SYNC: Measuring and Advancing Synthesizability in Structure-Based Drug Design
Authors: Yunfan Liu
Lirong Wu
Zhifeng Gao
Yufei Huang
Cheng Tan
Haitao Lin
Zicheng Liu
Changxi Chi
Chang Yu
Stan Z. Li
Venue: ICLR 2026 Poster (2026)
Keywords: Structure Based Drug Design; Synthesizable Drug Design; Controllable Generation
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)

Abstract: Designing 3D ligands that bind to a given protein pocket with high affinity is a fundamental task in Structure-Based Drug Design (SBDD). However, the lack of synthesizability of 3D ligands has been hindering progress toward experimental validation; moreover, computationally evaluating synthesizability is a non-trivial task. In this paper, we first benchmark eight classical synthesizability metrics across 11 SBDD methods. The comparison reveals significant inconsistencies between these metrics, making them impractical and inaccurate criteria for guiding SBDD methods toward synthesizable drug design. Therefore, we propose a simple yet effective SE(3)-invariant \textit{\underline{SYN}thesizability \underline{C}lassifier} (SYNC) to enable better synthesizability estimation in SBDD, which demonstrates superior generalizability and speed compared to existing metrics on five curated datasets. Finally, with SYNC as a plug-and-play module, we establish a synthesizability classifier-driven SBDD paradigm through guided diffusion and Direct Preference Optimization, where highly synthesizable molecules are directly generated without compromising binding affinity. Extensive experiments also demonstrate the effectiveness of SYNC and the advantage of our paradigm in synthesizable SBDD. Code is available at \url{https://github.com/XYxiyang/SYNC}.

Published as a conference paper at ICLR 2026
SYNC: MEASURING AND ADVANCING SYNTHESIZ- ABILITY IN BRUCTURE-BASED DRUG DESIGN
Yunfan Liu 13.4.1, Lirong Wu 223.. Zhifeng Gao", Yufei Huang", Cheng Tan
Haitao Lin", Zicheng Liu", Changxi Chi", Chang Yu",, Stan Z. Li
'Zhejiang University DAMO Academy, Alibaba Group Westlake University *DP Technology "Shanghai AI Laboratory
{1 [liuyunfan, wulirong, juchang]@west @west edu. cn
ABSTRACT
Designing 3D ligands that bind given protein pocket with high affinity is fundamental task in Structure- Based Drug Design (SBDD). However, the lack of synthesizability of 3D ligands has been hindering progress toward experimental validation; moreover, computationally evaluating synthesizabilityy is non-trivial task. In this paper, we first benchmark eight classical synthesizability metrics across 11 SBDD methods. The comparison reveals significant inconsistencies be- tween these metrics, making them impractical and inaccurate criteria for guiding SBDD methods toward synthesizable drug design. Therefore, we propose a simple yet effective innvariiv SYNthesizability Classifier (SYNC) to enable better synthesizability estimation in SBDD, which demonstrates superior generalizabil- ity and speed compared to existing metrics on five curated datasets. Finally, with SYNC plug-and-play module, we establish a synthesizability classifier-driven SBDD paradigm through guided diffusion and Direct Preference Optimization, where highly synthesizable molecules are directly generated without compromis- ing binding affinity. Extensive experiments also demonstrate the effectiveness of SYNC and the advantage of our paradigm in synthesizable SBDD. Code is avail- able at https: / htt.. com/ XYx yang/ SYNC
INTRODUCTION
Recent years have witnessed the remarkable success of Artificial Intelligence-driven methods for drug design, of which Structure-Base Drug Design (SBDD) is a representative example (Guan cet al. 2023: Peng et al. 2022 Luo et al. 2021). The primary objective of SBDD is to generate 3D ligands with desired properties that can bind tightly to a given protein pocket. Previous works typically formulate SBDD as a conditional generation task, where protein pockets are treated as conditions, and the distribution of 3D ligands is to be learned. Consequently, many deep generative models have been employed to handle this task, including autoregressive and diffusion -based mod- els. For example, autoregressive models (Luo et al. 2021 Peng et al. 2022 Liu et al., 2022) gener- ate atoms, bonds, or fragments of 3D ligands binding to a target pocket in a sequential manner. To overcome the dilemma of autoregressive models in error accumulation and generation order (Guan) lt al. 2024), diffusion- tributions of atom types, atom position, and bond types from prior distributions. Although promising results in terms of binding-relate related metrics (e.g., docking scores) have been achieved, the synthesiz- ability of the generated molecules, which is crucial for the practicality and validation of drug design,
models (Guan et al. 2023 Lin et al. 2025) directly learn the data dis-
has continuously been an obstacle (Luo et al. 2024 Gao et al. 2024 Shen et al. 2025).
In practice, generating valid drugs with desirable properties from the vast chemical space (estimated to contain 10e0 potential "drug-like" molecules) is extremely challenging, and considering synthe- sizability further exacerbates the difficulty. A central challenge here the lack of well-enough criteria for evaluating the molecular synthesizability. There have been many efforts to evaluate synthesizability, which can be categorized into three branches. Rule-based methods (Erti & Schuf- fenhauer 2009; Voršilák et al. 2020) make qualitative predictions based on substructure, molec- ular mass, etc., which are efficient but fail to quantify synthesizability and lack generalizability.
Equul contribution. "Corresponding authors.

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Published as a conference paper at ICLR 2026
Retrosynthetic-basee methods (Genheden et al. 2020) guarantee the highest true positive rate since they output synthetic pathways, but are limited by the precursors in searching stock and are ex- tremely time-consuming. Learning-basee methods (Yu et al. 2022 Coley et al. 2018 Neeser al.) 2023) flexibly model molecules, but most utilize only coarse-graiie molecular conformations into account, which are needed by SBDD methods. We present the rank- ings of the 8 synthesizability metrics for the molecules generated by 11 SBDD methods in Figure We observe huge inconsistencies between these metrics. Take the synthesizability of FLAG for ex- ample, 3 out of 8 metrics show poor synthesizability, while the others suggest good synthesizability. These conflicting metrics and their respective drawbacks make it hard to draw a meaningful conclu- sion about the synthesizability and therefore limil the ability to guide synthesizable drug design.
features and fail to take 31)
In this paper, we first establish a new benchmark for comprehensively comparing existing synthesizabil- ity metrics. Specifically, we have collected and or- ganized 5 datasets containing both Easy- and Hard- to-Synthesize (ES/HS) molecules (Yu et al. 2022).
