacophore merging--rooduinng high yields of valid and synthetically accessible molecules that outperform specialized, state-of-the-art baselines.
Our code is available at: https: httpppps/gggthh....cooooro/oooo.
RELATED WORK
2.1 UNCONDITIONAL MOLECULAR GENERATION
The Equivariant Diffusion Model (EDM) established a foundational baseline for 3D molecular gen- eration by using an E(3)-equivariant network to jointly denoise atom types and coordinates (Hooge- boom al. (022). Since EDM, several unconditional 3D generators have advanced capacity and ficiency, including EQGAT-diff (Le et al. 2024), FlowMol (Dunn & Koes 2024), GCDM (Morehead & Cheng 2024), GeoLDM (Xu et al. (023), and SemlaFlow (Irwin et al. 2024). A recent head-to- head evaluation (Buttenschoen al. 2025) GEOM DEOM-Drugs reports that SemlaFlow achieves the best overall performance, with an 87% success rate for generating valid, unique, and novel molecules without post-processing (92.4% with post-processing), and is the fastest method compared.
2.2 TRAIN-TIME JOINT CONDITIONAL MODELS (3D-NATIVE AND GOAL-DIRECO DIREETED))
Ligand-bassed based conditional design has long used ID/2D generators guided by 3D-derived constraints (e.g., sitcsshapepphhne haappp//phammcohhorreee.. which still require post hoc conformer generation (Skalic et al.) 2019
Imrie al.) 202T). In contrast, 3D conditional models place atoms in space directly. Both SQUID
(Adams & Coley 2023) and SHAPEMOL. (Chen et al. 2023) condition generation on target 3D shape, typically represented as point cloud. SQUID couples an equivariant shape encoder with fragment-wiss autoregressive assembly, and ShapeMol uses an equivariant shape encoder with conditional diffusion decoder. Closest to our task, ShEPhERD learns a joint diffusion over molecules and interaction grids (shape, ESP, pharmacophores) on 1.6 M MOSES (Polykovskiy et al., 2020) structures (Adams et al. 2025). Despite its capabilities, ShEPhERD must be retrained for every new conditioning channel and still requires manual grid construction. As non-joint alternative, REIN-
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VENT is non-3D-natin method that optimizes SMILES policy with reinforcement learning,
requiring post hoc conformer generation (Olivecrona et al. 2017).
2.3 BIOISOSTERIC DESIGN AND FRAGMENT-LEVEI OPTIMIZATION.
A complementary line of research tackles bioisosteric replacement from copperty-ottimiziin perspective. Many molecular optimization methods formulate replacement as an editing task on 2D molecular representations, such as SMILES (sequence) or molecular graphs (graph-editing) (He) et al. 2021; 2022 Yang et al., 2023: Jin et al., 2019; (2220). Within this category. models such as Chen et (2021) and DeepBioisostere (Kim al. 2024) learn fragment substitutions that preserve biological function while explicitly optimizing scalar physicochemical properties (c.g., logP, TPSA, potency). All of these approaches require task-specific retraining to capture the desired property or
substitution patterns.
2.4 INFERENCE-TIME CONDITIONING AND EDITING
Inference- conditioning allows users to steer a pretrained generator without additional training. SBDD controls parallel diffusion- inpainting and editing methods developed for images and point clouds (Lugmayr et al. 2022 Meng et al. 2022 Zeng et al. 2022). In SBDD, this is often ap- plied to generate molecules directly in the protein pocket; examples include DiffSBD (Schneuing SE(3) diffuser, while PILOT (Cremer et 2024) adds importance-sampling guidance on top of a large-scale pretrained model to meet multiple objectives. SILVR (Runcie & Mey 2023) refines latent trajectories to satisfy pocket or fragment constraints, and MolSnapper (Ziv al. (2255) projects explicit pharmacophore points (e.g.. donors, acceptors) into the diffusion process. FLOWR (Cremer et al.) (2025) uses equiv- ariant flow matching as faster alternative to diffusion for pocket-conditioned ligand generation, and additionally introduces multi-task model trained on inpainting tasks for fragment- based sampling. Fragment merging/linking provides closely related setting: DiffLinker (Igashov al., (0244) treats the linker as a 3D-conditioned diffusion sub-problem and reports higher validity and lower RMSD than inpainting: LinkerNet co-designs linker geometry and poses jointly (Guan et al. 2023). To
et al. 2024) that apply inpainting and context injection to a pocket-conditione
speed up inference, TurboHopp replaces the diffusion backbone with consistency model, enabling pocket-conditioned scaffold hopping (Yoo et al. 2024).
2.5 POSITIONING OF THIS WORK.
Our work introduces Interpolate-Interree and Replacement Guidance, two ligand-only, inference- time conditioning methods for bioisosteric design, where the goal is to generate a ligand that pre- serves the key binding determinants of reference molecule, such as overall shape and pharma- cophoric interactions, without need to retain the original atoms. As training-free framework on a fast flow-matching backbone, our approach avoids the costly retraining required by many condi- tional models. It provides modular, fine-grained control directly at sampling while preserving the high inference speed of the base model. This allows for the efficient generation of chemically diverse molecules that maintain key pharmacophoric and shape-based interactions.
METHODS: CONDITIONING FLOW MATCHING FOR MOLECULAR GENERATION
We propose two conditioning mechanisms for fast, controlled 3D molecular generation on top of SemlaFlow (Irwin et al., (2024). an Eqqivvrann floc-matching model that jointly generates co- ordinates and discrete chemistry. We first summarize conditional flow matching (CFM) (Lipman) jet al. 2022 Campbell et al. 2024) and SemlaFlow's 's path and solver in particular. Finally. we Formulate the interpolate-integrate and replacement guidance, our two conditioning methods.
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3.1 BACKGROUND
3.1.1 CONDITIONAL FLOW MATCHING (CFM)
Let = (z. ar..a,bor denote a molecule with atomic coordinates a Nx3 atom types E AA,, bond types b BNNN, and formal charges с E CN. CFM trains a neural vector field ey (t, 2) whose flow @r pushes a simple prior Po to data distribution pi by regressing to target conditional vector field (z(z / 21) defined along a per-example probability path (+(= 21) connecting noise a specific data point 21. The learning objective is the conditional regression
LCFM E
(t 24) ue [ 21)1
#1 Paatta (-(aa
which yields the same optimum regressing the (intractable) marginal vector field on the marginal path Pt obtained by integrating out 21. This conditional formulation simulation-fres and admits many paths, including diffusion and optimal-transport (OT) Gaussian paths.
3.1.2 SEMLAFLOW INSTANTIATION FOR MOLECULES
Joint variables and paths. SemlaFlow models the joint p(z) with: (i) continuous OT-like inter- polation for coordinates with small Gaussian jitter, and (ii) discrete conditional paths for and bond types; charges are predicted but do not participate in the flow. Concretely, at training time
2(11 + (1 - 1) xo: I²1),
L~ Bela (),
0r ~ Catl Ca ~C~Cal(
(1- [AT 14 + 16n.)
by ~ Call
Car((1 (1
[ 1s + 1.bb.)
after first aligning (Ig, r1) by an equivariant OT alignment f= (permutation rotation/reflection) (Klein & Noë 2024 Song et al., (023) that minimizes squared transport cost. SemlaFlow trains predictor po(e 1 2t) of clean data (rather than vector field directly), and uses it to reconstruct the vector field needed by the ODE sampler. The sampler is Euler with 100 steps and log-spaced. Here A and B are the finite sets of atom and bond types, respectively. 1 RIAI and 1 ||55| denote all-ones vectors (so [ 1A and [ 1B are the uniform distributions on A and B, respectively), and 8 denotes the Kronecker delta. δag ōb, denote one-hot point masses at and b,
With the predictor (0 f,,,,, denote 2 E[21 coordinates the conditional vector field implied by the linear path is
SemlaFlow sampling ODE.
t, 2e) For
Ø1 Zz
(x
x1)
Euler step: L+ It (ī) (i)-
Following SemlaFlow and the Discrete Flow Models (DFM) framework Cratework-cutegorrcaal interpolation at train time and Continuous Time Markov Chain (CTMC) sampling at test time- tiee-hhe discrete vari- ables are evolved via
ac-Ai ~ Cat(6o, + At R4 (ot.
bL+ ~ Cat( &b, + Rob,,)))
(1)
(i.5) = Bos-po(aslt.ez) 81,0 (1 - 0(,0))
for atoms, and analogously with dj for bonds. (2)
Intuitively, mass flows only toward the denoised label, with strength that increases t 1.
3.2 OUR CONDITIONING MECHANISMS
We now introduce two inference-time conditioning procedures. Both build on SemlaFlow's solver.
Notation.
Fragment mask M E 1) indicates atoms belonging to seeding fragments;
I-M.
* For any tensor y indexed by atoms (or atom pairs), write y/ [M] and M|M] for the masked and unmasked components respectively, and Replace y²) for replacing y[M] < [M].
Let 0 < 4 <ik be the integration grid (log-spaced in SemlaFlow).
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3.2.1 INTERPOLATE IHEERPOLAT--INTGRRAEE (SEED-GUIDES RESAMPLING)
Goal. Given reference input 21 Pdatas produce sample ž that is controllably similar while
allowing global edits.
Key idea. Start in the middle of the probability path at time [0, 1]: first interpolate from the seed toward noise to obtain 2r. then integrate the ODE forward from = T to 1. Large T yields decorrelates
conservative edits; small yields diverse rewrites. Optional noise injection geometry without discarding the seed entirely.
Construction.
Let 2u po and write the SemlaFlow path for t = x+ = r 21 Cat( r da, + (1
7) zo C,
~~, (optional extra jitter);
by Car( r 8b, + +1 r) 1 [8) 1B)
From (t) 26) (T, 2,), integrate the SemlaFlow ODE using &1 E[21 2e] at each step: A/k
Ftzt1 2ta +
( (tks =tx)
Ttae
k to E [T,
Discrete update. Apply the CTMC Euler step (Eqs, equation atst1 ~ Cath 6a, + R2, (at.:.)
e - Cat doee + Atg Re, (be..:)), k to E [T,
2):
beats
Effect. For + 21 matches up to small edits; for T + O, sampling reduces to unconditional generation. This 'mid-path restart' is conceptually related editing techniques in diffusion mod- els that manipulate the reverse sampling process from an intermediate timestep Zeng et al. (2022). However, our application to the deterministic ODE trajectory of a flow-matching model is distinet. To our knowledge, this is the first such cearpollat-intterrae conditioning scheme for flow-matching model, offering a computationally lightweight yet powerful method for controlled generation. Sem- laFlow provides OT-like linear path and the ODE needed do so efficiently.
3.2.2 REPLACEMENT GUIDANCE (BIOISOSTERIC FRAGMENT MERGING)
Goal. Use set of fragments to generate ligands that do not necessarily contain the exact fragment atoms but still preserve key binding interactions. As a complementary approach the global, soft conditioning of Interpolate-nnterrute,, Replacement Guidance provides hard, localized control over defined set of fragments, allowing for the generation of unconstrained parts of the molecule to merge or complete them.
Key idea. During ODE integration, after each Euler update, replace the masked fragment gion with the original, unperturbed fragment values. This "hard anchoring" maintains exact spatial and chemical identity of fragments throughout the trajectory, while leaving the complement M free to evolve. Relaxation of the constraint is user-controlled: starting from chosen time frelax E [0, 1] (with the convention that the final step is always free), replacement is disabled for all subsequent steps. More generally, one may specify a relaxation set Trren C {4x } of steps at which replace- ment is skipped; the default is Trree = {Ix]-
Masked ODE with replacement. Let Ztrag denote the original fragments to preserve. For each step& t + At with + At & Theee (e.g., t At < trelax),
1- i8 (1, 24) .1).
Al
Euler proposal:
T = It +
(ii) Replacement:
T+t = Replace p.prore: &trag);
and analogously
0++i: = Replace, (ap-op: afagg) bit = Replace (proop brrag),
This yields projected flow that exactly preserves the fragments up relaxation time(s) while giving the model freedom to synthesize linkers and local substitutions thereafter, capturing the essence of bioisosteric merges. The novel components of this method are the introduction of the replacement operator and the user-controlled relaxation policy, which together adapt the pre-trained flow for bioisosteric merging.
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3.2.3 INFERENCE RUNTIME.
All conditioning operates strictly at inference and preserves the original 100-step Euler integration schedule from SemlaFlow. (i) Interpolate-hnterrite introduces one-time O(N) seed interpolation prior to integration. (ii) Replacement Guidance adds only a lightweight (N) masked tensor update per step, negligible relative to model forward pass. Overall, both methods maintain inference efficiency comparable to the unconditioned bascline.
4 EXPERIMENTS
We demonstrate the effectiveness of our conditioning strategies on three challenging, drug-relevant tasks: natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging. Figure [.. providees visual overview of the conditioning setups and showcases representative top- ranked molecules for each task, selected by medicinal chemist after filtering. We evaluate all generated molecules on a suite of metrics designed to assess their validity, synthetic accessibility, and 3D similarity to reference compounds.
4.1 EVALUATION METRICS
We evaluate generated molecules synthetic accessibility (SA) using the score from Ertl & Schuf- fenhauer (2009), where lower values are better. For validity, molecules must be parsable by RDKit and pass PoseBusters plausibility checks (Buttenschoen al. 2023). We combine these and report the percentage of initial samples that pass both validity and SA score filters. We also compute 3D similarity to reference molecule by comparing surface, electrostatic, and pharmacophore features, following the protocol of AAdams et al.] (2025). Finally, we calculate an AutoDock Vina docking score (Eberhardt et al.. (02T), using energy minimization for conditionally-generrteed poses to sure robustness (Weller& & Rohs 2024).
4.2 NATURAL PRODUCT LIGAND HOOPPING
Natural products are complex and diverse substances produced by organisms such as plants, mi- crobes, and marine life. Their rich structural variety and biological relevance make them valuable starting points for small molecule drug discovery (Morrison & Hergenrother| (014). However, their inherent complexity and limited synthetic tractability often hinder the development of analogues and optimization for drug-like properties (Lee & Schneider (2001). Ligand hopping offers a strategy for generating novel compounds that retain the biological activity of known natural products while exploring more synthetically accessible chemical space.
We follow the SHEPHERD framework (Adams et al. 2025) using three structurally diverse natural products (NP1, NP2, NP3) from the COCONUT database (Sorokina et al. 2021), aiming generate analogues with SA scores below 4.5. We generated 2,500 candidate analogues with both Interpo- late-Int and Replacement Guidance, conditioning key pharmacophoric features (Hydrogen Bond Donors, Hydrogen Bond Acceptors, and Aromatic Rings) extracted from each reference NP using the RDKit FeatureFactory. We compare our results against baselines: MolSnapper, in ligand-only mode, conditioned on the NP's pharmacophore points, and SHEPHERD, conditioned on its ESP and pharmacophore profiles. Further details on the generation parameters and post- processing are available in Appendix E
Our results (Table I) demonstrate that our two conditioning strategies have distinct strengths, mak- ing them suitable for different design goals. Replacement -iiteemmmn--giddnceeee consistently generates molecules with the most favorable SA scores across all three targets. contrast, pnerpolate-integreen fective for high-fidelity generation. While its yield of SA-compliant molecules is lower in this task, the candidates produces tend to show high similarity to the reference seed in both ESP and phar- macophore space. Figure 2 provides visual summary of these performance trade-offs, showing the relationship between surface and pharmacophore similarity. As the figure highlights, Interpolate- Integrate generates analogues with high 3D similarity the reference. This focus conservative edits, reflected in its lower overall diversity, suggests its utility as a tool for scenarios where pre- serving precise interaction patterns the primary objective. The SNAPPER baseline struggled
is ef-

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Natural product figand hopping
fragment merging
Notural Product
64
Pharmacophore
Restommet Cataree
0
32.614 34611
3723
1A,06
22
Overview of the three drug-relevant tasks, with top-ranked molecules selected by medicinal chemist from our conditioning strategies Interpolate-Integrate and Replacement Guid- Left: The natural product ligand hopping task. The goal is generate analogues withm- lower SA scores while preserving the key features of the original natural product (leftmost column). Right (Top): The bioisosteric fragment merging task is demonstrated on EV-D68 3C protease using three different conditioning setups (multi-fragment, random atom seeding, and manual extraction) generate single coherent molecule from multiple inputs. All molecules shown are filtered for SA < 4.0. Right (Bottom): The pharmacophore merging task on erating a novel ligand that combines the interaction patterns from a diverse set of known fragments. All molecules shown are filtered for SA < 4.5. Created in https:/BioRender.com.