Synthesizability Rank
3DSIDD
D3FG
DITTP
5BDD Methods
FLAG
5
1
2
GraphAB
as well as their 3D conformations, which 8 clas- sical synthesizability metrics are evaluated. We fur- ther propose simple yet effective SYNihesizability Classifter (SYNC), which achieves the best perfor- mance in our benchmark and offers merits to guide SBDD methods. Together with existing metrics, provide comprehensive benchmark to evalu- ate the synthesizability of 11 SBDD methods. Fi- nally, we provide two SYNC-driven paradigms that employ SYNC a plug-and-play module to con- trol the diffusion process: (i) guided diffusion with SYNC; (ii) DPO (Rafailov et al., 2024) with SYNC to achieve higher synthesizabilityy while preserving binding affinity. Extensive experiments demonstrate the potential of SYNC and our paradigm for synthesizability estimation and synthesizable SBDD. Our contributions can be summarized as:
LIGAN
3
4
MolCrait
2
VoxBind
4
TargetDrf-
10
10
9
DecompDiff
Algmts
Homghe
SCScor
CASA
Synth siitabiltyyy Metrics
Figure 1: comparison of rankings of 8 clas- sical synthesizability metrics for molecules generated by 11 classical SBDD methods, which show strong inconsistency. Detailed numerical values can be found in Table
Comprehensive Benchmarks. We collect and organize 5 datasets containing both easy- and molecules from various data sources to benchmark 8 classical synthesizabilitty metrics. Additionally, we also benchmark 11 existing SBDD methods using these metrics.
A Practical Metric. We propose a 3D-aware and SE(3)-invariant SYNihesizability Classifier (SYNC) synthesizability metric, reaching better performance in accuracy and efficiency. Designed with multiple merits, SYNC is consequently suitable for guiding SBDD methods.
A Synthesizable Paradigm. Our proposed SYNC can be directly combined with existing SBDD methods with two controllable generation methods- guided diffusion and DPO- to improve the synthesizability of the generated molecules while not sacrificing their original binding affinity.
2 RELATED WORK
2.1 STRUCTURE- BASED DRUG DESIGN
Structure-Based Drug Design has evolved tremendously in recent years, driven by generative AI models. Some early works iteratively generate atom voxelization density maps, occupied grids, or atom types/positions in an autoregressive manner, including 3DSBDD (Luo et al.) 202T), and
Pocket2Mol (Peng et al.) 2202). Later research paradigms shifted to diffusion-based models, includ- ing TirgetDiff (Guan et al. 2023), DiftBP (Lin et al., (205)). and GraphBP (Liu et al. 2022), which enable generation of full-ane full-atom positions and atom types simultaneously. Functional group-based methods, FLAG (Zang et al. 2023) and D3FG (Lin et al. (024a)) have incorporated prior knowl- edge on fragment motifs into model design. DecompDiff (Guan et al., 2024) and its subsequent works, DecompOPT (Zhou et al. 2024) and DecompDPO (Cheng et al. 2024), break molecules into arms and scaffolds, design each arm more carefully and connect them with scaffolds to gen- erate new molecules. Recent works, such as MolCraft (Qu et al.) 2024) and VoxBind (Pinheiro jet al., 2024), adopt latest and more powerful molecular denoising techniques, achieving promising generation results. For more methods, we refer readers to recent benchmark (Lin al. 2024bb.
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2.2 SYNTHESIZABILITY EVALUATION
Existing works can be broadly categorized into three branches: (1) Rule-based methods, such as SA (Ertl & Schuffenhauer 2009) and SYBA (Voršilák et al. 2020). are based on predefined func- tional group compatibility, structural stability, etc. (2) Retrosynthetic-basse methods, such Aizyn- thfinder (Genheden et al. 2020), aim to decompose given molecule and recursively search for pur-
chasable precursors. (3) Learning-based approaches. such as Nonpher (Voršilák & Svozil 2017). RAscore (Thakkar et al., 2021). GASA (Yu et al., (022), and FSScore (Neeser et al. (023). learn to flexibly model synthesizable landscapes of the chemical space, which show better generaliza- tion. Despite much progress, limitations exist. Rule- bale-based methods are naive and often lead to sub-optimal performance. Retrosynthetic-asse
methods ensure the highest accuracy for molecules classified as synthesizable by providing synthetic routes, but are incredibly time-consuming More- over, the rich information underlying 3D conformation is neglected by learning-haa -aased methods.
2.3
SYNTIESIZABLE MOLECULE DESIGN
Current dominant approach of generating synthesizable molecules involves chemical building blocks (i.e., accessible molecular fragments) and reaction templates. Early methods explored Bayesian optimization (Korovina et al.
2020), genetic algorithms (Gao et al. (02T), and Monte Carlo tree search (Swanson et al. 2024). Recently, GFlow based (Bengio et al. 2023 methods (Shen et al.)
2023 Koziarski et al. 2024 Cretu et al. 2024, Sco et al. (024) have emerged with great potential.
However, these models do not generate 3D molecule structures and cost extra computation for docking 2D molecules into 3D) protein pockets, which draws clear boundary with our method. (2025) generates 3D conformations as an exception, it does not guarantee structural quality. We provide additional illustrations and comparisons with these building block-based methods in Appendix Synthesizability in 3D molecule design is yet to be improved.
Although CGFlow (Shen et al.]
PRELIMINARII ARIES
Seuucture-Bsseee Drug Design SBDD aims to generate 3D ligands that bind to given protein
pocket with high binding affinity. The protein pocket and 3D ligand can be represented as (<pp) , Up ) Nr
(i) (i-f) Jij== and M = [ M eMrr ) i.j= where Np, NNM denotes the number of atoms in the protein pocket and ligand, respectively, and E R33
(i)
Nas
Rdd and RR- denote the coordinates of atoms, the types of atoms, and the types of bonds between atoms.
Molecule Generation via Diffusion Based on the Denoising Diffusion Probabilistic Model (DDPM) framework (Ho et al., (020), most diffusion models for SBDD can be formulated as conditional Markov chain by the (forward) diffusion process that gradually adds noise to data:
T
(Mr Mo, P) II (M, Mc-1,))
The generative process denoises to generate molecules with a neural network parameterized by 8:
Pa Pa Mr. P)
II Po M,,
where Mi, M2,.... Mr is a sequence of latent variables with the same dimensionality as data Mo ~ p(Mo P), and p(Mr) is a prior data distribution during the sampling process.
4 BENCHMARKING SYNTHESIZABILITY METRICS
We first study the rationality of existing synthesizability metrics within a benchmark before moving into synthesizable molecule design. The central challenge in establishing such a benchmark is to collect molecules that can and cannot be synthesized. Synthesizable molecules are readily available, e.g., any molecule that can be purchased from a commercial library is synthesizable. However, it is tricky to determine that a molecule cannot be synthesized without the aid of expensive experimental assays. Therefore, we take easy- and hard-to-synthesizz molecules as a cheap but good alternative, because is easier collect hard-to-synie -yytthe-yy molecules than completely unsynthesizable ones.