Figure 1:
Mgro. involves gen-
Table 1: Performance summary for the NP hopping tasks. The table reports overall PoseBusters validity, the average SA score, the percentage initial samples passing both validity and an SA score 4.5 filter, and top-10 similarity scores (ESP and pharmacophore) are calculated only these successful candidates.
NP Method
Valid f
SA Score 1
Valid (SA 44.5)
ESP sim (top 10)
Pharma sim (top 10)
MolSnapper
26.24%
6.43 : 0.78
0.32%
+ 0.02
0.16 + 0.02
ShEPhERD Interpolate-inteen
57.3%
4.75
0.89
24.7%
0.63 1 0.02
0.34 0.03
71.5%
1.67 + 1.02
35.6%
0.81 1 0.02
0.49 1: 0.06
Replacement-guiiee guidance
60.5%
3.74 1.01
50.2%
0.81 + 0.02
0.52 + 0.06
NP2 MolSnapper
19.88%
6.84 + 0.79
0.16%
0.63 +: 0.02
0.19 + 0.01
ShEPhERD
51.0%
5.09
17.4%
0.79 + 0.01
0.34 4 0.02
Interpolate-intees integratt
93.6%
5.41 1.03
16.0%
0.86
0.46
Replacement guidancce
87.9%
4.43 :: 1.31
48.8 %
0.85 1: 0.01
0.44 1 0.04
NP3 MolSnapper
14.20%
6.32 0.85
0.32%
0.46
0.14
ShEPHERD
44,1%
1,69 + 0.96
20.2%
0.60 +: 0.01
0.30 4 0.02
Interpolate-inteegn
62.7%
5.92 1.03
5.2%
0.56 + 0.05
0.20 + 0.03
Replacceeen-guuiden
64.4%
4.07
42.1%
0.29
on this task; the mechanism of conditioning on discrete points proved overly restrictive for dense pharmacophore profiles that had not been manually filtered (see Appendix

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(2u4 Rouiio of 10007-5Siia
Delabbuiin CST Cinautty
Ul of ESI Chrilerfyy
NPO
NP1
NP2
Figure 2: ESP similarity vs. pharmacophore similarity for generated analogues (filtered by
SA < 4.5) across the three natural product tasks.
4.3 BIOISOSTERIC FRAGMENT MERGING.
Fragment- based screening identifies small molecules that bind to distinct regions of a target protein. Traditionally, these fragment hits are grown or linked to form larger ligands that retain the original fragments explicitly. However, recent work by (Wills et al., 2024) introduced a bioisosteric merging strategy, which aims to generate ligands that do not necessarily contain the exact fragment atoms while preserving key binding interactions. This approach enables greater chemical diversity while maintaining pharmacophoric fidelity.
Building on this framework, we perform bioisosteric merging on the EV-D68 3C protease sys- tem, using data from the Fragalysis platform[] Our input consists of 13 experimentally resolved fragment- structures, previously curated for the SHEPHERD study (Adams et al. (025). We evaluate our methods across the following three conditioning setups (shown in Figure 4.1). which are designed to assess performance in both automated and expert-driven scenarios: Multi-fragment Reference (Automated), where composite input is formed by combining three known fragment- bound ligands; Random Atom Seeding (Automated): A sparse set of pharmacophore features is randomly sampled from the full fragment ensemble. This provides an automated way to leverage collective information from the raw fragment data without the need for manual expert curation; and Full Interaction Profile (Manually Curated), a comprehensive 27-feature set curated for the SHEPHERD study that serves an ideal but labor-intensive baseline (Adams et al. 2025).
Our goal is to assess how effectively the automated conditioning strategies can approach the perfor- mance of the manually curated profile, which represents a key challenge for real-world application.
Following the SHEPHERD study protocol, we generated 1,000 candidates per setup and applied similar post-processing pipeline (see Appendix F.I for full details). Since the input fragment poses contain implicit information about the target's 's binding pocket, a primary goal was to assess in-pocket fit. To do this, we filtered candidates for PoseBusters validity and favorable SA score (SA < 4.0, as used in SHEPHERD study). Passing candidates were then assessed using AutoDock Vina. We compare against protein-aware and protein-blinn blind versions of MolSnapper, DifiSBDD, and SHEPHERD. A direct comparison across all methods is only possible on the manually curated 'Full Interaction Profile', the baseline methods require a single, well-defined conditioning profile. Detailed setups for all methods and an analysis of unfiltered results can be found in Appendix F3
A comparison of the three conditioning setups in Table reveals the trade-offs between automated and manually curated approaches. While the manually curated 'Full Interaction Profile was most effective at generating candidates with the highest predicted binding affinity (top-10 Vina score of -6.62), 'Multi-raagment reference' setup produced the highest overall rate of valid candidates with SA14 2..0%). The 'Random alom seeding', : representing a more challenging scenario, resulted in a lower yield but still produced potent candidates 10 Vina score of -5.78). This highlights the robustness of our framework, demonstrating its ability to generate high-quality candidates across a range of conditioning scenarios from fully automated to expert-curated ( ('Full Interaction Profile").
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Table 2: Performance summary for the EV-D668 3C bioisosteric merging task across three condition- ing setups. The table reports overall PoseBusters validity, the average SA score, the percentage of initial samples passing both validity and an SA score filter, the fraction of successful molecules with zero clashes (No clash), and the top-10 10 Vina docking score.
Conditioning seiup
Method
Valid
Score
(SA<4)
No f
Vina top
Multi frugment ref.
Interpolate integrate
59.3%
4.65 :: 0.96
14.8%
12.1%
0.49 -5.59
-5.30
Replacement-guieee guidance
25.0%
Random atom seeding
ineeppllat--ittenne
41.0%
5.18 + 1.17
6.8%
5.3%
-4.73
0.24
Replacement guidancee
28.1%
3.84 1 0.81
17.0%
13.5%
-5.78
0.26
Full interaction prolile
Interpolate- integraate Replacement-guidane
4.83
1.17
-4.84
31.9%
3.47 + 0.70
23.5%
18.7%
-6.62
- 0.42
Table 3: Performance summary for the EV-D68 3C bioisosteric merging task (ShEPhERD profile setup). The table reports overall PoseBusters validity, the average SA score, the percentage of initial samples passing both validity and an SA score < 4 filter, the fraction successful molecules with zero clashes (No clash), and the top-10 10 Vina docking score.
Method
Valid
SA Score
Valid (SA
No clash
Pharma sim top10
Vina top
MolSnapper (liand-based)
17.6%
7.01 = 0.51
0%
MolSnapper (clash-rate 0.1)
14.5%
6.91 = 0.53
0%
DifsSBD (largest Irag)
43.1%
6.91 0.53
0%%
ShFPhERD
33.8%
4.45 0.99
10.8%
10.1%
0.27 + 0.02
-6.16 + 0.50
Interpolate-imteerate
39.5%
1.96 = 1.17
8.7%
7.0%
0.18
-4.4 + 0.58
Replacemeat-guiddae
31.9%
3.47 + 0.70
23.5%
18.7%
0.22 = 0.02
-6.62 + 0.42
The results highlight that the conditioning mechanisms of DiftSBDD and MolSnapper-which are designed to strictly satisfy a of discrete constraints-are less suited for this flexible bioisosteric merging task. Satisfying constraints while maintaining drug-like drug-likeness proved challenging: neither baseline produced candidates passing the combined validity, connectivity, and SA score (< filter. Further analysis is provided in Appendix F Our results demonstrate that Replacement Guidance achieves performance competitive with state-of-the-art SHEPHERD model on the bioisosteric merging task. While SHEPHERD achieves higher pharmacophore similarity (0.27 vs. 0.22), our method produces candidates with superior synthetic accessibility (3.47 vs. 4.45) and a comparable top-10 Vina score (-6.62 vs. -6.16).
4.4 PHARMACOPHORE. MERGINN ON SARS-CoV-2 MPRO
Fragment screening the ---2 main protease (Maro) provides real-world test case. The Diamond/XChem campaign rapidly yielded a dense constellation of binding motifs, explicitly en- couraging the merging of these fragment hits into more potent ligand From public Fragalysis data, we curated a conditioning set of 81 small fragments and a baseline set of 1,426 known binders, with the goal of merging pharmacophoric motifs from the fragments into novel ligands.
We generated 2,500 candidate molecules per method, using the 'random atom seeding' setup (Sec- tion 4.3). To identify promising candidates, filtered all molecules for PoseBusters validity and favorable SA score before evaluating them against baseline binders with AutoDock Vina (see Ap- pendix G for the detailed setup). While we acknowledge the limitations of this high-throughput pipeline, such as docking to single rigid receptor, it serves as a standard computational funnel to promising candidates from large generative run.
identify a manageable
The results, summarized in Table 4 demonstrate that our methods can successfully merge pharma- cophoric motifs from a large set small fragments into novel, synthetically feasible ligands. In addition, we performed an interaction-recove analysis to verify that the generated molecules re- produce the key ligand interaction patterns encoded in the conditioning data. Using ProLIF
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fingerprints (Bouysset & Fiorucci 2021). we found that both Interpolate-Integraat and Replace- ment Guidance recover the full set of interaction types observed in the 81 conditioning fragments, after applying PoseBusters filtering and docking (see Appendix G for details). Together, these results indicate that our generated molecules not only satisfy geometric and physicochemical criteria but also explore the desired interaction space, making them competitive starting points for drug discovery campaign.
Performance summary for the SARS-CoV-2 Mpro pharmacophore-merea meriing task. The table reports overall PoseBusters validity, the average SA score, the percentage of initial samples passing both validity and an SA score filter, the fraction of successful molecules with zero clashes (No clash), and the top- 10 Vina docking score.
Table 4:
Target
Method
Valid
SA Score
Valid (SA<4.55)
No clash
Vina top
SARS-CoV-2 Mps
Interpolate-
41.0%
4.49 + 1.20
23.5%
15.4%
-6.48 0.24
Ccplaccment-guiddaae
32.2%
3.61 + 0.90
27.3%
12.4%
7.663 + 0.42
Known Binders
100%
3.70 1 0.97
80.1%
67.5%
99.5 = 0.07
4.5 INFERENCE RUNTIME.
On a single RTX 6000 GPU, generating a batch of 10 takes 2.85 with Interpolate- (T 0.75) and 3.9s with Replacement Guidance, measured on 50-atom molecules. CPU runtimes are 2 and 38.5 per sample (batch size 10). Batching improves efficiency but currently supports only identical input seeds per batch. In contrast, SHEPHERD requires approximately 3-4 minutes to generate batch of 10 samples with 400 denoising steps single V100 GPU, or 5-10 minutes per sample on CPU (Adams al. 2025).
4.6 VALIDATION OF CONDITIONING MECHANISMS
We performed ablation studies to validate the design principles of our conditioning strategies.
Replacement Guidance Inpainting. Standard diffusion inpainting replaces masked regions with noised version of the target fragment, keeping the imput locally in-distribution (Lugmayr et al. 2022). Table 13 compares this approach to our clean hard-replacement strategy on the DIFFLINKER set (Igashov et al., 2024), which includes cases with three or more disconnected fragments. Re- placement Guidance achieves higher overall validity (58.6% vs. 32.0%) and better similarity than fully-noised inpainting. Further analysis is in Appendix B.2
Behaviour of Interpolate-ntterrate on disconnected seed structures. We evaluate whether Eaerpolaae-Integraat
merge disconnected inputs into single valid molecule. As shown in Appendix B.T the method can "heal" disconnected seeds from the DIFFLINKER test set (Igashov al. 2024). Table [0 shows the relationship between the interpolation time and validity: decreases (i.e., more noise is injected), the validity increases from 65.3% to 84.4%.
5 CONCLUSION
We have introduced two Iraining-free, inference-time conditioning strategies-Inttrpolee Integrate and Replacement Guidance- operate on top of the fast SemlaFlow model for ligand- only. bioisosteric design. Both methods can steer 3D molecular generation to satisfy interaction- level constraints such as shape and pharmacophore overlap. We demonstrate their effectiveness across three tasks: natural product ligand hopping, bioisosteric fragment merging, and pharma- cophore merging, showing that they generate synthetically accessible molecules that match key reference features. Unlike prior methods that rely explicit retraining task-specific encoders, our framework preserves sampling efficiency and generalizes across tasks and conditioning modes. These results establish inference-time conditioning directed molecular design, whose modularity makes it readily extendable to new scientific domains such protein design and materials discovery.
powerful and scalable strategy for goal-
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ACKNOWLEDGEMENTS
We thank the authors of the SHEPHERD paper for their support, assistance with the experimental setups, and for guidance on code usage. The authors also thank Leo Klarner and Lucy Vost from the Oxford Protein Informatics Group for their helpful comments. We additionally thank Nele Quast, Alona Jurgenson, and Fergus Imrie for their valuable feedback during the rebuttal period. This work was supported by the Centre of Artificial Intelligence in Precision Medicines (CAIPM), King Abdulaziz University, Jeddah, Saudi Arabia.
REFERENCES
Chayan Acharya, Andrew Coop. James E. Polli, and Alexander D. MacKerell. Recent Advances higand-Based Based Drug Design: Relevance and Utility of the Conformationally Sampled Phar- macophore Approach. Current Computer-Aided Drug Design 7(1):10-2. doi: 10.2174/
in Ligand-
157340911793743544 URL https::t///// pettp::/www.eureet www.w/wwwww..ee..eeeeeemm www.eet..co.aaaeeee wttp://ot/t/tte//eeeeeee
Keir Adams and Connor W. Coley. Equivariant shape-conditioned generation of 3D molecules for ligand-based based drug design. In The Eleventh International Conference on Learning Representations, 2023.
Keir Adams, Kento Abeywardane, Jenna C Fromer, and Connor W Coley. Shepherd: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design. In ICLR, 2025.
Simon Axelrod and Rafael K6mez-Bombarelie GEOM, energy-annotated molecular conformations for property prediction and molecular generation. Scientific Data, 1(1):185, 2022.
Cédric Bouysset and Sébastien Fiorueci. Prolif: library to encode molecular interactions finger- prints. Journal of cheminformutics, 13(1):72, 2021.
Martin Buttenschoen, Garrett M. Morris, and Charlotte M. Deane. PoseBusters: Al-based dock- ing methods fail to generate physically valid poses or generalise to novel sequences. Chemical Science, 2023.
Martin Buttenschoen, Yael Ziv, Garrett M Morris, and Charlotte M Deane. An evaluation of uncon- ditional 3d molecular generation methods. arXiv preprint arXiv:2505.003: 2025.
Andrew Camphell, Jason Yim, Regina Barzilay, Tom Rainforth, and Tommi Jaakkola. Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design. arXiv preprint arXiv:2402.04990 2024.
Ziqi Chen, Martin Renqiang Min, Srinivasan Parthasarathy, and Xia Ning. A deep generative model for molecule optimization via one fragment modification. Nature machine intelligence, 3(12): 1040-1049, 2021.
Zigi Chen, Bo Peng, Srinivasan Parthasarathy, and Xia Ning. Shape-conditioned 3D molecule gen- eration via equivariant diffusion models. In NeurIPS 2023 Generative Al and Biology (GenBio) Workshop, 2023.
Connor W Coley, Luke Rogers, William H Green, and Klavs F Jensen. Scscore: synthetic com- plexity learned from a reaction corpus. Journal of Chemical Information and Modeling, 58(2): 252-200 261, 2018.
Julian Cremer, Tuan Le, Frank Noé, Djork-Amé Clevert, and Kristof T Schütt. Pilot: equivariant diffusion for pocket-conditioned de novo ligand generation with multi-objective guidance via 2024.
importance sampling. Chemical Science, 1415)):44544-443
Julian Cremer, Ross Irwin, Alessandro Tibo, Jon Paul Janet, Simon Olsson, and Djork-Amé Clev- ert. Flowr: Flow matching for structure-aware de novo, interaction-and fragment- ligand generation. arXiv preprint arXiv:2504. 10564, 2025.
Ian Dunn and David Ryan Koes. Mixed continuous and categorical flow matching for 3D novo molecule generation, 2024.

---

Published as a conference paper at ICLR 2026
Jerome Eberhardt, Diogo Santos-Martins, Andreas F Tillack, and Stefano Forli. Autodock vina 1.2. O: new docking methods, expanded force field, and python bindings. Journal of chemical information and modeling, 61(8))3891-3888, 2021.