Datasets Five datasets are used as benchmark datasets, Firstly, we utilize Enamine building block stock (Enamine 2023) as the ES dataset, ensuring that all molecules are readily purchasable. More- over, we collect three test sets (TSI/TS2/TS3) YYu et al. (222) and the Nonpher-Tee Test dataset from /Voršiáák & Svozil 2017), which contain both ES and HS molecules labeled by different previous
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Table 1: Classification accuracies for synthesizability metries across 5 datasets, where the best and second metrics each dataset are marked bold and underline. respectively (same for all subsequent tables). For scoring metrics (SA, SYBA, RAScore, SCScore), we apply thresholds to distinguish easy- and hard-to-synthest has been found, we dynamically adjust its threshold to guarantee the accumulative best performance over five datasets. All metrics are categorized as retrosynthetic-basee
molecules. For FSScore, since record for the threshold
rule-based, and learning- based approaches, labeled using , *, and A. More details of thresholds can be found in Appendix
Datasets/ / Accuracy
Alzynth
SA'
SYBA"
Nonpher
FSScore+
RAScore*
SCScore*
GASA*
SYNC* (ours)
0,9535
0.9853
0.9881
0.8817
0.5003
0,9683
0.6518
0,9850
0.9911
T82
0,7509
0,8090
0.7369
0.7737
0,5401
0.7136
0.4026
0,8010
0,8406
TS3
0,7511
0.5667
0.5800
0.6828
0.4717
0.6772
0,4761
0.7590
0.7564
Nonpher-Test
0.5688
0.8313
0.8812
0.8940
0.7937
0.9250
0.4125
0.9188
0,9313
Enamine
0.8061
0.9844
0.9571
0.7924
0,9171
0,9890
0.9171
0.9538
0.9520
Arg. Rank
6.0
4.0
4,4
5.6
7.8
4.0
8.0
3.0
2.0
Time 6s/10K mols)
174192.60
2.67
1,70
0.97
38.99
56.81
17.4
24.93
1.78
works, and we remove repetitive samples. Note SYNC is trained on an isolated dataset containing millions of 3D molecules excluding test molecules, where details can be found in Appendix B A comparison of 9 synthesizabilityy metrics (including our SYNC, detailed in the next subsection) on the 5 datasets is reported in Table [] from which we can make three important observations:
Observation 1: Rule-based methods often fall short on specific datasets, e.g.. SA, SYBA on TS3, demonstrating their limitations in terms of generalization capability. The retrosynthetic-bent based model can guarantee the highest true positive rate by assuring a synthetic path, but it can still lead to false negatives, since Ainder) fails to find all synthesizable molecules in Enamine. In contrast, learning-based models generally enjoy higher performance but can suffer from a high computational burden, while our work (SYNC) makes the best trade-off between efficiency and effectivencss.
Observation 2: SYNC surprisingly outperforms other baselines by wide margin in the overall rankings across 5 datasets. For example, SYNC performs best on TS1, TS2, and Nonpher-Teer Test datasets and ranks second the TS3 dataset, demonstrating its superiority. Although SYNC does not perform best on the Enamine dataset, the gap is not large. This gain is from two aspects: deep learning endows SYNC with higher flexibility in classifying different molecular patterns than rule- based models, while 3D information provides richer information to surpass conventional learning- based methods. For example, bond angles and steric hindrance are two key factors determining a molecule can be synthesized, which can only be reflected in the 3D space. We provide detailed explanation of why 3D is important and investigate the interpretability of SYNC in Appendix D
Observation 3: SYNC achieves excellent performance within limited time consumption. Al- though Nonpher and SYBA are faster than SYNC, they are significantly less accurate. Both learning- based and retrosynthetic-bacee based methods are computationally heavy; e.g., AizynthFinder, designed for searching retrosynthetic pathways, is unaffordable for sarge-scale molecule screening.
METHODOLOGY
5.1 SE(3) SYNTHESIZABILITY CLASSIFIER
In this subsection, we introduce how to train an ric. Conventional synthesizabilityy predictors often neglect the 3D conformation of target molecules, so rich information is discarded. For example, some out-of-distribution bond angles can signifi- cantly increase the synthetic difficulty, or the synthesized molccules can be unstable due to such uncommon structures (Schulman & Venanzi 1974). Another example is steric hindrance, where atoms or groups within a molecule are brought too close together in space, resulting in unstable molecules (Lenoir et al.) 2006]] Rösel al. 2017). More importantly, SBDD methods usually gen- erate 3D conformations and require a 3D-aware synthesizability metric to take conformations into account. Therefore, enabling 3D conformation awareness not only brings additional information that helps predict synthesizability but also makes it plug-and-ple 3D-aware classifier for ad advancing synthesizable molecular design within our classifier-driven paradigm.
classifier as the synthesizability met-
AAthoogh AizynthFinder is widely used and ensures high accuracy on molecules classified as synthesizable by providing synthetic routes, its overall performance is not optimal. Enlarging the molecular stock (search space) and computational resources may improve results, while use the official stock for fair comparison.
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(a) SE(3) Invariant SYNC
(b) Guided Synthesizable Generation
Gradient Guidance
(y (M,LP)
Atomic Type Coords
(y
edges
EGCL Layers
Projection
T=t
T=0
(M (,,P)
Sampling Process
(M, MM,
(c) Direct Preference Optimization with SYNC
Guidance to Winnina Deta
(M, (M,LP))
Easy-to- toottthheeizze Molecules
M'
DPO
Diffusion
OM M.LLP)
Protein Pocket
Guidance to Losing Data
M
(0) B Mr1 logar P
R.M IP) P. (M [P)
Blog BM [P]
RM PP),
Figure 2: A high-level overview of the Synthesizability Classifier-Driver paradigm. We propose SYNC, an SE(3)-invarian) synthesizability classifier, to accurately and efficiently distinguish easy- to-synthesize (ES) and sarrtto--yntthesiee (HS) molecules. We then integrate as a plug-and-pu module with guided diffusion and direct preference optimization (DPO) for SBDD methods.
Based on the above observations and discussion, we believe that a good classifier should have Firstly,
the following advantages: fast, differentiable, 3D structure-aware, and a good classifier must not only be highly accurate, but also infer fast so that can be used for large-scale screening meet practical needs. Secondly, differentiability is another elegant property that benefits the integration classifier into our synthesizable SBDD paradigm. Thirdly, as men- tioned, 3D awareness provides richer information. Finally, synthesizability is intuitively supposed to be agnostic to translations and rotations, so the classifier expected to be -Ev3a-inn To meet the above four properties, we propose simple yet effective SE(3)-invariant SYNthesizability Classifter (SYNC) to achieve synthesizabilitty estimation. Assuming we have any arbitrary SE(3)- transformation Tg, the prediction output of SYNC is expected to be invariant T follows:
SYNC(T, (MM. UMr eM)) = SYNC M, , Uar CM)
To achieve SE(3)-invariance, we use a L-layer EGNN (Satorras et al. 2021) as the backbone of our classifier, where each equivariant Graph Convolutional Layer (EGCL. takes (1) (l)
M M 6M inputs:
(I+1) ((+1) FM PM EGCL[ (1) (1) "M eM))
where 0 < S L, and x (0) ZM and UM UM are the input features. Finally, we directly utilize (E) nonlinear transformation from LM to predict synthesizability. Note that we have not designed the classifier to be very complicated, due to the priority inference efficiency. However, thanks to the massifier-driven SBDD paradigm.
merits mentioned above, SYNC is the only choice for
5.2 CLASSIFIER CIIUPIERRDDIVEE DRIVEN SBDD PARADIGM I: GUIDED DIFFUSION
The denoising process Po Mr, P) in Eq. 2 can be represented as the product of a Gaussian distribution N of continuous atomic coordinates I:-1 and Categorical distribution C of discrete atomic types u/-1:
Next, we integrate SYNC with guided diffusion, as shown in Figure 2
PM M P) N (( (Ir v,,, P), x C (o),,, ()),
(5)
where Br the simplified variance schedule factor, and vi ([v,, ve), f. P) and co([x,, Vr], P) are the predicted data distribution time 1, where uy and Ga can be regarded sub-modules of pe that predict positions and atom types, respectively. We next add guidance from a pre-trained classifier (SYNC) Ps M,) constrain the generated molecules with higher synthesizability, as follows:
(M Mr, P. p) (M M,, P)p M.))/
(6)
where is a normalizing constant. However, the conditional score log Pa (y M,) )) is hard to be precisely evaluated because the classifier is trained only on ground-truth molecular conformations
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and has not seen intermediate diffusion results. Therefore, follow (Song et al. 2023 Wu al. 2024) to approximate V Vp (P M,) through the estimated output of the denoising network:
/ I
Vpolu M,)
po(u) Polul M.) dMo =V
(M.
-Vp (vl po(Mo)lM,
For the continuous atom coordinate distribution, the positions for the next timestep are sampled from:
It-1 < .V (vo(( e P) + d V po M,,4, PJ),
(8)
As for the categorical atom type distribution, we apply Gumbel softmax to sample from categorical probabilities, and then feed molecules to SYNC and get categorical guidance:
Ut-1 + C exp log (ce([ tt),t,)) + X log Vo.Po (M M,, M, PI)).
(9)
where 22 and X control the strength of guidance in terms of atomic coordinates and types.
Multi-Step Guidance As the diffusion step i approaches O the model may suffer from diffusion confinement, i.e., the model will stubbornly keep atoms in some particular spatial patterns. To address this issue, we introduce multi-step guidance, which allows for multiple guidance steps in a single diffusion step. The reason for not simply setting larger factor À but utilizing multí-step guidance is that larger A tends to move the atoms too far away from each other. As result, the bonds between atoms no longer exist, and the molecule is torn into pieces, forming isolated atoms.
Edge Construction Another challenge is that the diffusion process does not involve molecule bonds. Some previous works (Guan et al. 2024 Peng et al. 2023) integrate edge diffusion that at each timestamp t, the ligand is no longer a set of atom points, but the complete molecule. In this paper, we address this issue in another straightforward way. When training SYNC, utilize the ground-truth bonds but ignore their bond type information, which is proven to be harmless performance in Section 6.5 During guidance, we connect atoms by finding their K-nearest neighbors based on the maximum valency of each atom. We also filter bonds longer than 2.0 Á and make sure all bonds are bidirectional: otherwise, atoms with higher valency may mistakenly connect to other atoms. Such design can solve the atom- bond inconsistency during diffusion generation.
5.3
MHASSIFIER-DRIVER SBDD PARADIGM 2: DIRECT PREFERENCE OPTIMIZATION
After introducing training- free controllable diffusion, we explore how to fine-tune the generative model with SYNC by DPO (Raailov al. 2024) in Figure 2 In practice, how to obtain high-quality pairwise preference data is central challenge for DPO. However, generating data directly using diffusion models does not work because most of the generated molecules are hard-to-synthesizee Different from previous methods (Zhou et al.) 2024) that the classifier directly as reward function in the DPO framework, we utilize SYNC to generate better pairwise preference data in this paper. Specifically,
follow the strategy described in Section 5.2 to generate easy- and hard- to-synthesize molecules (M+,M .M) either by adding or subtracting the gradients in Eqs.
The vanilla DPO framework (Rafailov al. (M+, M (D) - (Bp log
2024 fine-tunes the model on pairwise preference data
B BBogg (M(M Piet (M+IP) ,3 log PM M (M)
(10)
Pret(M (M)1)
where M is the preferred seasyto--snntheiizze molecule, M the unfavored (hard-to- syythhei-yyt molecule, and / is factor controlling DPO strength. In addition, pa(-I-) is the target model to be fine-tuned, and (l.) (.) is the frozen original model to constrain the tuned model not too far away. Following (Wallace et al. 2024), the DPO loss in diffusion can be formulated follows:
LDiffusionDDPO()) B.P.M..MM J-D log o (BBM Mir
log
Prer( (Mor P) Pretl Mor )
(M P)
log Po P)
(11)
Using Jensen's 's inequality and the convexity of log a, the following inequality can be derived:
Litttsion (9) S -E M-) logo
(8) [0] log Po (M MM, P) I,,,M MM, [MT, N P)
log IM 1
(12)
|M, P))

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Based on Eq. ( the above loss is further simplified as:
Lo - (p,at M- -AP. (o0 log o ( (CO PDMM P))
(13)
LD(M* (Ma(M t, P1) (Co(M" ,Po(M ,t, P)) LMM PialMi t, P1))
where LD the original diffusion loss, X the dignal-tonoiise ratio, w(A)) is weighting function (usually constant in practice (Ho et al.) (020)), and we factor the constant T into 3. The final loss Ca in Eq. (13) encourages the model to improve generative possibility at sample M+ than sample M T, which finally leads to higher overall synthesizability of generated molecules.
EXPERIMENT
6.1 EXPERIMENTAL SETUP
Datasets, Baselines, Metries We follow previous works (Luo al., 2021) use the Cross- Docked2020 (Francoeur a 2020) dataset for training and evaluating SBDD methods. We sample 100 molecules for each protein pocket the test set, resulting in a total of 10,000 molecules. For pairwise data for DPO, to prevent data leakage, data points from the training set and filter a total of 3,431 pairs satisfying the ES and HS molecules annotated by SYNC for TiffetDiff, and 1,991 pairs for DecompDiff. We consider multiple classical synthesizability metrics, including AizynthFinder, FSScore, GASA, SA, SYBA, SCScore, RAS- core, Nonpher, and SYNC. To benchmark synthesizability, consider 3DSBDD, LiGAN, D3FG, DecompDiff, DiABB, FLAG, GraphBP, MolCraft, Pocket2Mol, TargetDiff, and VoxBind.
randomly sample 10,000 pairwise preference
Motivation for Backbones As a milestone in SBDD, TargetDiff is often applied as a backbone model in later works (Huang et al.) 2024 Qian et al. 2024), and we follow this setting. While methods like FLAG and GraphBP excel in synthesizability, they lag in binding affinity. We believe that focusing solely synthesizability while neglecting binding affinity is unwise. Conversely, DecompDiff performs well in terms of binding affinity but lacks synthesizability, so we intend to overcome this dilemma generate molecules with high binding affinity and synthesizability.