Daniel A Erlanson, Robert S McDowell, and Tom O' O'Brien. Fragment-based drug discovery. Journal of medicinal chemistry, (1144)3443-3482, 2004.
David Errington, Constantin Schneider, Cédric Bouysset, and Frédéric Dreyer. Assessing interac- tion recovery of predicted protein-ligand poses: D. errington et al. Journal of Cheminformatics, 17(1):76, 2025.
Peter Ertl and Ansgar Schuffenhauer. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminfor- matics, 1(1):8, 2009.
Jiaqi Guan, Xingang Peng, PeiQi Jiang, Yunan Luo, Jian Peng, and Jianzhu Ma. Linkernet: Frag- ment poses and linker co-design with 3d equivariant diffusion. Advances in Neural Information Processing Systems, 36:77503-77519 2023.
Jiazhen He, Huifang You, Emil Sandström, Eva Nittinger, Esben Jannik Bjerrum, Christian Tyrchan, Werngard Czechtizky, and Ola Engkvist. Molecular optimization by capturing chemist's 's intuition using deep neural networks. Journal of cheminformatics, 13(1):26, 2021.
Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, and Ola Engkvist. Transformer-based molecular optimization beyond matched molecu- lar pairs. Journal of cheminformatics, 14(1):18, 2022.
Emiel Hoogeboom, Victor Garcia Satorras, Clément Vignac, and Max Welling. Equivariant diffu- sion for molecule generation in 3d. In International conference on machine learning, pp. 8867- 8887. PMLR, 2022.
Ilia Igashov, Hannes Stärk, Clément Vignac, Arne Schneuing, Victor Gareia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, and Bruno Correia. Equivariant 3d-conditional diffu- sion model for molecular linker design. Nature Machine Intelligence, 6(4):417-427, 2024.
Fergus Imrie, Thomas Hadfield, Anthony R Bradley, and Charlotte M Deane. Deep generative design with 3d pharmacophoric constraints. Chemical science, 12445):4457-114589, 2021.
Ross Irwin, Alessandro Tibo, Jon Paul Janet, and Simon Olsson. Semlaflow-effiiiet 3d molecular generation with latent attention and equivariant flow matching. arXiv preprint 0rtp: 2024.
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Hicrarchical traph-to-grapl translation for 2019.
molecules. arXiv preprint
Wengong Jin, Regina Barzilay, and Tommi Jaakkola. Hierarchical generation of molecular graphs using structural motifs. In International conference on machine learning, pp. 4839-4848, PMLR, 2020.
Hyeongwoo Kim, Seokhyun Moon, Wonho Zhung, Jaechang Lim, and Woo Youn Kim. Deep- bioisostere: Discovering bioisosteres with deep learning for fine control of multiple molecular properties. arXiv preprint arXiv:2403.02706, 2024.
Leon Klein and Frank Noé. Transferable boltzmann generators. Advances in Neural Information Processing Systems, 37:45281-4531 2024.
Gregory A Landrum, Julie E Penzotti, and Santosh Putta. Feature-map vectors: a new class of informative descriptors for computational drug discovery. Journal of Computer-Aided Molecular Design, 20:751-762, 2006.
Tuan Le, Julian Cremer. Frank Noe, Djork-Amé Clevert, and Kristof T Schütt. Navigating the design space of equivariant diffusion- based generative models for de novo 3D molecule generation. In The Twelfth International Conference Learning Representations, 2024.
12

---

Published as a conference paper at ICLR 2026
Man-Ling Lee and Gisbert Schneider. Scaffold architecture and pharmacophoric properties of nat- ural products and trade drugs: application in the design of natural product-based combinatorial libraries. Journal of combinatorial chemistry, 3(3):284-289, 2001.
Yaron Lipman, Ricky TQ Chen, Heli Ben-Hamu, Maximilian Nickel, and Matt Le. Flow matching for generative modeling. arXiv preprint arXiv:2210.02247, 2022.
Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.
11461-11471, 2022.
Chenlin Meng, Yutong He, Yang Song. Jiaming Song, Jiajun Wu, Jun- Yan Zhu, and Stefano Ermon.
SDEdit: Guided image synthesis and editing with stochastic differential equations. In Interna- tional Conference on Learning Representations, 2022.
Alex Morehead and Jianlin Cheng. Geometry-complett diffusion for 3D molecule generation and optimization. Communications Chemistry, 7(1):150, 2024.
Harry L Morgan. The generation of unique machine description for chemical structures-a tech- nique developed at chemical abstracts service. Journal of chemical documentation, 502)1107-113, 1965.
Karen C Morrison and Paul J Hergenrother. Natural products as starting points for the synthesis of complex and diverse compounds. Natural product reports, 31(1):6-14, 2014.
Mareus Olivecrona, Thomas Blaschke, Ola Engkvist, and Hongming Chen. Molecular de-novo
design through deep reinforcement learning. Journal of Cheminformatics, 9()):88, 2017.
OpenBind Consortium. OpenBind: Large-scale structural fragment initiative. https://
openbind.org 2025. accessed 2025-05-23.
Ferruccio Palazzesi and Alfonso Pozzan. Deep Learning Applied to Bogand-Based De Novo Drug DesignDe novo drug design. In Alexander Heifetz (ed.), Artificial Intelligence in Drug Design, pp. 273-299, Springer US, New York, NY, 2022. ISBN 978-1-07-161787--
doi: 10.1007/
978 178-1-0716-1787-8..11 URL http::://0....rr//000000///////0
12
Daniil Polykovskiy, Alexander Zhebrak, Benjamin Sanchez-Lengeling, Sergey Golovanov, Oktai Tatanov, Stanislav Belyaev, Rauf Kurbanov, Aleksey Artamonov, Vladimir Aladinskiy, Mark Veselov, et al. Molecular sets (moses): a benchmarking platform for molecular generation models. Frontiers in pharmacology, 11 11:565644, 2020.
Santosh Putta, Gregory A Landrum, and Julie E Penzotti. Conformation mining: algorithm for finding biologically relevant conformations. Journal of Medicinal Chemistry, 4819)3313,-31 2005.
Nicholas T Runcie and Antonia SIS Mey. Silvr: guided diffusion for molecule generation. Journal of chemical information and modeling, 63 68(19)5599666005 2023.
David Schaller, Dora Šribar, Theresa Noonan, Lihua Deng. Trung Ngoc Nguyen, Szymon Pach, David Machalz, Marcel Bermudez, and Gerhard Wolber. Next generation 3d pharmacophore modeling. Wiley Interdisciplinary Reviews: Computational Molecular Science, 10(4)e1168, 2020.
Arne Schneuing, Charles Harris, Yuanqi Du, Kieran Didi, Arian Jamasb, Ilia Igashov, Weitao Du, Carla Gomes, Tom L Blundell, Pietro Lio, et al. Structure-based drug design with equivariant diffusion models. Nature Computational Science, 4(12):899-9)) 909, 2024.
Vidushi Sharma, Sharad Wakode, and Hirdesh Kumar. Chapter 2 Structure- and figand-based drug design: concepts, approaches, and challenges. In Navneet Sharma, Himanshu Ojha, Pawan Ku- mar Raghav, and Ramesh k. Goyal (eds.). Chemoinformatics and Bioinformatics in the Phar- maceutical Sciences, pp. 27-53. Academic Press, January 2021. ISBN 978-0- 12-827488-- 1. doi: 10.1016/B978-0-122 12- 821 1. 0000400X URL ht www.wwwwwee.eeeeeee....c com/ Tecteancceaarrticlla//1////7/119 OC0044
13

---

Published as a conference paper at ICLR 2026
Miha Skalic, José Jiménez, Davide Sabbadin, and Gianni De Fabritiis. Shape-based generative modeling for de novo drug design. Journal of Chemical Information and Modeling, 1205) 1214, 2019.
Yuxuan Song, Jingjing Gong, Minkai Xu, Ziyao Cao, Yanyan Lan, Stefano Ermon, Hao Zhou, and Wei-Ying Ma. Equivariant flow matching with hybrid probability transport for 3d molecule generation. Advances Neural Information Processing Systems, 36:549-568 2023.
Maria Sorokina, Peter Merseburger, Kohulan Rajan, Mehmet Aziz Yirik, and Christoph Steinbeck. Coconut online: collection of open natural products database. Journal of Cheminformatics, 13 (1):2, 2021.
Amol Thakkar, Veronika Chadimová, Esben Jannik Bjerrum, Ola Engkvist, and Jean-Louis Rey- mond. Retrosynthetic accessibility score (rascore) - rapid machine learned synthesizability clas- sification from ai driven retrosynthetic planning. Chem. Sci., 12:3339-3349, 2021.
Jesse A Weller and Remo Rohs. Structure- drug design with a deep hierarchical generative model. Journal of Chemical Information and Modeling, 64(16))6450-6444 2024.
Stephanie Wills, Ruben Sanchez-Garcia, Stephen D. Roughley, Andy Merritt, Roderick E. Hub- bard, Frank Von Delft, and Charlotte M. Deane. Expanding the scope of catalogue search to bioisosteric fragment merges using a graph database approach, 2024.
Minkai Xu, Alexander Powers, Ron Dror, Stefano Ermon, and Jure Leskovec. Geometric latent diffusion models for 3D molecule generation. In IMCL. 2023, 2023.
Lijuan Yang, Chao Jin, Guanghui Yang. Zhitong Bing, Liang Huang, Yuzhen Niu, and Lei Yang. Transformer-basee deep learning method for optimizing admet properties of lead compounds. Physical Chemistry Chemical Physics, 25(3):2377-2385 2023.
Kiwoong Yoo, Owen Oertell, Junhyun Lee, Sanghoon Lee, and Jaewoo Kang. Turbohopp: ac- celerated molecule scaffold hopping with consistency models. Advances in Neural Information Processing Systems, 37:41157 4155, 2024.
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. LION: Latent point diffusion models for 3D shape generation. In Advances in Neural Information Processing Systems, volume 35, pp. 10021-10039 2022.
Wonho Zhung, Hyeongwoo Kim, and Woo Youn Kim. 3D molecular generative framework for interaction-guided drug design. Nature Communications, 15(1):2688, March 2024. ISSN 2041- 1723. doi: 10. 1038//4 467--41--701111 s 841467-024-470112
htthtts:: www. nature. ctt/artiicessss.eeeee Publisher: Nature Publishing Group.
Yael Ziv, Fergus Imrie, Brian Marsden, and Charlotte M Deane. Molsnapper: conditioning diffusion for structure-based drug design. Journal of Chemical Information and Modeling, 65(9):4263- 4273, 2025.
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A
APPENDIX OVERVIEW
This appendix contains material that supports and extends the main text.
Ablation studies on inference-time conditioning parameters
B.1
Interpolate-Integrate ablations
B.2
Replacement Guidance ablations
Evaluation metrics
Pharmacophore feature definitions
Extended results for natural product hopping
Detailed evaluation of bioisosteric fragment merging
Extended evaluation pharmacophoor-merginn on SARS-CoV-2 M
Limitations
Reproducibility Statement
Statement on Large Language Model (LLM) Usage
B ABLATION: CONDITIONING PARAMETERS
We quantify how the principal inference-ting syper-parameters of the two conditioning schemes Interpolate and Replacement Guidance, affect validity, diversity, and similarity.
Validity, uniqueness, and novelty. Validity refers to passing all PoseBusters (Buttenschoen et al, 2023) intramolecular checks. Uniqueness and novelty computed canonical SMILES obtained via RDKit MolTo Les.A generated molecule novel if its SMILES different from the seed molecule, and unique within batch if its SMILES appears exactly once.
Similarity metrics. Shape and color similarity score (SCRdœu): assesses 3D similarity be- tween generated molecules and reference molecule, described in Imrie al. 2021 SCRUKa scores range from 0 (no match) to (perfect match). The color similarity function evaluates the overlap of pharmacophoric features, while the shape similarity measure involves simple volu- metric comparison between the two conformers. SCRDKI uses two RDKit functions, based on the methods described in PPutta et al. 2005 and Landrum al. 2006)
ECFP4 Tanimoto is the Tanimoto coefficient between 2 048-bit Morgan fingerprints (Morgan 1965) (radius 2) generated with RDKit GetMorgan of 2-D scaffold similarity.
standard measure
B.1 INTERPOLATE- INREF--INEGRRATT
We analyze the effect of varying the restart time and the seed noise scale a. All experiments use the first 1,000 molecules from the GEOM-Drugs (Axelrod & Gómez-Bomband 2022 test set as reference seeds.
B.1.1 EFFECT OF TIME 7
In all ablation experiments, we use the 100-step, log-spaced ODE grid from SEMLAFLOW. In Interpolate-Interratee, the restart time т is set by choosing step index & this grid; simply the s-th value in the solver's precomputed log-spaced time schedule. Table 5 lists the restart step (index in the log-spaced 100-step grid) and the corresponding continuous time T.
We restart the flow at six grid positions (s [70, 80]->r E [0.75, 0.6)). Larger (late restarts) preserves more of the seed; smaller yields greater novelty at the cost of similarity. Unless otherwise stated, the seed-noise scale is fixed to 0.1.
The choice of the restart time, т, reveals a clear trade-off between novelty and fidelity. Lower values of produce more novel and unique molecules, while higher values improve chemical validity and 3D similarity (SCRdKi) to the seed molecule. These trends are detailed Tables and 7 and visualized in Figure
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Table Mapping between restart steps in the log-spaced ODE grid and the corresponding continu- ous time 7 used in intepollat-Inteerraee Restart steps
(continuous time)
70
0.75
72
0.72
74
0.70
76
0.69
78
0.64
80
0.60
Table Validity of generated molecules conditioned on reference at noise level σ = 0.1. Each row reports the number of molecules attempted and the percentage that generated, connected, chemically valid, physically valid, and overall valid. A molecule is considered fully valid only if passes all three criteria: connectedness, chemical validity, and physical validity.
Generated % connectedness % chemical
% physical
% Valid
0.70
95.8
95.3
95.8
89.5
89.0
0.72
94.0
92.8
94.0
87.4
86.3
0.74
91.6
89.0
91.5
85.4
83.1
0.76
90.0
86.7
90.0
84.7
81.6
0.78
90.6
87.0
90.6
86.2
82.8
0.80
91.5
88.0
91.5
87.8
84.4
Table 7: Validity, uniqueness, and novelty of generated molecules conditioned reference molecule at noise level a = 0.10. Values are reported as proportions of the total number of molecules that were be generated.
Valid
Valid & Novel
Valid Unique
Valid & Unique Novel
0.70
89.05
85.18
20.63
20.36
0.72
86.31
83.38
39.03
38.74
0.74
83.08
81.41
61.99
61.70
0.76
81.56
80.88
76.38
76.12
0.78
82.78
82.57
82.13
81.97
0.80
84.45
84.34
84.37
84.24
Distribution SuCOS Unique Novol Valid Molecules
fisrrib.tiee
Uniig
Valld Molec
matieed
predict ons 670 so
predictions
0.03
predict ens 212 20
predictiona 422
predictins_ LC
predicion 024400000000
predictins to
predictions
0.04
prodict
9020
predictions
Pojerten
predict
predictions
0.03
0.055
0.2
0.000
0.0
0.00
0.2
0.6
0.2
0.3
044
0.3
0.0
0.9
Snape
(a) Distribution of SCRUKI between the generated and reference molccules.
(b) Cumulative distribution ECFP4 Tanimoto similarity. A sharper jump near 1.0 indicates more identical fingerprints.
Chemical and shape/pharmacophoricc similarity between generated and reference molecules, measured by ECFP4 Tanimoto similarity and SCRDKH> respectively. Curves are shown for different integration step counts at fixed noise level 0.1. Only valid molecules are included.
Figure 3:
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B.1.2 EFFECT OF NOISE LEVEL
We fix 0.72 and vary the noise added to the interpolated seed. Higher injects more coordi- nate noise the restart point.
Table 8: Validity of generated molecules conditioned reference molecule for restart step
= 0.72, across different noise levels a. Each row reports the total number of molecules attempted and the percentage that were generated, connected, chemically valid, physically valid, and overall valid. A molecule is considered valid only if it meets all three criteria.
Total
% Gen.
% Conn.
% Chem.
% Phys.
% Valid
0.00
94956
95.0
94.2
94.9
88.5
87.8
0.05
94765
94.8
93.9
94.8
88.1
87.3
0.10
94019
94.0
92.8
94.0
87.4
86.3
0.72
0.15
92807
92.8
91.0
92.8
86.4
84.7
0.20
91043
91.0
88.4
91.0
84.7
82.2
0.25
89617
89.6
86.0
89.6
83.5
80.2
Table 9: Validity, uniqueness, and novelty of generated molecules conditioned on a reference
molecule for 0.72, across varying noise levels a.. Values are reported proportions of the total number of molecules that were to be generated.
Valid
Valid & Novel
Valid & Unique
Valid & Unique & Novel
0.00
87.83
84.42
30.80
30.52
0.05
87.35
84.04
32.73
32,45
0.10
86.31
83.38
39.03
38.74
0.72
0.15
84.69
82.31
49.57
49.26
0.20
82.16
80.50
60.45
60.16
0,25
80.18
79.23
69.52
69.26
Tables and 9 show clear trade-off: higher values increase novelty but reduce validity. This suggests a should be tuned for each application based on desired diversity.
bistiiusioon SuCCS of Unique Neval valid Molecules
Distritution of Shape Tanimoto of Linggus Kovel Valid Moleciile lps
mittod prediction d32 60
0.0000
predictune 10
36173
0.020
prediction
06150
pelkktion_di2_36
Degporten
06125
90100
0.00
pred predction
00015
predetione 40.10
0.0030
prodiction: 672 50.15
preditioon d/2 J0.20
0.8035
prrdichons d72 x0.25
0.000
0.000
0.6
(b) Cumulative distribution ECFP4 Tanimoto similarity. sharper jump near 1.0 indicates more identical fingerprints.
(a) Distribution SCRDKI between the generated and reference molecules.
Figure Chemical and shape/pharmacophoricc similarity between generated and reference molecules across different noise levels in intepollaeeInneeraan We report SCruKa (a) and ECFP4 Tanimoto similarity (b), computed only for valid molecules. Curves are shown for different levels.
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always generates molecule with the same number of Published as a conference paper at ICLR 2026
B.1.3 BEHAVIOUR ON DISCONNECTED SEED STRUCTURES
We include an ablation study evaluating the "healing" capability of Interpolate-Integrace when pro- vided with disconnected inputs. The experiment is performed on the DIFFLINKER test set (Igashov) al., 2024). using curated subset of 1,129 examples, each consisting of three spatially separated fragments. This setup provides deliberately challenging test of whether the model can transform set of unconnected seed structures into single, chemically valid molecule. Because the flow pre- serves dimensionality, atoms as the input fragments.
We measure how generation validity varies function of the restart step s, which determines the noise level injected into the seed structure. As defined in Table a larger step index & corresponds to smaller continuous time т, meaning that more noise is added to the input and the ODE is integrated for a longer duration. This allows us to directly assess how increased noise facilitates the model's ability "heal" disconnected configurations.
Table 10: Validity of generated molecules conditioned on disconnected 3-fragment seeds from the DIFFLINKER set, as function of restart step s. Each row reports the percentage of molecules that were generated, connected (chemical validity), physically valid, and overall valid.
Restart Step (s)
% Generated
% Chemical
% Physical
Valid
68
94.6
62.4
86.4
58.1
70
93.6
66.2
85.7
61.1
72
94.8
68.2
87.8
63.0
74
94.2
77.0
88.9
72.9
76
95.4
83.5
90.7
79.6
78
93.7
84.5
89.3
80.8
80
94.1
86.0
89.5
82.2
The results in Table 10 show a clear monotonic trend: as s increases from 68 to 80, the overall validity rises from 58. 1% 82.2%. This confirms our hypothesis. larger (more noise) moves the restart point further from the "broken" disconnected seed, giving the vector field a longer, smoother trajectory to "heal" the structure, generate linkers, and converge to valid, connected molecule on the data manifold. Conversely, smaller s (less noise) starts too close to the out-of -itto--distriuution input, and the vector field fails to correct the disconnections. 5 provides visual examples of this process.
68
S 70
74
=
= 78
$ = 80
= 74
= 76
$ 78
Figure 5: Effect of the restart step 8 on the output of Interpolate-Integrate. In each panel, the left column shows the disconnected reference fragments provided as input. The right column shows generated molecules for different values of s. Lower values of inject less noise and leave fewer integration steps, causing the model to stay close the reference fragments and largely preserve their geometry. Higher values of s add more noise and allow longer integration, giving the model greater freedom to deviate from the seed and produce increasingly modified structures.
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B.1.4 EFFECT OF NUMBER OF SEED FRAGMENTS
To study how the number of input fragments affects generation quality, we performed ablation in the M case where we supplied the model with varying numbers of fragments as the seed. For each configuration, we generated 100 molecules with sizes between 25-35 atoms. We start from fragment till 10. For each configuration, we also estimate the molecular volume of the generated seeds using RDKit's A1 1 Chem. ComputeMo 1 Volumme which provides an approximate van der Waals volume based on the 3D conformer.
As shown Table increasing the number of fragments generally degrades performance, con- firming that higher fragment counts increase task difficulty. This overall downward trend is not monotonie; the model exhibits fluctuating robustness in the mid-range. The volume measurements help contextualize this behavior. The sharp increase in molecular volume between 5 and 6 fragments corresponds to pronounced drop in validity, suggesting that sudden expansions in the seed's spatial extent reduce structural coherence and hinder successful reconstruction. Conversely, at 8 fragments the prammframmet ratio is relatively low, which may explain why performance temporarily stabilizes before dropping again for highly fragmented inputs (9-10 fragments).
Table 11: Ablation the number of seed fragments provided to the model. For each setting, generate 100 molecules of size 25-35 atoms the M case.
# Fragments
Volume (A
Vol.Frag
% Generated
% Chemical
% Physical
% Valid
2
211.63
105.8
91.0
90.0
86.0
84.0
3
282.57
94.2
92.0
85.0
88.0
81.0
290.47
72.6
87.0
79.0
80.0
72.0
5
393.12
78.6
86.0
80.0
84.0
77.0
513.30
85.6
85.0
78.0
82.0
75.0
7
559.46
79.9
90.0
80.0
84.0
74.0
8
586.67
73.3
86.0
83.0
82.0
79.0
9
628.82
69.9
72.0
65.0
69.0
61.0
10
741.60
74.2
77.0
68.0
74.0
65.0
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B.2 REPLACEMENT GUIDANCE
We investigate the effect of the relaxation time free in Replacement Guidance, where thee denotes the integration step after which fragment masking is disabled. We also compare this strategy against an inpainting-ben based conditioning approach.
Experiments conducted on the DIFELINKER test (Igashov al. 2024). using a curated sub- set of 1,129 cases in which the objective is to generate a molecule that preserves the shape and pharmacophoric features of three bound fragments.
B.2.1 EFFECT OF TIME (FREE
The relaxation time, Uiee, directly affects the properties of the generated molecules, as shown in Table T2
Increasing firee- which holds the fragment constraints for longer duration-consistenlly improves the 3D similarity (SCRDKI) to the reference fragments. Conversely, this stronger anchoring can negatively impact PoseBusters' validity across all metrics.
Table 12: Effect of relaxation time free Replacement Guidance. We report the proportion of molecules that are successfully generated, pass all chemical and physical PoseBusters' validity checks, meet the full validity criteria, and the SCROKи- Larger tree allows fragment replacement for more steps, leading to greater flexibility but weaker fragment anchoring.
Uree % Generated
Chemical
Physical
% Valid
SCrdki
0.85
89.8
79.0
81.5
72.2
0.49 + 0.10
0.90
89.6
79.2
82.0
72.8
0.50 0.10
0.95
89.7
79.1
82.0
72.6
0.51 = 0.10
1.00
78.0
65.4
68.4
58.6
0.55 0.10
B.2.2 REPLACEMENT GUIDANCE VS. NOISED INPAINTING CONDITIONING
In this ablation, we compare Replacement Guidance to an inpainting-style conditioning strategy. For inpainting, instead of inserting the clean fragment at each step, we interpolate the fragment to time and use this noised fragment for replacement, making the input locally in-distribution. The inpainting baseline uses the same update schedule and the same the relaxation setting as Replacement Guidance, ensuring a fully matched comparison.
To disentangle the effect of noising different components, evaluate four inpainting variants: (i) noising only the coordinates while hard-replacing atom and bond features, noising only the atom types, while hard-replacing coordinates and bond logits (iii) noising only the bond logits, while hard- replacing coordinates and atom types, and (iv) noising all components simultancously (the standard inpainting analogue).
As shown in Table 13 Replacement Guidance achieves significantly higher overall validity (58.6%)
compared to standard, fully-noised inpainting (32.0%). The steep drop Inpaint-Coorde (35.6% confirms that noising the geometry is the primary failure mode, underscoring the critical role of stable geometric anchor.
We also note that the Inpaint Aopaist-Bonds Bonds configuration- where wwere coordinates and atoms remain hard- canstraind--yiedde validity (59.3%) and shape similarity (SCRdKI 0.56) that are highly comparable to Replacement Guidance (58.6% and 0.55, respectively). This reinforces that a hard coordinate constraint is key to both structural coherence and shape retention. While this specific Inpaint-Ronds setup warrants further investigation, this ablation confirms hypothesis that maintaining hard, un-noised geometric anchor is superior to standard inpainting.
To further analyze why Replacement Guidance yields higher validity than inpainting, we quantify the geometric stability of both approaches by tracking the L2 distance between the predicted co- ordinates and the clean seed coordinates throughout the entire reverse trajectory. Figure T shows this deviation averaged over 1129 runs with different seeds. Replacement Guidance shows a large drop in 1.2 distance (Á) the beginning of the trajectory, after which the distance remains relatively
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Validity comparison between inpainting configurations and Replacement Guidance (ffree = 1.0) on the DUTLLINKER test subset (1,129 cases), We report the percentage of molecules that successfully generated, chemically valid, physically valid, valid overall (i.e., passing all checks), and SCRDKi
Table 13:
Method
% Generated
Chemical
% Physical
% Valid
SCRDKi
Inpaint coords
61.8
41.4
49.7
35.6
0.45+0.11
(Hard-Replace Atoms + Bonds)
Inpaint atoms
74.8
62.8
64.9
56.1
0.52:10.10
(Hard-Replace Coords Bonds)
Inpaint bonds
80.8
66.4
70.0
59.3
0.5610.11
-aard-eellace Coords Atoms)
Inpaint (all)
57.0
38.3
44.1
32.0
0.44=0.10
Replacement Guidance
78.0
65.4
68.4
58.6
0.5510.10
stable and low. In contrast, the inpainting curve decreases much more gradually and stays at a higher L2 distance throughout the trajectory.
Lafprediced, seed)
p2ppeciicedd seb0)
(a) Replacement Guidance.
(b) Inpainting style conditioning.
Figure 6: Comparison of coordinate stability during generation under (a) Replacement Guidance and (b) inpainting-style conditioning. Each curve reports the mean + standard deviation (shaded) of the 1.2 distance (A) between the predicted coordinates and the clean input seed coordinates across 1129 different seeds.
B.2.3 EFFECT CONDITIONING INFORMATION
To assess how different conditioning signals influence generation quality in Replacement-guiddacee we perform an ablation over three levels of structural information provided to the model: (i) 3D coordinates only (coords), (ii) coordinates with atom-type annotations (coords + atoms), and (ii) coordinates supplemented with both atom types and bond connectivity (coords atoms + bonds). As shown in Table 14] increasing the amount of conditioning information consistently reduces chem- ical and physical validity, at the same time, SCROKi similarity increases with richer conditioning. reflecting that more explicit structural guidance leads to molecules that adhere more closely to the provided fragment template, as expected.
B.2.4 EFFECT NUMBER OF SEED FRAGMENTS.
Table 15 reports an ablation of pellacement-guidanee in the M case, where the model is condi- tioned on between 2 and 10 input fragments. For each configuration, generate 100 molecules with sizes between 25-35 atoms. also estimate the molecular volume of each seed using RDKit's AliChem. Comput eMo eMo Volum, which provides approximate van der Waals volume based on the 3D conformer.
The relationship between fragment count and generation quality is not strictly monotonic. Using two to five fragments yields strong performance across all validity metrics, with five fragments achiev- ing the highest full validity (82%). A sharp increase in molecular volume between five and six
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Table 14: Effect of conditioning information in Replacement Guidance. We compare three levels of structural context provided to the model: coordinates only (coords), coordinates with atom types (coords atoms), and coordinates with atom types and bond connectivity (coords + atoms + bonds). We report the proportion of molecules that are successfully generated, pass chemical and physical PoseBusters validity checks, and meet full validity criteria.
Conditioning
% Generated % Chemical
% Physical
% Valid
SCrdki
coords
92.5
86.4
92.5
86.3
0.35+00006
coords atoms
85,2
75.8
85.2
75.7
0.43+00008
coords atoms + bonds
78.0
65.4
68.4
58.6
0.55+00100
fragments coincides with noticeable drop in validity, suggesting that abrupt expansions in spatial extent make reconstruction more difficult. Performance partially recovers seven and eight frag- ments-where the volume-per-fragment ratio decreases-but declines again for highly fragmented inputs (9-10 fragments), indicating that excessive fragmentation ultimately reduces structural co- herence and hampers the model's ability to reconstruct chemically consistent molecules.
Table 15: Ablation on the number of seed fragments provided to the model under Replacement- guidance. For each setting, generate 100 molecules of size 25-35 atoms in the M case. In- creasing the number of fragments gradually reduces structural coherence, making reconstruction more challenging for highly fragmented inputs.
# Fragments Volume (A
Vol.Fraag
% Generated
Chemical
% Physical
% Valid
2
211.63
105.8
91.0
91.0
87.0
86.0
282.57
94.2
85.0
81.0
79.0
75.0
290.47
72.6
79.0
75.0
75.0
71.0
5
393.12
78.6
87.0
86.0
84.0
82,0
6
513.30
85.6
72.0
69.0
63.0
61.0
559.46
79.9
83.0
78.0
70.0
66.0
586.67
73.3
83.0
78.0
74.0
68.0
9
628.82
69.9
83.0
79.0
68.0
66.0
10
741.60
74.2
74.0
69.0
64.0
58.0
C EVALUATION METRICS
We assess the generated molecules using the following metrics:
Synthetic Accessibility (SA) Score. Several scoring systems exist estimate the synthetic accessi- bility of a compound (Coley et al. 2018 Thakkar et al. 12021] | Ertl & Schuffenhauer 2009). We use the SA score of Ertl and Schuffenhauer (Ertl & Schuffenhauer] 2009), which combines historical data from synthesized compounds with molecular complexity to assess synthetic feasibility. Lower scores indicate greater ease of synthesis.
Validity Checks. Molecules are validated at multiple stages:
RDKit Validity: The molecule can be successfully parsed and sanitized by RDKit.
PoseBusters Validity: The molecule passes chemical and physical plausibility checks using
PoseBusters (Buttenschoen et al.) 2023).
Similarity Metrics: We adapted similarity scores introduced in the ShEPhERD paper (Adams et al.
2025) with the same settings (xTB relaxtion in implicit water and ESP alignment with 400 surface
points)
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Surface Similarity: Quantifies the volumetric overlap between two atomic point clouds of the generated and reference molecules. High scores indicate preservation of 3D shape.
Electrostatic Similarity: Measures the similarity between the electrostatic potential fields of the generated and reference molecules, capturing charge complementarity relevant for molecular recognition.
Pharmacoophoe Similarity: Assesses the overlap of key pharmacophoric fea- tures hydrogen bond donors (HBD), acceptors (HBA), and aromatic centers- venters-between the generated and reference molecules. Directional alignment is included when necessary capture interaction specificity.
Docking Score. We used AutoDock Vina (Eberhardt et al. 2021 to further assess the quality of the generated molecules. We used the Vina scoring function to assess molecules with binding site conditional poses. Since such scoring functions are sensitive to the exact coordinates and can assign experimental structures poor scores without energy minimization (Weller & Rohs (024), we computed energy minrimieid scores for generated molecules. This protocol evaluates the plausibility of the generated pose via local energy minimization and does not perform global pose search (i.e., redocking). For this minimization step, the binding site was defined with buffer of 5 Á around the reference ligand.