6.2 CLASSIFIER SERLIFIER-DRIVEE DRIVEN PARADIGM GENERATED SYNTHESIZABLE MOLECULES
Table 2: Synthesizability ratings of generated drugs for various SBDD methods. (t)(() denotes that a larger/smaller value is better. The Top 2 results are marked by bold and underline. Since the optimal direction varies across metrics (i.e., larger-is-hetter vs. smaller-is- better,, for clarity, we uniformly use green and red to denote improved and reduced synthesizability w.r.t the backbones.
Methods
Aizynth* (1) SA'
SYBA (1)
Nonph. (1)
FSSc. (1)
RASc. (?) SCSc.()
GASA
SYNC
3DSBDD
0.1960
0.635
-25.795
0.2538
-4.310
0.538
3.202
0.3321
0.2978
D3FG
0.0728
0.564
-56.213
0.1599
-8.097
0.407
3.679
0.2061
0.1041
DinBP
0.0968
0.609
-27.891
0.2556
-5.671
0.446
3.364
0.2118
0.2533
FLAG
0.0760
0.582
-29.799
0.3608
0.759
0.5639
0.2345
GraphBP
0.1936
0.690
-8.862
0.3988
-3.843
0.794
3.032
0,5538
0,2180
LiGAN
0.2280
0.663
-2.471
0.4538
-2.413
0.605
3,027
0.4302
0,3348
MolCraft
0.1929
0.672
-14,381
0.3909
-5.938
0,533
3.495
0,3935
0,3292
Pocke12Mol
0.1389
0.654
-27.299
0.3529
-2,368
0.683
2,899
0.4168
0.1630
VoxBind
0.1296
0.657
22.362
0.3333
-6.888
0.570
3.500
0.3442
0.2365
Targe:Diff
0.1124
0.601
41,991
0.2328
-6,560
0.421
3.522
0.2418
0.1958
TargetDiff-Guuie Guide
0.1365
0.626
-33.123
0,2793
-5,270
0.427
3,423
0.2653
0.3977
ATsaaDir
+21.4%
+4.2%
+21.1%
+20.00 20..0%
+19.7%
+1 +1.4%
-2.8%
49.7%
100.3%
Targe:Diff-DPO
0.1283
0.626
-343966
0.2844
-5.437
0.455
3,417
0.2683
0.2385
AugenDur
+14.1%
+4.2%
+18.1%
+22.2%
+18.5%
+8.1%
-3.0%
+ +11.0%
+21 8%%
DecompDiff
0.0176
0.574
-16.006
0.2739
-12.860
0.330
4.414
0.2104
0.1201
DecompDDif-Cludde
0.0263
0.584
-8.788
0.3108
-12.227
0.337
4,359
0.2239
0.1378
Apecmpturr
+49. +49.4% 4%%
+1.7%
445.15
+13.5%
+5.0%
+2.1%
-1.2%
46.5%
+ +14.7%
DecompDift-DPO
0.0241
0.586
-14.94
0.3105
-11.349
0.300
4.211
0.1762
0.1529
ApecompDur
+36.9%
+2.1%
+6.798
+13.4%
+11.8%%
-9.1%
-4.6%
-16.3%
+27.31%
We first benchmark in Table the the molecular synthesizability of 11 classical SBDD methods, from which some interesting observations can be made. For example, D3FG and DecompDiff, which previously shown to perform well binding due to the consideration of fragmental and de- composable priors, have relatively poor synthesizability. Instead, some earliest methods, such as FLAG and GraphBP, perform well in terms of synthesizability despite their simplicity.
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Further, we demonstrate the effectiveness of "TaggeDiif/DeemmDDffffC of the syathesizability classifier driven SBDD paradigm, in terms of molecular synthesizability using two SBDD methods the backbone model. While there are significant inconsistencies across various synthesizability metrics, as analyzed in Figure [ when SYNC is integrated into our classifier-driven SBDD paradigm, nearly all synthesizability metrics consistently improved. It can be found that TargetDiff-Gunn and TargetDiff- DPO both outperform vanilla TargetDiff" on all 9 metries, and Targe(Difl-Guiee achieves the best performance on SYNC. Besides, TargelDiff-Guide performs much better than TargetDiff-DPO on the SYNC metric, since it is directly guided by gra- dients from SYNC. However, the synthesizability of TargetDiff- as DPO implicitly regulates the model to generate more favorable molecules. More importantly, TargetDiff-DPO enjoys better inference efficiency because no longer requires tedious and complex calculations of gradients as TargetDiff- thesizability gains on 9 metrics, while DecompDift-DPO falls short only on RAScore and GASA. The performance gain is relatively lower than TargetDiff, which we hypothesize is caused by the inner molecular complexity of DicompDiff since it generates larger molecules and is naturally less synthesizable than TargetDiff. Note that we only take these two SBDD methods great potential of our synthesizability classifier-driven paradigm, and do not claim any brand-new generative models. More discussion on properties of generated molecules is in Appendix E
and -DPO, two means
still benefits from fine-tuning,
does. DecompDiff- Guide also achieves universal syn-
to show the
6.3 BINDING AFFINITY COMPARISON
Table Comparison of binding affinity for var- ious SBDD methods. Performance gains and
We have controlled the generation of molecules in the synthesizable chemical space, but have not imposed any restrictions on the binding of ligands to the pocket. Therefore, we fur- ther explore whether our paradigm would harm the performance of vanilla backbone models in binding-related metrics. As shown in Table [ we find that generated molecules enjoy sim- ilar binding affinity within limited fluctuation
drops are marked in green and red, respectively.
Methods
Vina Score (
Vina Min ())
Dock (.)
Avg.
Med.
Avg.
Med.
Avg.
Med.