D PHARMACOPHORE FEATURE DEFINITIONS AND EXTRACTION PROTOCOLS
Pharmacophores can be used as representation of chemical interactions crucial for ligand bind- ing to macromolecular targets (Schaller al. 2020). These interactions include hydrogen bonds, charge interactions, and lipophilic contacts. Pharmacophores can be derived through ligand-based or structure-based approaches, providing versatility in their application. In this study, we employed 3D pharmacophores derived from ground truth molecules.
Throughout this work, when conditioning on pharmacophoric features, we define features using the standard RDKit FeatureFactor (based on the BaseF fdef definition file). This implementation also provided in our supplementary code.
We primarily extract the 3D coordinates of three specific feature types: Hydrogen Bond Donors (HBD), Hydrogen Bond Acceptors (HBA), and Aromatic Rings. These extracted feature points are used post-hoc filter, but rather the direct input seed structure (21) for the generative pro- cess. After finding the subset of HBBD/BAA/rroman features, we use their original 3D coordinates construct the seed.
is important to distinguish this conditioning protocol (used as input for our model) from the eval- uation metric (used score the output). For the pharmacophore similarity score (from the SHEP- HERD protocol (Adams et al. 2025)), ) much broader set of features is used. This includes both directional features (such as HBA, HBD, and Aromatic ring vectors) and non-directional features (such as Hydrophobes, Anions, and Cations) to provide a comprehensive assessment of 3D property overlap.
E NATURAL PRODUCT TASK: EXTENDED EVALUATION
To complement the main results, we provide an extended evaluation of model behavior on the natu- ral product (NP) hopping task. We report molecular validity, synthetic accessibility scores, pharma- cophore feature recovery, and conformational stability for generated analogues across all three NP targets.
E. EXPERIMENTAL DETAILS
For each seed NP, generated 2,500 candidate analogues with total atom counts between 36 and 80 (including hydrogens). We used our two conditioning strategies: enerpolate-Integree (restarting at continuous time of 0.75) and Replacement Guidance (with relaxation time of tyllxx 1.0). Both approaches were conditioned by sampling key pharmacophore features -hddrggen bond donors (HBD), acceptors (HBA), and aromatic atoms-from the reference NP. The remaining atoms
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in the seed structure sampled randomly from the full set of atoms in the reference. All generated molecules were post-proeessed with xTB geometry relaxation and ESP alignment before evaluation.
E.2 VALIDITY AND FILTERING PIPELINE
Table T6 reports the validity of the generated molecules each stage of our filtering pipeline. The first stage assesses RDKit validity, followed by geometry quality check using PoseBusters. The fi- nal filter is based on synthetic accessibility (SA 4.5), where Replacement Guidance demon- strates a significantly higher final yield of successful candidates.
Table Reported validity of generated molecules the natural product (NP) hopping task. For each NP target (NP1, NP2, NP3), 2,500 molecules were generated using two methods: interpo- late-integrate and replacement-guidane according to RDKit parsing, (2) pass all PoseBusters (PB) geometry checks prior xTB relaxation, (3) pass all PoseBusters geometry checks after xTB geometry relaxation and ESP alignment, and (4) additionally have synthetic accessibility (SA) score
We report the percentage of molecules that (1) are valid
4.5.
NP
Method
Valid (RDKit)
PB Valid
xTB + PB Valid
XTB + PB Valid SA < 4.5
ShEPhERD
65.5%
55.5%
57.3%
24.7%
89.4%
64.9%
71.5%
35.6%%
Replacement guidance
72.5%
51.2%
50.2%
ShEPPERD
56.5%
50,0%
51.0%
17.45
Interpolate integrate
98.5
91.4%
93.6%
16.0%
Ceplacement-guidanee
48.8%
ShEPbERD
55.3%
39,7%
44.1%
20.2%%
interpolate-ittgnnn
77.1%
50.1%
62.7%
5.2%
NP3
ceeplacemei--uuideee guidance
72.9%
48.2%
64.4%
42.1 %
E.3 PERFORMANCE ON FILTERED CANDIDATES (SA < 4.5)
Table 17 presents the primary performance metrics for the final set of candidates that passed all filters, including the SA <4.5 threshold. All molecules were subject xTB geometry relaxation and ESP alignment before scoring. These results show that Replacement eetiicemmmm---iddnceeeee guidance consistently yields the largest fraction of fully valid, synthetically accessible candidates and generates molecules with the best synthetic accessibility scores across all three natural product targets. In contrast, Interpolate-interrate the superior method for achieving the highest 3D similarity scores.
Table 17: Performance summary for molecular generation across NP hopping tasks (NP1, NP2, NP3) after xTB geometry relaxation and ESP alignment. We report PoseBusters Success Rate (Valid and SA 4.5), similarity metrics (ESP and pharnacophore), and SA score. Only molecules with synthetic accessibility (SA) scores less than 4.5 are included in this analysis.
NP
Method
Success Rute
ESP sim
ESP sim top10
Pharma sim
Pharma top10
SA
NPI
SWEPLERD
24.7%
0.18 + 0.06
0.63 0.02
0.17 0.06
0.31 0.03
3.95 0.15
Interpolate iintegrat
35.6%
0.51 1: 0.11
0.51 1: 0.02
0.18 1 0.09
01.49 :: 0.06
3.51 2: 0.55
Replucement guidance
50.2%
0.00
0,81 0.02
0.18
0.06
3.36 0.66
NP2
SAEPhERD
17.4%
0.68 1 1..66
0.79 0.01
0.20 1 0.08
0.34 2: 0.12
3.70 0.57
Interpolate-
16.0%
0.70 +: 0.08
0.86 + 0.01
0.22 4: 0.10
(L46 + 0.02
3.90 0.48
Replacement guidunce
0.44
3.41
NP3
ShEPhERD
20.2%
0.46 1: 0.06
(160 1: 0.01
0.17 + 0.05
0.30 +: 0.02
3.81 0.11
Interpolate integrate
5.25
0.40 4: 0.07
0.56 0.05
0.11 4: 0.04
0.20 0.03
0.44
celleemmnnt-guidanee
42.1%
0.45 +: 0.06
0.65 0.03
0.14: 0.04
0.29 0.02
3.33 0.72
E.4 PERFORMANCE ALL VALID MOLECULES (UNFILTERED)
For more comprehensive analysis, Table 18 summarizes the same performance metrics for all molecules that passed the initial PoseBusters validity checks, prior to SA filtering. These unfiltered results reveal the inherent trade-offs of the methods: erating a high percentage of initially valid structures, while Replacement -uidance demonstrates a strong intrinsic bias towards producing molecules with better synthetic accessibility.
is most effective gen-
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Table 18: Performance summary for molecular generation across NP hopping tasks (NP1, NP2, NP3) after xTB geometry relaxation and ESP alignment. We report PoseBusters validity, similarity metries (ESP and pharmacophore). and SA score.
NP
Method
Valid 1
ESP sim
ESP sim top10
Pharma sim
Pharma sim top10 |
SA
ShEPhERD
0.47
Interpolate-iateerrae
71.5%
0.55 + 0.13
0.89 + 0.02
0.21 1:21::00 0.11
0.73 = 0.09
1.67 = 1.02
Replacement guidance
0.11
3.74
NP2
ShEPhERD
51.0%
0.68 0.08
0.81 + 0.01
0.21 1: 0.06
0.39 : 0.02
5.09 4: 1.2
Interpolate integrate
ineerolaat-iineeraate
Replacement- guidance
87.9%
0.72 0.09
0.91 0.02
0,26
0.82 + 0.08
4.43 4: 1.31
ShEPhERD
0.06
0.62
0.004
0.02
4,09
Interpolate integrate
62.7%
0.47 4: 0.09
0.69 + 0.02
0.14 0.06
0.39 1 0.05
5.92 + 1.03
Replacement-guiiee 64.4%
0.47
0.02
0.16
0.39
4.07 1.27
Table 19: Molecular property statistics for molecular generation across NP hopping tasks (NPI, NP2, NP3), after xTB geometry relaxation and ESP aligmment. We report molecular weight, Lip- inski rule-of-fin five violations, logP, QED, number of rings, and SMILES uniqueness, computed over PoseBusters- molecules.
NP
Method
Weight [Da]
Lipiaski R5
logP
QED
# Rings
% Unique SMILES
NPI
ShEPPERD Intenpolate-integrate
388.46
47.64
4.64 + 0.51
1.16 + 1.59
0.52
0.18
2.97 1.27
99.9%
412.26
76.50
4.06 +
1.53 +
0.42
0
2.83
100%
Replacement guidance
409.36
73.24
4.37 + 0.77
3.23 + 1.55
0.50 0.19
3.33
1.04
99.7%
ShEPhERD
45.59
1.1.2
Interpolate-interrate
322 11
37.92
1.70
2:0::00 0.59
0.62 + 1.56
0.51 0.16
2.35 + 1.18
84.5%
Replacement guidance
34.
NP3
ShEPhERD
422.55
14.14
4.35 1 0.01
127 1 1.57
0.42 0.17
2.73
1.10
99.9%
Interpolate- integrate
431.50
-0.36 2.27
0.35 0.20
100%
Replacement-guidnnce
116. 12 76.61
1.13
1 0.82
2.96 + 1.41
0.50 0.199
3.68 = 1.41
100%
E.5 DISTRIBUTIONS OF KEY METRICS
The figures below provide visual summary of the performance trade-offs between the methods. the clear advantage of Replacement Guidance in producing molecules with
Figure 7 demonstrates F lower (better) SA scores. While Carrpolate-Interratee yields more geometrically stable molecules post-relaxation (lower RMSD in Figure 8) Figure 9 confirms that Replacement Guidance and SHEPHERD generate more chemically diverse candidates. reflecting broader exploration.
NP1
NP2
NP3
Synthetic accessibility (SA) score distributions for generated analogues across the three natural product tasks.
Figure 7:
E.6 DETAILED POSEBUSTERS VALIDITY
Tables 20
21 and 22 provide detailed breakdown of the PoseBusters validity evaluation. Table
reports overall generation success and combined chemical/physical validity. Table 2T decomposes chemical validity into sanitization, hydrogen compatibility, valence, and connectivity checks. Ta- ble 22 presents results from the PoseBusters suite, including bond lengths, angles, sterie clashes, and energetic plausibility.
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2.
19gge Stgg Aapacaere gallaca
Blagrae publa Raptice wanagaaaaee
Blggobe Via pabla Paytice gaidare
NPI: RMSD (aligned vs. original)
NP2: RMSD (aligned vs. original)
NP3: RMSD (aligned vs. original)
Figure 8: RMSD between the xTB-relaxcd/aligned and original generated conformers for each NP task.
203
113
108
00251 Siniarty
dindorty
NPO: Graph similarity reference
NP1: Graph similarity to reference
NP2: Graph similarity reference
Peodky
NPO: Pairwise Tanimoto similarity NP1: Pairwise Tanimoto similarity
NP2: Pairwise Tanimoto similarity
Figure Graph similarity to the reference (top row) and pairwise Tanimoto similarity among generated ligands (bottom row) for the three NP hopping tasks.
Table 20: Validity of generated molecules across the natural-product (NP) hopping tasks. Each method attempts 2,500 generations; we report the percentage of molecules that were generated (% Generated) and the proportion of those that pass chemical validity checks, physical PoseBusters checks, or both. NP
Method
% Generated % Chemical
% Physical
% Valid
Interpolate-intograte
81.6
81.6
71.6
71.5
NP1
Replacementt-uuidance
68.2
68.2
60.5
60.5
cecrpolate-integrann
96.7
96.7
93.6
93.6
NP2
Replacement--uiddacce
92.6
92.6
88.0
87.9
petroolateiinterrane
69.0
69.0
62.8
62.7
NP3
Replasement-guidance
67.6
67.6
64.4
64.4
E.7
PHARMACOPHORE FEATURE RECOVERY
To better understand how pharmacophoric features are preserved during generation, we analyzed subset of ligands produced by the Replacement Guidance strategy. Molecules were selected based on pharmacophore similarity from the filtered set with SA score 4.5, and each recovered reference pharmacophore feature here focusing on HBD, HBA, and aromatic centers as defined by RDKit's FeatureFactory- was matched within 1. 1.5Ä threshold. While this is not strict pharmacophore
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Table 21: Chemical validity of generated molecules for the natural product (NP) hopping task. Shown are the percentages of generated molecules that pass each chemical validity check. These checks operate on the molecular graph and do not involve 3D conformations. The last column reports the percentage that passed all checks.
NP
Method
Gen.
% Sanit.
H-comp.
No Rad.
% Conn.
% Chem.
NP1
Interpolate-integrate
81.6
81.6
81.6
81.6
81.6
81.6
Replacementt-guidaaee
68.2
68.2
68.2
68.2
68.2
68.2
NP2
Interpolate
100.0
96.6
96,6
96,6
96.6
96.7
Replacement-guidanne
92.6
92.6
92.6
92.6
92.6
92.6
NP3
Interpolate-integrate
100.0
68.9
68.9
68.9
68.9
69.0
Replacement-guidame
67.6
67.6
67.6
67.6
67.6
67.6
Table 22: Physical validity of generated molecules for the natural product (NP) hopping task.
Shown are the percentages of generated molecules that pass each intramolecular PoseBusters check. The final column shows the percentage passing all physical tests.
NP
Method
% Gen.
Bond Len.
Bond Ang.
Sterie Clash
Ring Flat.
DB Flat.
Energy
Phys.
NPI
pneepolate-integnee
8166
81.4
81.6
72.3
81.6
81.0
81.3
71.6
balaccemet-guuidaaee
68.2
68.0
68.2
61.2
68.2
67.6
60.5
Interpolate integrate
96.7
96.5
94.0
Replacement-guiee
92.6
92.5
92.5
88.4
92.6
92.3
92.0
88.0
NP3
Interpolate-integnnee
69.0
68.3
68.8
65.1
68.9
67.7
67.8
62.8
Replacement guidance
67.6
67.6
recovery metric, it allows us to examine which reference features are most consistently reconstructed in high-quality analogues.
Figure T0 presents the per-feature match fractions for each target. Spheres represent the reference pharmacophore features, and their color encodes the recovery rate across the selected molecules: green indicates features recovered in most analogues, yellow indicates moderate recovery, and red indicates rare recovery. In each subpanel, Top rows display the recovery rate for the ten highest- ranked analogues by pharmacophore similarity, while Bottom rows show rates for all filtered ana- logues meeting the SA < 4.5 threshold.
For example, in NPI (top row, left), most pharmacophoric features are green or yellow, showing high retention in top-ranked molecules. In contrast, the bottom row for NP3 contains more red features, indicating less consistent recovery.
The pessorerfonnng molecules recover nearly all pharmacophoric features for NPO and NP1, in- cluding those that were inconsistently reproduced in the general population. This suggests that the model capable of high-fidelity reconstruction when guided by appropriate selection criteria.
In contrast, the broader set of generated molecules shows greater variability in feature retention, particularly for features located on the molecular periphery. These features are more flexible and less scaffold-constrained,, making them harder to consistently reproduce-desplee their potential importance for binding interactions.
E.8 PERFORMANCE OF MOLSNAPPER BASELINE
For the NP hopping task, also evaluated MolSnapper in ligand-only mode (clash rate 0) using the NP's HBA and HBD features as input as secondary baseline to understand how a method conditioned discrete pharmacophore points would perform. A limitation of this baseline is that does not explicitly place hydrogens. The results for each natural product target are presented in Table 24
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NP1: Top 10 analogues
NP2: Top 10 analogues
NP3: Top 10 analogues
NPI: All filtered
NP2: All filtered
NP3: All filtered
Pharmacophore feature retention for NP1, NP2, and NP3. Top: top 10 analogues ranked by pharmacophore similarity to the reference NP. Bottom: all filtered analogues with SA S 4.5. Dummy atoms are colored by recovery rate. Reference molecules omitted for clarity.
Figure 10:
We hypothesize that MolSnapper's performance is limited in this context due to the nature of its conditioning mechanism. MolSnapper is designed to strictly satisfy set of discrete, fixed pharma- cophore points, which acts as "hard" constraint on the generation. While this effective for tasks with a few key constraints, providing dense set of points derived from large, complex natural product may be overly restrictive, making it difficult for the model generate novel and syntheti- cally accessible scaffolds that satisfy all points simultaneously. This highlights the challenge of the NP hopping task and the effectiveness of conditioning strategies that allow flexibility and capture more holistic 3D information.
Table 23: Performance summary for the MolSnapper baseline the NP hopping tasks after xTB geometry relaxation and ESP alignment. The table reports success rate (percentage of 2,500 samples passing validity and SA < 4.5 filters), SA score, and top-10 similarity scores.
NP
Method Success Rate t Score
ESP sim (top 10) f
Pharma sim (top 10) t
NPI
MolSnapper 0.32%
4.23 0.36
0.32 + 0.04
0.07 0.05
NP2
MolSnapper 0.16%
4.22 0.39
0.46 +
0.09 0.02
NP3
MolSnapper 0.32%
4.06 0.43
0.22 + 0.07
0.06 + 0.01
Table 24: Performance summary for the MolSnapper baseline on the NP hopping tasks after xTB geometry relaxation and ESP alignment. NP
Method
Valid SA Score ESP sim (top 10) Pharma sim (top 10) 1
NP1
MolSnapper
26.24%
6.43 0.78
0.51 0.02
0.16 0.02
NP2
MolSnapper
19.88 %
6.84 + 0.79
0.63 + 0.02
0.19 + 0.01
NP3
MolSnapper
14.20 %
6.32 + 0.85
0.46 + 0.02
0.14 0.01
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F BIOISOSTERIC FRAGMENT MERGING: EXTENDED EVALUATION
This section provides a detailed evaluation of the EV-D68 3C bioisosteric fragment-merging task. We assess molecule quality across synthetic accessibility, conformational stability, chemical validity, and performance against relevant baselines.
F.1 EXPERIMENTAL DETAILS
Data and Setups. The input data, sourced from the Fragalysis platform consisted of 13 exper- imentally resolved froumeet-bouud structures previously curated by SHEPHERD (Adams et al.] 2025). Across all setups, generated molecules ranged from 50 to 89 atoms. From this set, the pro- tein conformation from the D -000100000000000--- and used for all subsequent docking simulations.
complex was randomly selected
MULTI-FRAGMENT REFERENCE. For this setup, we created three distinct composite inputs, each by combining three of the known fragment ligands. We generated 400 candidate molecules for each of the three combinations (1,200 total per method). To allow for the generation of scaffolds larger than the combined fragment set, the input for Replacement-giee setecemmen--guddance was padded with additional atoms. Conversely, for Interpolate- of the input fragments due the fixed dimensionality of the interpolation trajectory. This selection was subject to spatial constraints to ensure physical realism: the sampled atoms were kept at least 1 Á apart.
the target size was restricted to the exact atom count
RANDOM ATOM SEEDING. The conditioning profile was created by randomly selecting subset of pharmacophore features from the full fragment library. This selection was subject to spatial constraints ensure physical realism: hydrogen bond features were kept at least 1 Á apart, and aromatic centers at least Å apart. The remaining atoms for the seed were sampled from the broader pool of fragment-derived structures with minimum distance of Á between all atoms. For this setup, we generated 1,000 candidate ligands per method.
FUL.I INTERACTION PROFILE. For the manually curated setup, we adopted the 27-feature phar- macophore profile reported in the SHEPHERD paper (Adams et al. (025). This profile, which was originally extracted from Fragalysis annotations, represents the aggregate interaction features of the fragment ensemble. (The SHEPHERD baseline also used averaged ESP field, which our methods do not require). To use this profile as a seed for our generative models, we first converted the 27 abstract features into concrete atomic representation (an SDF file) by instantiating specific atoms (e.g., Nitrogen for an HBA) at the given coordinates. This 34-atom set formed the core of our seed. To build full-sized seed matching our generation range (50-89 atoms), supplemented these 34 atoms with additional "filler" atoms. These filler atoms were randomly sampled from the original set of 13 fragments, ensuring minimum distance of 1 Á between all atoms to maintain physical realism. We generated 1,000 candidate ligands per method using this full, supplemented seed.
Generation Parameters. Across all setups, generated molecules ranged from 50 to 89 atoms. We used our standard conditioning parameters: Interpolate-Integran was with restart time of 7 = 0.75, and Replacement Guidance was run with a relaxation time of frelax = 1.0 without conditioning the bonds.
Evaluation Pipeline. All candidate molecules in this section were evaluated using standardized
pipeline. Following generation, each ligand was subject to: (1) geometry relaxation using xTB, (2) alignment to the reference fragment constellation based on electrostatic potential (ESP), and (3) in-pocket energy minimization and scoring using AutoDock Vina with 5 Á buffer.
Baseline Comparability. Existing baselines (e.g., MOLSNAPPER, DIFTSBDD, SHEPHERD) cannot operate directly on raw fragment collections or sparse 3D feature clouds. They require single, well-defined input structure or pharmacophore profile conditioning. For this reason, they can be evaluated only under the manually curated Full Interaction Profile setup, where the fragment
https://fragaayst s. amond ac. wtt.:att//wwweeeeeeeeeeeeceeeeee
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information is consolidated into unified profile. is not clear how these baselines could be ap- plied to the other two setups setups-Mullt fragment Reference and Random Atom Seeding and therefore they cannot be fairly compared in the automated setups.
F.2 COMPARISON OF CONDITIONING SETUPS (UNFILTERED)
We first compared the unfiltered performance of our two methods across the three conditioning setups to understand their inherent properties. Table 25 summarizes the results for all generated molecules that passed initial PoseBusters validity checks, prior to applying the synthetic accessibility filter. This unfiltered analysis reveals the inherent tendencies of two strategies. Interpolate- integrate consistently generates a higher percentage of geometrically valid molecules. In contrast, the molecules generated by cetticement-guidddaee scores and stronger Vina scores. This highlights a trade-off between generating a high quantity of valid structures versus higher quality of drug-like -ike candidates.
while fewer number, tend to have better SA
Performance comparison of interpoiate-Interree and Replacement Guidance across three input conditioning setups for the EV-D68 3C merging task. We report PoseBusters validity, clash rate (fraction of molecules with zero steric clashes), SA and top 10 Vina scores.
Table 25:
Conditioning setup Multi foltm--fragent ref.
Method
Valid (%)
No clash (%)
SA
Vina top J
interpolateiittegaaee
59.3%
47.2
4.65 +: 0.96
-6.00 +: 0.36
Replacement-
36.3%
26.8
3.57 :: 0.85
5..74 1 0.22
Random atom seeding
pnerpolate-integnae sxplaxcement-guidane
41.0%
31.2
5.18 = 1.17
5.45 + 0.20
28.1 28.1%
20.7
3.84 0.81
6.00 + 0.31
Full interaction profile
Interpolate-integrate Replacement-guuddacce
38.1%
30.2
4.83 = 1.17
55.52 + 0.18
31.9%
25.4%
3.47 = 0.70
-6.70 0.39
F.3 DETAILED COMPARISON ON THE FULL INTERACTION PROFILE
This section provides a deeper analysis on the manually curated 'Full Interaction Profile' setup, which allows for direct comparison against multiple external baselines.
F BASELINE METHODS
This section benchmarks our methods against the most relevant existing approaches. is impor- tant note that our task of flexible, abstract bioisosteric merging is novel, and these baselines not designed for this exact scenario. SHEPHERD is designed for a similar task but requires pre- computed grids, while MOLSNAPPER and DIFFSBDD are atom- cooo-presrriing linkers/inpainters. They are, however, the closest available methods for comparison.
Although our methods are ligand-only, we also benchmark against pocket-conditioned SBDD meth- ods in this section, our input fragment poses contain implicit information about the binding site.
We evaluated three distinct baselines:
Adams et al. (2025) The primary baseline, conditioned on the interaction profile and ESP in a figand-only setting. It is also designed for bioisosteric merging.
SHEPHERD:
MolSnapper: Ziv et al. (2025) An atom-preserving conditioning mechanism. We evalu- ated MoISnapper in two settings: protein-blind mode (' ("clash-rate=0") and a protein-aware mode ("clash-rate=O.1) 1') using the explicit protein structure.
Schneuing et al. (024) An atom- poon-preeriing inpainter. We used the official inpainting script with checkpoints pre-trained on the CrossDocked dataset. As structure- based method, it conditions on the protein pocket geometry while using the provided phar- macophore points a reference context to guide the inpainting.
DiftSDD:
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To adapt the atom-based baselines (MOLSNAPPER, DIFFSBDD, and ours), converted the 27- feature abstract profile into the same atom SDF file described in Section F.I MOLSNAPPER, DIFFS- BDD were given only the SDF file imput (in addition to pocket info)
Both MolSnapper and DifiSBDD do not explicitly generate hydrogens. To account for their limita- tions, generated 1,000 molecules for each within heavy atom range of 36-46. For Dif as no fully connected molecules were produced, we selected the largest resulting fragment for eval- uation.
F.3.2 VALIDITY AND FILTERING PIPELINE
Table 26 details the validity rate of the methods at each stage of our filtering pipeline for the 'Full Interaction Profile" setup. While guidance ultimately produces a greater final yield of candidates passing all filters.
integrate shows high initial validity, Replacement-
Table 26: Validity assessment of generated molecules for the bioisosteric merging task for EV-D68 3C (ShEPhERD profile setup). Each method generated 1000 molecules. We report the percentage of molecules that (1) are valid according to RDKit parsing, (2) pass PoseBusters (PB) geometry checks prior xTB relaxation, (3) pass all PoseBusters checks after xTB geometry relaxation and ESP alignment, and (4) additionally have a synthetic accessibility (SA) score 4.
Method
Valid (RDKit)
PB Valid
xTB
PB Valid
PB Valid + SA
DiffSBDD (largest frag)
99.8%
0%
43.1%
0%
MoiSnapper (ligand-based)
31.1%
0%
17.6%
0%
MolSnapper (clash-rate 0.1)
30.4%
0%
14.5%
0%
ShEPhERD
45.1%
29.9%
33.8%
10.8%
Interpolate-integee
52.2%
32%
39.5%
8.7%
Replacement guidance
39.7%
21.8%
31.9%
23.5%
F.3.3 PERFORMANCE COMPARISON
Table 27 summarizes the unfiltered performance of our methods against the baselines. The results highlight that the conditioning mechanisms of DIFFSBDD and MOLSNAPPER are less suited for this bioisosteric merging task, yielding molecules with poor SA and Vina scores. Among the top- performing methods, comparable performance in Vina scores and in generating valid, clash-free molecules. However, they exhibit distinct strengths in other metries: Replacement seveemenntgudancee guidance produces candidates with the best synthetic accessibility (SA score of 3.47), while SHEPHERD achieves the highest pharmacophore similarity.
-interratee Replacement-guuddanee and SHEPHERD all achieve
Table 27: Performance summary for molecular generation in the EV-D68 3C bioisosteric merg-
ing task (ShEPhERD profile setup), after xTB geometry relaxation and ESP alignment. We report PoseBusters validity, clash rate (fraction of molecules with zero steric clashes), SA score, top-10 pharmacophore similarity and top Vina scores.
Target
Method
Valid
Na clash
SA
Pharma sim (top 10)
Vina 10) L
DV8 3C
DiISBDD (largest frug)
43.1%
38.0%%
6.91 1: 0.53
0.1 0.01
-4.35 1: 0.33 0.35
MolSnapper (ligand-hasedd hased)
-4.56
MolSnapper (clash-rate 0.1)
14.5%
13.9%
6.91 + 0.53
0.11 0.01
-4.99
SlEPHERD
33.8%
29.8%
4.45
0.99
0.3
-6.74 0.29
Incerpolat-iinternne Roplacemant-guidance
39.5%
30.8%
4.96
1.17
0.19 0.001
-5.95
31.9%
25.4%
3.17 + 0.70
0.23 1:23:00 0.02
-6.70 0.39
Figure 11 shows the distribution of Vina scores for all generated molecules. Panel (a) displays the full range, while panel (b) visualizes the same set of scores but truncated at a Vina score of 5 for clarity, removing only extreme outliers.
Figure 13 provides a detailed comparison of the structural similarity characteristics captured by each method. Panel (a) measures ESP and pharmacophore similarity the merged fragment template af-
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Table 28: Molecular property statistics for molecular generation in the EV-D68 3C bioisosteric merging task (ShEPhERD profile setup), after xTB geometry relaxation and ESP alignment. We report molecular weight, Lipinski violations, logP, QED, number of rings, and SMILES uniqueness.
Method
Weight [Da]
Lipinski R5
log!
QED
# Rings
% Unique SMILES
DifSBDD (largest frag)
229.01
4.97
0:21
0.6
0.95
33.7%
MopSnupper (ligand based)
207.29
24.51
4.94 0.23
-0.30
1.05
0.29 1: 0.10
3.23 1: 0.90
48.6%
MolSnupper (elash-rate 0.1)
214.27 44.04444.0
4.93 0.28
-0.42 1.17
0.08
3.21 0.09
38.9%
SbEPhERD
171.14 37.92
4.32 0.75
3.79
0.33
0.11
5.37 + 1.25
100%
Cappolate-Integruee
472.00 48.47
3,88
2.66
0.31
+
100%
Replacement-Guune Guidance
503.52
51.09
1.01
0.87
1,09
009:100 1.52
0.26 = 0.11
4,68 = 1.01
100%
Caperr
(a) Vina score distribution for all generated molecules (full range).
(b) Same Vina scores, truncated at score of to improve visualization.
Figure 11: Docking performance on the EV-D68 3C fragment- benchmark. Lower Vina scores are better.
ter xTB relaxation and ESP alignment, capturing how well the generated molecules preserve the electrostatic and shape complementarity implied by the input fragments. Panel (b) shows the phar- macophore similarity to the full target profile the docked molecules, reflecting whether the final poses recover the key 3D interaction features expected at the binding site.
Finally, Figure 12 characterizes pairwise chemical diversity, synthetic accessibility, and confor- mational stability. For DIFFSBDD and MOLSNAPPER, RMSD could not be computed because generated structures changed graph topology during relaxation, which prevented consistent atom-atom mapping between the pre- and post-relaxed geometries when using the SHEPHERD- SCORE RMSD code. The pairwise Tanimoto distributions confirm that Interpolate- integrate and Replacement -uiddanc explore chemically diverse set of merges. Replaceeennt-guidaee the most synthetically accessible candidates (lowest SA scores), while interpolate-interral exhibits the lowest RMSD between xTBBrrllxxed and original geometries, indicating the most geometrically stable poses. Together, these results echo the trends seen in the NP-hopping task: Replacement- guidance favors diverse, synthesizable molecules, whereas Interpolate-integraut optimizes more aggressively for geometric regularity; SHEPHERD sits between them, offering the strongest phar- macophore recovery.
yields
F.3.4 ANALYSIS OF CONDITIONING BEHAVIOR
As shown in Table 26 the proportion of fully valid and synthesizable molecules on the 27-feature abstract consensus profile highlights how different conditioning strategies respond respond to this specified manually curated input derived from the underlying fragment set.
For the Full Interaction Profile setup, the methods (MOI.SNAPPER, DIFFSBDD and ours) received the same conditioning input: SDF file representing the 27-feature abstract profile, instantiated as a set of atoms to serve as the conditioning seed. The divergent outcomes stem from a fundamental difference in how each method handles these constraints:
Methods like MOLSNAPPER and DIF are designed as atom- coompreseriing linkers or inpainters. Their conditioning mechanisms function as "hard constraints." enforcing that the generated molecule contain the exact seed atoms in the output. This approach effective for tasks like linking two known fragments, growing fragment, or satisfying constraints from single known ligand. How-
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(a) Pairwise Tanimoto similarity between generated molecules.
(b) SA score distribution across methods.
(c) RMSD between xTB-relaxed and aligned conformers vs. original 3D geometries.
Figure 12: Diversity, synthetic accessibility and conformational stability for the EV-D68 3C fragment-merge benchmark.
method
Snapper aahvaa
Intoprate ihrppciioo
5.4
0.5
0.8
Phamacophoree Sime barty
(a) ESP pharmacophore similarity the combined fragments after ESP alignment.
(b) Pharmacophore similarity density the target profile after docking.
Figure 13: Similarity to the reference ligand for the EV-D68 3C fragment-merging task (27-feature profile).
ever, the 27-feature profile is holistic, abstract consensus of many fragments, not a single, physi- cally plausible molecule. It is therefore chemically challenging to satisfy all 27 dense atomic straints simultancously while also forming valid, connected, low-energy molecule. As shown in Figure (a) (a) and (b), this hard-constraint approach struggles produce single, coherent structure from this abstract input, resulting in disconnected fragments and invalid chemistry.