3DSBDD
-3.55
6.45
D3FG
-2.59
-6.88
DiTBP
FLAG
GraphBP
.480
4.70
LIGAN
.6.33
-6.20
MclCraft
-6.51
-8.14
-8.14
-8.42
-9.25
-9.20
when compared to the vanilla backbone mod-
Pockst2Mal
VoxBind
4..94
-5.14
-4.70
-2.11
-2:54
-5.82
-2.55
-7.15
-6.79
els. This suggests that our paradigm not only significantly improves synthesizability, but also
TargetDiff
-5.33
-6.08
-6.47
-6.60
-7.42
-7.68
TargelDiffGuude Guide
-5.63
-6.39
-6.76
-6.85
-7.75
-7.83
preserves or even enhances binding affinity in
-0.30
-0.31
-0.29
-0.25
-0.33
-0.15
TirgeDDiT-DrO
-5/40
particular situations at the same time. Synthe-
Ataeeenn
-0.07
-0.02
-0.04
+0.05
-0.16
-0.02
sizable molecules usually adopt stable confor- mations that better complement protein pockets. In contrast, unfavorable molecules often contain unrealistic substructures, such as distortions or
DecompDitt
+3.50
-5.80
-6.57
-7.22
.8.39
-8.43
DecompDiff Guide
-3.64
-5.84
-6.54
-7.45
-8.00
-8.44
-0.14
-0.01
+0.03
-0.23
-021
-0.01
-3.48
Aucomppar
+0.02
+0.07
+0.13
-0.12
-0.18
-0.25
oversized rings, which hinder pocket binding.
6.4 CASE STUDY ON GENERATED MOLECULES
We provide a case of molecules generated by TargetDiff, TargetDiff-Guuie and TargetDiff-DPo along with an analogy projected by ChemProjector (Luo al. (024) from HS molecule generated by TafgetDiff, and we report their binding affinities. We can observe that TargetDiff- and TargetDiff-DPO enhance the binding (docking) strength to the protein pocket, despite not utilizing any additional binding constraints. Another advantage lies in the fact that our methods improve synthesizability mainly through minor modifications local substructures of the original molecules, which is quite different from previous works, such as ChemProjector, which projects each molecular fragment onto purchasable building blocks, leading to more severe structural changes. Moreover, methods can directly generate ES molecules with excellent binding affinity and synthesizability in one single step and do not require extra 2D-to-3D mapping, ranking of top molecules, and multiple times of molecule docking. For more details and case studies, please refer to Appendix F
6.5 ABLATION STUDIES
Edge Reconstruction As mentioned in Section 5.2 we ignore edge features to mitigate incon- sistencies from neglecting bonds during diffusion. Theoretically, single, double, and triple bonds can be implicitly captured by neighboring atoms, while bonds are preserved by assigning distinct tokens to atoms on æ rings. To test this design, we compare SYNC with S SYNC-EEdee Table only minor gap, suggesting that modeling edge features is not decisive for synthesizability.
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Vina dock: -8.847
Vina dock: -9.104
Vina dock: -10.206
Vina dock: -8.208
(a) TifgetDiff (HS) (h) TargetDiff-Cuide (ES) (c) TargetDift-DPO (ES) (d) ChemProjector (ES)
Figure 3: Examples when Tiff is applied as the backbone on PDB 5IJU. All ES molecules are verifed by SYNC and AizynthFinder. We first imput the HS molecule in (a) to ChemPro- jector, which generates 2D analog molecule. Then we generate its 3D conformation and dock it to the protein pocket by AutoDock Vina get the conformation and binding affinity in (d). The syn- thesizability gain is not derived from correcting basic errors in HS molecular structures, but rather from the rich knowledge in SYNC that enables the modification of atomic types and coordinates.
3D Conformation Most SBDD methods generate full-atom types and coordinates simultaneously. making SE(3)-invarians SYNC essential. Beyond our default 3D SYNC, we also test variants using ID fingerprints (FPS), ID SMILES, and 2D graphs. As shown in Table 4 3D SYNC ranks first on three datasets and second on one, highlighting the importance of 3D information for synthesizability.
Table Performance comparison whether or not to con- sider edge features and information of different modalities.
Synthesizabliiy Changing
Gulcance
Sirang
SWWCK
Vanila
Methods
TSI
TS2
T83
Nonpher-
Enamine
SYNC (3D)
0.9911
0.8406
0.7564
0,9313
0.9520
22
SYNC-EEge
0.9846
0.8391
0.7575
0,9250
0,9366
202
400
500
200
1003
Stop
SYNC-ID-FPS
0.9899
0.7422
0,8250
0.9370
Figure 4: Curves of synthesizability with diffusion steps utilizing different guidance strategies PDB IWN6.
SYNG-ID-SMILESS
0.9947
0,8355
0.7917
0.7250
0,9321
SYNC-2D-Graphs
0.9879
0.8332
0.7722
0,9188
0,9473
apply SYNC guidance SBDD methods in multi-step manner. Here, we provide analysis to validate its effectiveness. Figure4 four guidance settings against vanilla TargetDiff by plotting the percentage of synthesizable molecules over diffusion steps. Our main setting, Multi-Step, begins guidance in the latter half of training (from step 600). Although synthesizability improves, it often drops in the final steps; applying multiple guidance steps at this stage produces sharp increase. Adjusting guidance strength (Strong Weak, controlled by Ay and X₂) yields very different outcomes: Strong guidance boosts synthesizability but breaks molecular structures into fragments (Appendix G), while Weak guidance has little effect. The Constant setting. where guidance is applied throughout, performs worse than the vanilla version, suggesting early guidance is ineffective
Guidance Settings As discussed in Section 5.2
harmful since molecules not yet well-formed
DPO Settings To examine the effect of DPO, track the percentage of synthesizable molecules using SYNC and the SA score Ertl & Schuffenhauer (2009) during fine-tuning. Both metrics rise steadily in the first 2,000 iterations, peaking at iteration 1,800. which we finally se- lect as the optimal checkpoint. Further training, however, leads to performance fluctuation and generates fractured molecules, most likely due to overfitting on the pairwise preference dataset. Recall that we have sampled 3,431 ES-HS pairs and use a batch size of 4, the peak performance corresponds to roughly two training epochs.
Synthesizability Changing Curve of DPO
0.65
SYNC
40
0.64
SYNCASS
0.63 3
0.62
30
1000
2000
3000
Figure Curve of synthesizability (SYNC per- centage and SA Score) with DPO iterations on the protein (pdb: 1WN6), when TargetDiff is applied as the backbone model as showcase.

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7 CONCLUSION
In this work, we first provide a comprehensive benchmark on synthesizability metrics, showing their drawbacks and the lack of consistency. We then propose simple yet effective SYNthesizability Classifier (SYNC), which performs the best on the benchmark datasets. With SYNC as a plug- and-play module, we construct synthesizability classifier-driven SBDD paradigm, with two classic SBDD methods, TargetDiff and DecompDiff, to generate highly synthesizable molecules while pre- serving their binding affinity. Extensive experiments demonstrate that our methods can effectively improve chemical properties and synthesizabilityy metrics the generated molecules. Even though promising results show our potential to meet more practical needs of the pharmaccutical industry, acknowledge this paper is exploratory work for improving synthesizability with respect to de novo designed molecules. We leave limitations such as wet-lab confirmation for future work.
ACKNOWLEDGEMENTS
This work was supported by National Science and Technology Major Project (No.2022200111101) National Natural Science Foundation of China Project (No.624B2115 No. No.U221204227 the Na- tional Science and Technology Major Project of China (No. 1301603), the Project (No. WU2025B006) from the SOE Dean Special Project Fund (SOE-DSPF) Program of Westlake University, Project (No. WU2022A009) from the Center of Synthetic Biology and Integrated Bioengineering of Westlake University and the Hangzhou Postdoctoral Daily Funding Program (No.103140026582500, 2025).
REPRODUCIBILITY STATEMENT
We have taken several steps to ensure the reproducibility of our results. First, all applied datasets are open-sourced and can be directly downloaded. Second, we have appended a GitHub link in the abstract, which contains source codes for SYNC, haaiit/DeooopDiffGGuieleeeell conduct detailed ablation studies to show hyperparameters. Finally, we describe all datasets and experiment details in Appendix
Third,
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USE OF LLM
We only apply LLM for checking spelling and grammar.
A COMPARISONS WITH GFLOWNET-BASED METHODS
GFlowNet-basee [Bengio et al., 2023 methods (Shen et al. 2023 Koziarski et al.) 2024; Cretu et al, 2024 Sco et al. 2024g have emerged to show great potential in generating synthesizable molecules. However, these methods do not produce 3D conformations, which are critical in structure-based drug it applies GFlowNet for synthesizable molecule generation and generates 3D conformation when designing molecules for the target protein pocket. Therefore, we take it as a representative work of this series. However, shown in Figure [A] the generated molecules clash with the protein pocket, distorting their shapes. We set the protein surface be partially transparent in order to visualize the regions where clashes occur with the protein, i.e., the parts of the molecule shown in lighter colors in the figure. This demonstrates that, despite the tremendous success of GFlowNettbased and building block-based methods in designing synthesiz- able molecules, their ability to design ligands for protein pockets remains limited. Conversely, our method fills this gap by modifying the type and coordinate of each atom to improve synthesizability.
design. CGFlow (Shen et al. 2025) stands
Clash with protein
(b) Clash with protein
(c) Distorted molecule shape
Figure Al: Examples of ligands generated by CGFlow on 1K9T protein target.
B DATASET & PLATFORM DESCRIPTION
We benchmark total of 9 synthesizabilitty metrics on 5 datasets. We denote easy- and hard- to-synthesize molecules as ES and HS for simplification in the following. Here are the detailed descriptions of these involved datasets:
TS1 TSI is directly obtained from SYBA (Voršilák et al., 2020). which contains 3,581 ES molecules randomly selected from the ZINC15 database and 3,581 HS molecules picked from the GDB-17 (Ruddigkeit et al. 2012) database.
TS2 TS2 is obtained from ChEMBL, GDBCHEMBL, and GDBMedChem, which is used as the test set RAscore TTakkar al. 2021) and displays broader distribution in the chemical space (Thakkar et al. 2021). TS2 contains 17,348 ES molecules and 13,000 HS molecules.
TS3 TS3 is composed of 900 ES and 900 HS molecules, which are collected from various sources and are more challenging to distinguish, including (1) 203 ES molecules selected from the ZINC15 database used by SYBA (Voššilák et al.) 2020): (2) 40 HS molecules derived from the evaluation of 296 published compounds by experienced medicinal chemists (Huang et al. 2011 Ertl Schuf- fenhauer 2009; Boda et al.) 2007 Fukunishi et al.] 2014); (3) 518 molecules collected from the Molecule Libraries Small Molecule Repository (MLSMR) from PubChem (Kim et al. 2016); (4) 1039 molecules from Sheridan et al.) 2014) whose complexity is evaluated by chemists.
Nonpher Test The compounds in the Nompher-Test dataset obtained by analyzing 296 pub- lished structures, whose case of synthesis was assessed by experienced medicinal chemists. Data sources described in (Voršilák & Svozil 2017). It contains 120 ES and 40 HS molecules.
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Enamine We utilize molecules from the Enamine US Stock catalog, retrieved in October 2023 (Enamine 2023), which contains 211,220 molecules. Since all molecules are purchasable, they are all categorized ES molecules.
Train Set First, small molecules with an SAscore between 3.5 and 6 from the ChEMBL GGulltn et al. 2017) and GDBChEMBL (Bühlmann Reymond 2020) databases are collected. SAs- core, although the authors recommend 6 as the classification threshold, it can be found from several studies that the synthesizability of molecules distributed between 3.5 and 6 is quite ambiguous (a large number of HS compounds with an SAscore less than 6), and thus closer to the decision boundary (Gao & Coley 2020; Thakkar et al. (021). Then, the potential synthetic routes for the molecules
predicted by Retro* (Chen al.) 2020) (a multistep retrosynthetic planning algo- rithm that predicts the synthetic routes for target molecules). The molecules that require less than 10 synthetic steps marked ES, and those that need more than 10 reaction steps cannot be successfully predicted by Retro are labeled as HS. Second, except for the sampling through ret- rosynthetic analysis, the data sets used in SYBA (Voršilák al. 2020) are also used in our study. The positive examples were collected from the purchasable compounds in the ZINC15 (Sterling & Irwin 2015) database, and the negative examples were generated by Nonpher (Voršilák & Svozil, 2017) (a molecular morphing algorithm for the generation of HS structures). Pairs of compounds (ES and HS) with fingerprint similarities higher than 0.35 are selected to improve the model's sen- sitivity in distinguishing similar structures. Finally, the data set consisting of total of 800,000 compounds was randomly split into the training. validation, and test set by a ratio 8:1:1 based on stratified sampling. Note that these works are done in Yuu et al. 2022 we just follow their settings.
Platform We train and evaluate our SYNC a single NVIDIA A100-80G GPU. We also use A100 to complete TargetDiff/DeecmpDiff DuuieeommpiiiGiGiiiie///OOOOO to sample single pocket using TifgetDiff, and 50 minutes using DecompDiff.
Guidance/DPO experiments. It takes about 20 minutes
C THRESHOLDS FOR SCORING METRICS
The thresholds for each metric listed in Table AT Notably, we dynamically adjusted the thresh- old with granularity of for FSScore, since no reference value is reported to our best knowledge.
Table Al: Thresholds of each metric.
SA
SYBA
FSScore
RAScore
SCScore
0.61
-18.6
-13
0.5
3.1
We visualize the overall distributions of metrics to show the sensitivity of each threshold Fig- ure A2 Due space and time limitations, visualize the distributions of SA, SYBA, RAScore TSI. Based on these figures, the threshold is within an appropriate margin.
CASA TESTL Butrreed
GASA TESTS DitrEbtce SIDA
CASA, TES Bult bec
24h0
SA Distribution TSI
(b) SYBA Distribution TSI
(c) RAScore Distribution TSI
Figure A2: Threshold and score distribution on TSI.