In contrast, our conditioning strategies are designed to handle such abstract, bioisosteric tasks. Replacement-guiee guidance uses a "relaxation" mechanism that uses the dense profile as strong guide during the generative flow, but then releases the hard constraint in the final integration steps. This allows the model to form final, coherent molecule that is chemically valid and bioisosteric to the abstract profile, rather than attempting to be identical to (Figure [4 (c)). Similarly, Interpolate- integrate treats the input "soft" global seed rather than enforcing its exact atomic identity. While SHEPIERD also achieves success by using a more holistic conditioning mechanism (aggregate ESP and pharmacophore grids).
F.4 DETAILED POSEBUSTERS VALIDITY
Tables 29 30 and 3] provide detailed breakdown of molecular validity for the fragment merging task. Table 29 reports overall generation success and the percentage of molecules passing both chemical and physical filters. Table 30 decomposes chemical validity into sanitization, hydrogen compatibility, valence, and connectivity checks. Table 33 presents results from the PoseBusters suite, including bond lengths, angles, steric clashes, and energetic plausibility after xTB relaxation.
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(a) MOLSNAPPER
(b) DIFFSBDD
(c) Replacement Guidance
Visualizing the behavior of different conditioning methods the 27-feature curated profile. Magenta spheres represent the conditioning points. (a, b) MOLSNAPPER and DITSSDD enforce them rigidly and fail to produce connected molecule. (c) Our Replacement Guidance relaxes the constraints and yields connected bioisosteric molecule.
Figure 14:
Table 29: Validity of the generated and ground truth molecules for the bioisosteric fragment merg- ing task for EV-D68 3C (ShEPhERD profile setup). The table contains the total number of molecules that was to be generated or that successfully passed the XTB process and alignment and the propor- tions of molecules that pass all the chemical validity test, all physical validity tests. The last columns is the percentage of molecules that pass all the tests together.
Method
% Generated
% Chemical
% Physical
% Valid
Interpolate integrate
42.4
42.4
39.5
39.5
Replacement-guidaat
35.3
35.3
31.9
31.9
Components of the chemical validity of the generated molecules for the bioisosteric fragment merging task for EV-D68 3C (ShEPhERD profile setup). Each entry shows the percentage of molecules passing individual chemical validity checks. The final column (Chemical) indicates the proportion of molecules that pass all checks. These tests assess only the molecular graph (c.g., sanitization, radicals, hydrogen completion), not the 3D conformation.
Table 30:
Method
% Gen.
% Sanit.
% H-comp. No Rad.
Conn.
% Chem.
Interpolate-integraae
42.4
42.3
42.3
42.3
42.3
42.4
Replacement-guidan 35.3
35.2
35.2
35.2
35.2
35.3
Components of the physical validity of the generated molecules for the bioisosteric fragment merging task for EV-D68 3C (ShEPhERD profile setup). The numbers shown are the percentages of the molecules that pass each of the intramolecular PoseBusters tests. The last column is the percentage of molecules that pass all of the intramolecular tests. The physical tests check the 3D conformations of the generated molecules.
Table 31:
Method
Gen.
Bond Len. % Bood Ang.
Clash % Arom. Flat.
% DB Flat.
Int. Energy
Physical
Interpolate integrate
42.4
41.9
42.2
41.4
42.3
41.4
41.2
39.5
Replacement-guidaee guidance
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G PHARMACOPHORE-MERGIN ON SARS-COV-2 MrRO: EXTENDED EVALUATION
This section provides detailed evaluation of the SARS-CoV-2 Mpn Mharnacophorr-mergye task. We assess the quality of generated molecules by comparing their predicted binding affinity against strong baseline of larger, known binders for the same target.
G.1 EXPERIMENTAL DETAILS
Datasets.
The data for this task was curated from the Fragalysis platform We created two distinct
sets:
Conditioning Set: A set of 81 small, non-covalent fragments (MW 300 Da) from the initial Diamond/XChem screen.
Baseline Set: set of 1,426 larger, known binders (MW 300 Da) for M This set serves a challenging, real-world baseline for evaluating the predicted binding affinity of our generated molecules.
The full names for the conditioning set fragments, taken directly from the input SDF file, listed below:
7gf9 A 404 1 SARS2 MprroA-00011144444111 1,6
Vgłv A 404 1 SARS2 40 IIG
5rek A 404 SARS2 [B24122|praa-yy1111:::4401111110
Arfw A 21000010112aaaak-011111:----1111
7gcm.A.403.1.AAA
7gdr A.46 1at.::011..k22pprrrrr:0111444111
7gły A 404 240.111882222200:2-1111--------3
404
5rhd A 408 SAR32
A.06_11
tas/1b32627-272
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5rez A 404 41111111110///pp----111111111111
7geo A.404 2at.2220......a...parrrla1122211
5rew 404 SAR32 144422224rr--1111111444441111110
7gcp A 405 1 3 IIG
Ar 3rte240001..les///pprrr00-111111
5rhf A 405 40011188222MM0022-01111--:-----1
5r84 1001 SARS2 MproA-y0118@A4+4100 1.16
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Generation and Evaluation. We generated 2,500 candidate molecules per method, with total time of T = 0.75) and Replacement Guidance (with relaxation time tees = 1.0 without conditioning the bonds). The total atom count range of 40 to 80 was chosen to match the core distribution of the 1,426 known Mpro binders, shown in Figure [3] The generation was conditioned by sampling pharmacophore features from the 81-fragment conditioning set, following the 'random atom seeding' setup (Section 4.3). where each seed constructed by sampling atoms from 81-fragment conditioning set: half of the atoms were randomly se- lected pharmacophore features, while the remaining half were non- pharmacophore atoms. The pharmacophore selection was subject to the spatial constraints described in Appendix F.I to ensure physical realism.
counts between 40 and 80, using our conditioning strategies: emerpolate-Integrae (with
For evaluation, both our generated molecules and the 1,426 baseline binders were docked using an identical AutoDock Vina protocol (--minimize mode, 5 Á buffer). For this protocol, the rigid receptor structure was taken from randomly selected crystal structure, SA aARS5b0014 which defines binding pocket for all docking runs. Note that for this task we evaluated the docked geometries directly, without applying xTB relaxation or ESP alignment prior to scoring.
200
00318
300
Figure 15: Distribution of total atom counts (including hydrogens) for the baseline set of 1,426 known binders for SARS-CoV-2 Mpro. The generation range of 40-80 atoms selected cover the main peaks of this distribution.
G.2 VALIDITY AND FILTERING PIPELINE
Table 32 details the validity of our methods at each stage of the filtering pipeline. Both methods demonstrate high initial validity, but Replacement-guiieee produces higher final yield of candidates that pass the synthetic accessibility score filter.
Table 32: Validity assessment of generated molecules the pharmacophorr-meering task on
SARS-CoV-2 Mpro- Each method generated 2,500 molecules. We report the percentage of molecules that: (1) are valid according to RDKit parsing, (2) pass all PoseBusters (PB) geometry checks, (3) pass PB and successfully docked, and (4) additionally satisfy synthetic accessibility (SA) score
< 4.5.
Method
RDKit Valid
PB Valid
PB + Docked
PB + Docked + SA < 4.5
Inteeral
63.8
46.0% %
41.1 %
23.5%
ReplacementtCCuidance
51.5%
39.7%
32.2%
27.3 %
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G.3 INTERACTION RECOVERY ANALYSIS WITH PROLIF
To quantitatively assess whether our conditioning strategies effectively transfer interaction patterns from the input data to the generated molecules, performed an interaction recovery analysis. The objective of this analysis was verify that the pharmacophorie information present in the conditioning set was successfully encoded and reproduced by the generative models. This was accomplished using the ProLIF (Protein- Ligand Interaction Fingerprints) package (Bouysset & Fiorucci 2021) to create detailed interaction profiles for both the conditioning set and the generated molecules, allowing for a direct comparison of their interaction pat- terns (Errington et al. 2025). Following Errington et al. (025), we considered only hydrogen and halogen honds (donor and acceptor), r-sttcking, cation-w and TT-atton interactions, and ionic interactions (anionic and cationic), while excluding the less specific hydrophobic interactions and Van der Waals contacts.
First, we established a reference interaction profile by characterizing the complete set protein-ligane in- teractions for the 81 fragments in the conditioning set against the SARS-CoV-2 M target (SARS-a05 104))..... This step yielded reference set of 20 distinct interaction types. We then evaluated the entire population of molecules generated by both the Interpolate -nttge-tteeeeeeeeeeeee and Replacement Guidance methods. For each generated molecule, we used ProLIF to determine which of the 20 reference interactions successfully reproduced.
The analysis confirmed that both conditioning methods were effective at exploring the desired interaction space. We found that the generated populations, in aggregate, were capable of reproducing the complete set of 20 reference interactions found in the original conditioning data. This result indicates that inference-tine guidance successfully generate set of candidates for lead generation, serving as starting points for drug discovery campaign.
HOU
teti
(a) Interaction recovery for interpolate-nnterraan
(b) Interaction recovery for Replacement Guidance.
Figure 16: Histograms showing the recovery frequency of 20 reference interactions for molecules generated by (a) Interpolate-Integraie and (b) Replacement Guidance. The y-axis lists the specific protein-ligand interactions from the conditioning set of 81 Mp fragments, and the x-axis shows the number of generated molecules that successfully reproduced each interaction.
We subsequently performed the same interaction recovery amalysis on a more stringently filtered subset of molecules, including only those that passed all PoseBusters validity checks and were successfully docked using cDock Vina in -n-minnima ze mode, with a 5 Á buffer. This second analysis, shown in Figure 17 serves to confirm that the models reproduce the target interactions not just in any conformation, but specifically in poses that both chemically plausible and have favorable predicted binding geometry. The results from this filtered subset further validate ability of our methods to generate high-quality candidates that retain the essential binding features of the conditioning data.
G.4 PERFORMANCE ON UNFILTERED CANDIDATES
Table 33 summarizes the final performance metrics for all valid generated molecules, compared against the baseline set of known binders.
G.5 DETAILED POSEBUSTERS VALIDITY
Table 35 summarizes the validity metrics for the pharmacophore merooongeeeeiigggeee task on SARS-CoV-2 M We
report the percentage of generated molecules that pass all chemical validity checks (e.g.. sanitization, valence), physical plausibility checks via PoseBusters (after xTB relaxation and ESP alignment). and the intersection of both. Interpolate-Integnat yields higher overall validity, while Replacement Guidance shows competitive chemical correctness and stronger SA-filtered outputs (see Appendix for extended analysis).
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Published as a conference paper at ICLR 2026
#31C Pett
H011
18330
(a) Interaction recovery for Interpolate Integraae.
(b) Interaction recovery for Replacement Guidance.
Figure 17:
Histograms showing the recovery frequency of 20 reference interactions for PoseBusters Valid and docked molecules generated by (a) Anlerpolate-Integratt and (b) Replacement Guidance. The y-axis lists the specific protein-ligang interactions from the conditioning set of 81 Mpro frag- ments, and the x-axis shows the number of generated molecules that successfully reproduced each interaction.
Table 33: Performance summary for the SARS-CoV-2 Maro pharmacophore-merihnn task. The table reports overall PoseBusters validity (Valid), the percentage of initial samples that are valid and clash-free (No clash), the average unfiltered SA score, and the top- 10 Vina docking score.
Target
Method
Valid (%) f
No clash (%)
SA
Vina top10
Interpolatc-integrate Replacement-guidance
41.0%
24.0%
4.49 1.20
-6.67 + 0.13
SARS-CoV-2 Mp
32.2%
13.6%
3.61 + 0.90
-7.70 4: 0.37
Known Binders (Baseline)
100%
84.1%
3.70:F0.99
10.03 F0.12
Table 34: Molecular property statistics for the SARS-CoV-2 Mp pharmacophore-merging task, computed over PoseBusters-vaal molecules. We report molecular weight, Lipinski rule-of-five vio- lations, logP, QED, number of rings, and SMILES uniqueness.
Method
Weight [Da]
Lipinski R5
logP
QED
# Rings
% Unique SMILES
interpolate-integraal
460.47 79.92
3.98 1.22
2.01 + 2:40
0.36 + 0.19
4.53 + 1.51
100%
Replacement-guiddan
507.16 97.87
4.05
0.98
3.10
0.30
4.62 1.40
100%
Table 35: Validity of the generated molecules for the pharmacophore-monn merging task on SARS-Co* 2 Mpr-- Each method attempts 2,500 generations. We report the percentage of generated molecules that pass all chemical validity checks, all physical validity checks, and the percentage that pass both.
Method
Generated % Chemical
% Physical
% Valid
Interpolate-Integrat
51.3
51.3
41.0
41.0
CeplacemeettGGiddanc 48.0
48.0
32.2
32.2
Although our method operates in protein-blind setting, we further assessed the spatial plausibility of generated molecules by docking them into SARS-CoV-2 Mç using AutoDock Vína in --mi ze mode, with a buffer centered on the reference fragment ensemble. As a post hoc sanity check, we computed the percentage of SA-filtered molecules (i.e., those passing xTB + PB checks and SA < 4.5) that do not exhibit steric clashes following docking.
H LIMITATIONS
Our framework, while flexible, has several limitations that provide avenues for future work.
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Published as a conference paper at ICLR 2026
Table 36: Components of the chemical validity of the generated molecules for the pharmacophore- merging task on SARS-CoV-2 Mpo. Each entry shows the percentage of molecules passing indi- vidual chemical validity checks. The final column indicates the percentage that pass all chemical checks.
Method
% Gen.
% Sanit.
% H-comp.
% No Rad.
% Conn.
% Chem.
Interpolate-Integrate
51.3
51.2
51.2
51.2 48.0
51.2 48.0
51.3
Replacement -uudenn
48.0
48.0
48.0
48.0
Table 37: Components of the physical validity of the generated molecules for the pharmacophore- merging task on SARS-CoV-2 Mp Each entry shows the percentage of molecules passing indi-
vidual PoseBusters checks after xTB relaxation and ESP alignment. The final column shows the percentage that pass all physical checks.
Method
Gen. % Bond Rond Ang.
& Clash
% Arom. Flar.
% DB Flat.
Int. Energy
Physical
Interpolate- Integrate
51.3
50.0
50.3
48.4 43.0
51.2 47.6
46.4 42.1
49.4 46.5
41.0
Roplacement-
48.0
42.5
45.8
32.2
Dependence on the Conditioning Signal. Both conditioning strategics depend on the structure and density of the input information. When the seed is well-formed and chemically coherent (c.g., full ligand in the NP hopping task), validity is naturally higher because the model starts close the data manifold. Conversely, when conditioning input dense, abstract, or highly fragmented (e-g., many disjoint features or large merged profiles), the model must resolve more ambiguity, which can reduce validity. This reflects an inherent trade-off between flexibility of conditioning and difficulty of reconstruction.
Fixed Dimensionality Requirement Interpolate-Integratee Unlike Replacement Guidance, which can operate on variable-sized inputs via muskinggpadddngg Interpolate-Integruue requires the seed to share the exact dimensionality of the output. This is because the interpolation formula æ,, + +1 7)ro operates the global state. This makes Interpolate-Interre less suitable for tasks like linking distant frag- ments, where the required linker size is unknown requires dynamic padding that the interpolation path cannot casily accommodate.
Seed Construction and Fixed Atom Count. Our methods operate fixed tensor size, which requires an explicit sampling step at beginning inference to define the target atom count and composition (e.g.. by subsampling a reference molecule or adding "filler" atoms to fragment set). While this seed construction automated, is prerequisite for generation.
Evaluation Proxies. Our evaluation using docking scores relies on rigid-receptor docking (e.g.. AutoDock Vina) for both the bioisosteric merging and Mp tasks. This protocol neglects protein flexibility and induced fit. This can unfairly penalize valid binders (including known ligands) that require minor backbone adjustments, or underestimate the potential of generated bioisosteres. Therefore, the reported Vina scores should be interpreted measures of geometric complementarity rather than definitive binding affinities.
I STATEMENT ON LARGE LANGUAGE MODEL (LLM) USAGE
accordance with the conference policy, report the large language model (Google's Gemini and GPT, OpenAI) the preparation of this manuscript. LLM''s role was strictly that a general-purper writing and programming assistant; did not contribute to the core research ideation, experimental design, or the development of the proposed methods.
REPRODUCIBILITY STATEMENT
To ensure reproducibility, we provide all experimental details in the main text and appendices. This includes descriptions of our datasets, evaluation metrics (Section and conditioning setups (Appendices E and G Our open-source code available at h
sem1 and all processed
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Published as a conference paper at ICLR 2026
data and generated molecules from experiments available on Zenodo httpp::/ //cp:::://d....// org/IC.
5281/zenodo. 17521477 The repository includes the scripts required to reproduce our main results.
41


Reviews:
[{"review_id": "i4YLYS0Jdo", "reviewer": "Reviewer_vGwq", "summary": "The paper proposes two training-free, inference-time conditioning strategies—Interpolate–Integrate (a mid-path restart on a flow-matching ODE) and Replacement Guidance (hard anchoring and optional relaxation of user-specified fragments)—built atop SemlaFlow, an E(3)-equivariant flow-matching generator for 3D molecules. The methods target three ligand-only design tasks: (1) natural-product ligand hopping, (2) bioisosteric fragment merging on EV-D68 3C protease, and (3) pharmacophore merging for SARS-CoV-2 Mpro. Evaluation uses PoseBusters validity, synthetic accessibility (SA), 3D similarity (shape/ESP/pharmacophore), and rigid-receptor AutoDock Vina. Reported results show stronger \"success rates\" and/or top-10 docking among filtered candidates compared to several baselines in selected setups; code is provided via an anonymous link.", "questions": "See the weeknesses.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Both controls operate at sampling time without retraining, which is practically attractive for iterative design. Interpolate–Integrate is clearly specified as a mid-path restart with a tunable τ that trades fidelity vs novelty; Replacement Guidance formalizes fragment anchoring with user-controlled relaxation.\n- The paper demonstrates three realistic tasks (NP hopping; EV-D68 fragment merge; Mpro pharmacophore merge) with metrics spanning SA, PoseBusters validity, 3D similarity, and docking.\n- Sensible $\\tau$ / $\\phi$ ablations illustrate controllability and the fidelity–novelty trade-off.\n- An anonymous repository link is provided.", "weaknesses": "- \"Success rate\" and similarity/docking are reported after SA and PoseBusters filtering; key tables focus on the top-10 among successful molecules, which inflates perceived gains and obscures unconditional quality and sample efficiency. Some comparisons are only feasible under a manually curated setup (Full Interaction Profile), limiting fairness and generality across methods. Please provide full, unfiltered distributions and uniform pipelines across baselines. \n- For Mpro, docking uses a single rigid receptor; improvements in top-10 Vina (filtered) do not establish binding or pose realism and are fragile to receptor flexibility and scoring-function bias. No physics-based refinement, ensemble receptors, or wet-lab validation is provided.\n- The EV-D68 analysis mixes multiple conditioning regimes; authors note a direct comparison is only possible for the curated profile. This undermines cross-method conclusions; several baselines show zero success under the authors' pipeline, suggesting a setup mismatch rather than method inferiority. Re-run all methods under the same input specification and filters.\n- SA and PoseBusters are necessary but insufficient; there is no report of synthesizability beyond SA (e.g., route-based metrics), drug-likeness/ADMET proxies, stereochemistry handling, or diversity/novelty vs bioactivity retention across the full set. The evaluation would benefit from route-planning feasibility and multi-objective profiling.\n- Interpolate–Integrate is positioned as the first mid-path restart for flow-matching models, but its analogy to diffusion editing is acknowledged; the empirical section does not clearly isolate a capability that cannot be matched by established editing/guidance in diffusion with comparable compute. Stronger head-to-head controls are needed.\n- Replacement Guidance projects the ODE step back onto a masked manifold via hard replacement; this projection can disrupt the learned flow and potentially induce artifacts (e.g., broken connectivity) unless carefully relaxed. The paper mentions user-chosen relaxation but lacks theoretical guarantees or stability analyses beyond small ablations.\n- All tasks are ligand-only. There is no demonstration of pocket-conditioned SBDD or cross-target transfer where protein contexts drive constraints—limits the scope of the claimed “bioisosteric design” advantages. (The EV-D68 and Mpro studies still evaluate with docking rather than explicit pocket-aware generation.)", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "HRTWuTfzbX", "reviewer": "Reviewer_cV2Y", "summary": "This submission investigates the problem of ligand-based drug design (LBDD) and proposes two inference-time conditioning methods for a given, trained molecule foundation model. Method Interpolate-Integrate takes a seed ligand as input and generates a similar one, starting in the middle of a probability path and first taking steps toward the noise and then integrating forward toward the data. Method Replacement Guidance takes a set of fragments as input and generates a ligand that preserves key interaction patterns of the fragments. The methods build on SemlaFlow (Irwin et al., 2024).Experiments were performed for three important tasks of drug discovery, i.e. natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging. The proposed methods were compared against SHEPHERD, which conditions on shape, ESP, and pharmacophore grids.", "questions": "1) What is novel here beyond applying the replacement/masking approach to a flow matching model? \n\n2) Several questions regarding Interpolate-Integrate:\n* Atom count control: How do you control the number of atoms in the generated molecule?\n* Figure 1 conformations: Are the 3D shapes shown from SemlaFlow's generation, or did you run RDKit conformation generation afterward? This matters for understanding what the model actually produces.\n* Seed molecule validity for fragment merging: For the fragment merging task, how are seed molecules constructed?\n    * Multi-fragment reference: Are you literally concatenating 3 disconnected fragments? That's not a valid molecule.\n    * Random atom seeding: \"Filling in remaining atoms\" is completely underspecified. What does this seed actually look like?\n    * Full interaction profile: Instantiating individual atoms at pharmacophore coordinates gives you isolated atoms, not a connected molecule.\n* The flow matching model was trained on valid molecules from GEOM-Drugs. How does the interpolation path even work when z₁ is chemically invalid or disconnected?\n\n3) What are the \"architectural constraints\" preventing Interpolate-Integrate from handling padded inputs like Replacement Guidance can?\n\n4) How do you prevent fragment overlap when combining them?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "Ligand-based drug design is an important paradigm in drug discovery.\n\nBeing able to repurpose pre-trained unconditional models without retraining is practical and efficient. \n\nBoth proposed methods seem computationally cheap to add on top of the base model.", "weaknesses": "The replacement/masking approach has been used in previous works like DiffSBDD for inpainting. The paper should better distinguish its contributions compared to related work.\n\nI have a concern regarding the soundness of the Interpolate-Integrate Method, in particular regarding the seed molecule validity for fragment merging.  I am not convinced the method works properly when given invalid/disconnected seeds.\n\nI have another concern regarding the soundness of Interpolate-Integrate: the method does not really enforce similarity in shape and pharmacophore specifically. It just makes molecules similar to the original seed when τ→1. That is different from preserving specific interaction features. You are getting global similarity, not targeted preservation of binding determinants.\n\nIt's unclear how SA is actually considered during generation. The proposed method filters molecules by SA score post-hoc (SA < 4.0 or 4.5), but SA is not used as guidance during the generation process itself.  So the model might be generating lots of complex molecules that just get thrown away. This seems inefficient and makes the \"success rate\" metric less meaningful - you are just filtering harder, not actually guiding generation toward synthesizable molecules.\n\nThe presentation lacks clarity and detail on several key issues of the proposed methods.  See my questions for the details.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "w0MbuXqB4Z", "reviewer": "Reviewer_ckkR", "summary": "This paper explores how to design bioisosteres using the pre-trained SemlaFlow model—a 3D unconditional molecular generator that jointly designs molecular graphs and 3D conformers—without requiring fine-tuning. While prior attempts at bioisosteric design exist, they often neglect 3D conformational information, which is crucial for drug design (2d-based design). In contrast, the authors demonstrate that their methods can successfully design bioisosteric ligands under 3D constraints.\n\nThe authors propose two inference-time conditioning strategies: Interpolate-Integrate (resampling) and Replacement Guidance (fragment-conditioned generation). These methods appear quite simple yet are intuitive and robust. The authors validate their framework across serveral drug design tasks, including fragment merging and pharmacophore-guided design, which are important in drug discovery campaigns. Moreover, the results for natural product ligand hopping are interesting.\n\nHowever, I have some concerns, and I would like to increase my score based on the response.\n\n**The usage of LLM**: I wrote the entire review myself and only used the LLM to correct the grammar and improve readability.", "questions": "- What are the limitations of this approach?\n- Table 4: Why is the **No clash** not 100% for Known Binders?\n- Line 544: There are duplicated references:\n    - Fergus Imrie, Thomas E. Hadfield, Anthony R. Bradley, and Charlotte M. Deane. Deep generative design with 3D pharmacophoric constraints. Chemical Science, 12(43):14577–14589, 2021a.\n    - Fergus Imrie, Thomas E. Hadfield, Anthony R. Bradley, and Charlotte M. Deane. Deep generative design with 3D pharmacophoric constraints. Chemical Science, 12(43):14577–14589, 2021b.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- **Practicality of Training-Free Conditioning**: The paper's primary strength is its proposal of two training-free strategie. This inference-time approach is highly practical as it enables goal-directed generation using unconditional generative models (or foundation model) without costly retraining.\n\n- **Effective 3D-Native Bioisosteric Design**: The paper addresses the important task of bioisosteric design in a 3D-native manner, directly conditioning on 3D shape and pharmacophore patterns. This is a significant advantage over 2D graph-based methods. The two proposed strategies are intuitive and complementary: Interpolate-Integrate provides 'soft' guidance for high-fidelity edits, while Replacement Guidance offers 'hard' anchoring for fragment merging.\n\n- **Strong Empirical Validation:** The framework is not just a theoretical proposal; it is validated across three challenging and highly relevant tasks in drug discovery (natural product hopping, bioisosteric fragment merging, and pharmacophore merging).", "weaknesses": "**1. Theoretical Justification and Ablation for Replacement Guidance**\n\nI wonder the theoretical justification for the Replacement Guidance. As I understand it, the method control the ODE process by replacing a portion of the state $x_t$ with a clean fragment structure $x_{frag}$ at each step.This creates a partially 'clean', out-of-distribution (OOD) state that the SemlaFlow model never encountered during its unconditional training (which only included fully noised states). It would be helpful if the authors could elaborate on why this OOD state manipulation is effective and preferable to using a noised fragment $x_{frag; t}$ (which would be in-distribution).\n\nFurthermore, I am curious if the authors have explored the alternative of applying guidance to the self-conditioning term $\\hat{x_1}$ rather than manipulating the state $x_t$. \n\nThe paper would be significantly strengthened by including ablation studies on these design choices (e.g., clean vs. noised fragment injection, state $x_t$ vs. prediction $\\hat{x_1}$ manipulation).\n\n**2. Omission of Related Work on Bioisosteric Design**\n\nThe related work section provides a good overview of 3D conditional and inference-time methods, but it appears to overlook a relevant line of research focused specifically on fragment-based bioisosteric design and editing [1, 2]. While these methods may differ in their approach (e.g., 2D graph-based vs. 3D-native), they address the same core task. Including them would provide readers with a more complete context of the bioisosteric design field, even if direct experimental comparison is not the focus.\n\n**3. Limited Scope of Property Optimization**\n\nThe paper compellingly demonstrates control over 3D similarity and synthetic accessibility (SA score). However, bioisosteric replacement, especially in the lead optimization stage, often requires the simultaneous optimization of other key physicochemical properties (e.g., logP, TPSA, Molecular Weight) [1, 2]. It is not clear from the paper if or how the proposed conditioning strategies could be extended to incorporate guidance towards these scalar property targets. This currently limits the method's immediate applicability for multi-objective lead optimization, which is a primary use case for bioisosteric design.\n\n---\n**Reference**\n1. Chen, Ziqi, et al. \"A deep generative model for molecule optimization via one fragment modification.\" Nature machine intelligence 3.12 (2021): 1040-1049.\n2. Kim, Hyeongwoo, et al. \"Deepbioisostere: Discovering bioisosteres with deep learning for a fine control of multiple molecular properties.\" arXiv preprint arXiv:2403.02706 (2024).", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eK0QUHk73c", "reviewer": "Reviewer_adpW", "summary": "In this paper, the authors present two novel, training-free conditioning strategies (1) Interpolate–Integrate and (2) Replacement Guidance that enable controllable 3D molecular generation using pre-trained E(3)-equivariant flow-matching models. The authors target ligand-based drug design scenarios, where protein structures are unavailable, and the goal is to generate new molecules that preserve key pharmacophoric and shape features of known ligands or fragments.\nThe framework is evaluated on three meaningful and representative LBDD tasks natural product ligand hopping, bioisosteric fragment merging, and pharmacophore merging, showing improved validity, synthetic accessibility, and diversity over specialized baselines.", "questions": "it is unclear to me how pharmacophore merging is different from biosteric fragmetn merging. It appears that in both cases the fragments from fraglysis program are replaced and merged to produce a functionally similar synthetic molecule. Can the authors elaborate and explain on this point?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The presented manuscripts tackles a key area in 3D generative drug design and present an elegant solution synthetic library search and fragment merging. The manuscript is well written and easy to follow. The experiments conducted sufficiently demonstrate the key claims though some additional experiments can further help elucidate the strengths of the 2 methods.", "weaknesses": "- In table 1, Since the success rate is low, it would be transparent to see top-100 and top-1000 when possible as well to see how fast the similarity declines.\n- P7, L325, which are these \"key pharmaphoric features\"? It should be specified or referenced. \n- In fig 1, please also note the pharmacophoric similarity to the corresponding NP. Some reported molecules especially for NP3 appear to be significant simplification of NP3 and may lack the same pharmacological effect.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "uW9UFFHlae", "reviewer": "Reviewer_mYeo", "summary": "The authors introduce two training-free interpolation strategies that mix known reference target data into flow-matching molecular generators based on SemlaFlow. They either interpolate between the generated sample and the reference partway through FM integration, or hold parts of the reference data fixed during the integration process. This is proposed to enable scaffold hopping, bioisosteric replacement, and pharmacophore matching by incorporating information from known binders into the generation process. The approach is compared to several recent methods—MolSnapper, DiffSBDD, and perhaps most appropriately ShEPhERD—showing improved SA scores and pharmacophoric similarities/docking scores on experiments covering some intended use cases. There are details of the evaluation that appear to be missing, which makes it difficult to fully assess how the method works. Overall, it is quite a simple idea and appears to work well.", "questions": "Why is validity generally much lower in the second two experiments than in the first or in the ablation?\n\nAll methods produce chemical graphs with extremely low similarity to the reference (Figure 7), despite injecting the NP structure. Why? \n\nHow does this compare to samples from the unperturbed SemlaFlow?\n\nIt is odd to report only Vina scores and not pharmacophoric similarity (to the target) in the bioisosteric fragment matching and SARS-CoV examples, given that pharmacophore matching is the explicit target.\n\nIt would be useful to report SMILES uniqueness for successes in each method in the experiments as well. In general, the chemical diversity of the successful identifications is not quantified.\n\nInpainting experiment (Table 11): the description lacks detail. Can the authors explain how this was implemented? Also, the only metrics for inpainting relate to validity, not SC similarity as in other experiments.\n\nHow were pharmacophoric features defined? Is this based on RDKit shape and color similarity? In the appendix it states: “Molecules were selected based on pharmacophore similarity ..., and each recovered reference pharmacophore feature was matched within a 1.5 Å threshold.” The definition of “hydrogen bond donors (HBD), acceptors (HBA), and aromatic centres” is not precise—how is this determined? Additionally, this is a short and somewhat arbitrary list.\n\nIt seems important to provide some indication of the quality of the geometries pre-relaxation, as xTB will resolve many issues.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "The idea is very simple and easily explained, and the paper is clearly presented.\n\nThe ability to merge across many reference pharmacophoric features in a scalable way is useful, even if in practice this is achieved by random subsampling.\n\nThe authors report superior results to ShEPhERD, a considerably more complicated method, on several shared tasks.", "weaknesses": "A lot of emphasis is placed on SA scores, but this method does nothing to directly control SA relative to other methods as I can see. Presumably, the improvements here are due to better characteristics of the base model (SemlaFlow), so comparisons along this axis are not particularly convincing. The method shows quite low validity, especially compared to the baseline SemlaFlow. The authors are quite loose with the definition of a pharmacophore; interpolate-integrate seems more like enforcing global similarity to the reference molecule rather than specifically matching pharmacophoric features. Regarding the “dense” pharmacophoric features in the NP problem, it seems dubious that retaining all atomic features of input molecules is useful in practice. In general, a specific set of interactions or features govern the ligand–target interaction, not all features of the ligand, as implied in the second two experiments.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)