However, there is no reference value for FSScore. From the visualizations Figure A3 on different datasets, FSScore cannot distinguish molecules perfectly, dynamically adjust the threshold, so the cumulative performance on 6 datasets is maximized. We can observe that FSScore fails to distinguish molecules in most scenarios.
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(a) FSScore Distribution on TS2
(b) FSScore Distribution on TS3
(c) FSScore Dist. on Nonpher
Figure A3: Distribution of FSScore different datasets.
D LNTERPRETABILITY OF SYNC
3D Conformation Conformation can provide rich extra information for judging whether molecule synthesizable. Molecules with extreme bond angles are typically more challenging to synthesize. For example, tetrahedrane (Schulman & Venanzi 1974) (C12C3C1C23) remains un- synthesized date due its 60% bond angles, which significantly hinder the laboratory preparation of tetrahedrane. Another example is steric hindrance, where atoms groups within molecule are brought too close together space, resulting in unstable molecules. For example, tetra-tert- butylethylene (Lenoir et al. 2006) remains unsynthesized to date due to its overcrowded spatial dis- tribution. We visualize their 3D conformation Figure A4 As shown Table A2 when tested on all synthetic metrics, only AizynthFinder and SYNC can properly classify these molecules hard- to-synthesize molecules, whereas all other methods fail to give a correct answer. AizynthFinder pre- diets correctly because these molecules have no precursors, whereas SYNC benefits from its unique 3D structural view. However, all other methods fall short due to the neglect of 3D information.
(a) Tetrahedrane. (Extreme bond angles.)
(b) Terra-ert--uutlleehyleee (Steric hindrance.)
Figure A4: Examples of two hard-to-synthesize molecules, due to their uncommon 3D structures.
Table A2: Predictions made by synthetic metrics. / stands for prediction correct (predicted as hard-to-symthesiize) and stands for prediction is incorrect (predicted easy-to-symthesize)
Molecule
Aizynth SA* SYBA Nonph. FSSc. RASc. SCSe.
GASA SYNC
Tetrahedrane
x
X
X
X
X
V
Teura-tertbbutyleehyllen
X
X
X
X
X
Gradient Visualization To further investigate what structural pattern can influence the judgment of SYNC, we visualize the gradients of molecules from three sources in Figure A5 From red to blue, the importance of each atom is descending order. (a) A hard-to-synthesize molecule
from TS2. Red atoms are in the center of this molecule, indicating that SYNC does not prefer overlapping rings. (b) An example from Compas (Wahab et al. 2022). which contains thousands of computational photoclectric molecules and is hard-to- -ard-tto-ymttesiize. This also shows similar lack of preference for overlapping rings. (c) An SBDD BBDD-gencrated molecule. SYNC significantly dislikes the heptatomic ring structure, which has far less frequency of aromatic rings.
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(a)
(b) Compas
(c) SBDD
Figure A5: Three hard-to-synthesize samples and the gradients backpropagated by SYNC.
E PROPERTY OF GENERATED MOLECULES
Following previous works (Guan 2023 2024), we discuss drug-likenessQEED (Bickerton et al. 2012)), molecule diversity. uniqueness, and novelty in Table A3 It turns out that the diversity of Tafget Guide and TargetDiff-DD DPO is higher than TargetDiff, showing the superiority of our method. The QED and diversity of DecompDift-Gutte
both improved. While QED for DecompDiff- DPO slightly drops, the diversity significantly increases, showing DecompDPO generates a variety of molecules. This conclusion is also reflected by uniqueness and novelty, all the methods generate highly diverse molccules (uniqueness) and not over similar (novelty) to those in the training set.
Table A3: The statistical analysis of generated molecules.
Methods
QED
Diversity (Mean)
Diversity (Median)
Uniquenesst
Noveltyt
TargetDiff
0.487
0.744
0.738
0.978
0.981
TargetDiff-Guide
0,512
0.761
0.758
0.972
0.981
TargetDiff-DPO
0.503
0.765
0.766
0.976
0.977
DecompDiff
0.393
0.596
0.588
1.0000
0.995
DecompDiff- Guid
0.394
0.597
0.589
1.0000
0.994
DecompDiff-DPe
0.377
0.652
0.645
1.0000
0.996
Table A4:
The pass ratio of each test provided by Buseeus
Methods
Bond Lengths
Bond Angles
Steric Clash
Ring Flatness
Double Bond Flatness
Internal Energy
TargeDDfff
0.973
0.762
0,903
1.000
0.999
0.712
TangeDDff-Guide
0.977
0.734
0.941
0.999
1,000
0.695
TargeDiiff-DPO
0.978
0.772
0.945
1.000
0.999
0.753
DecompDiff
0.942
0.919
0.908
0.999
0.959
0.736
DecompDiff-Gull Duff-Guide
0.934
0.916
0.903
1.000
0.966
0.740
DecompDitt-DPO 0.953
0.885
0.947
0.999
0.937
0,789
We further apply PoseBusters (Buttenschoen et al. 2024) to validate the structural validity of are applied evaluate generated
the generated molecules comprehensively. total of molecules. and we report the pass rate of each test. Bond length evaluates whether bonds are too long or too short; bond angle evaluates whether bond angles are falling in proper distributions; steric clash detects molecules intertwined and atoms clashing: ring and double bond flatness as- sure generate aromatic rings and double bonds are flat; internal energy blocks twisted rings that energetically unfavorable. As shown in Table A4 the pass ratio of each test is highly related to the performance of backbones. When tested on TifgetDiff, the pass rate for each task almost increases, where experiments DecompDiff show similar results. Despite occasion performance drops, our methods show overall improvements in all scenarios. This is attributed to our method being able more effectively guide the generative model to produce structurally more reasonable
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small molecules, such as making rings and chemical bonds flatter, thereby generating valid molecu- lar structures.
F
CASE STUDIES
Projection- based methods (Luo et al., 2024 Gao et al. 2024) take target molecule as input and generate analog molccule that guaranteed to be synthesizable for drug discovery. In particular, they decompose the molecule into fragments by breaking chemical bonds according to predefined reaction templates, then identify similar commercially available building blocks to replace these fragments, thereby constructing new molecule. We use TargetDiff-generate molecules examples. It can be observed in Figure A6 that our method can only generate ES molecules but also molecules with better binding affinity, showing superiority to Tragment-based based methods.
hard-to-synthesize
Vina dock: -5.882
Vina dock: -6.221
Vina dock: -6.351
Vina dock: -3.060
(a) Vanilla TargetDiff
(b) TargetDiff-Guuin
TargetDiff-DD
(d) Project- Based Method
Vima dock: -5.387
Vina dock: -5.640
Vina dock: -6.151
Vina dock: -6.146
(e) Vanilla TafgeDDiff
(I) TargetDiff-Guuie
(g) TargelDifI-DPO
(h) Project-Based Method
Figure A6: Examples when TargetDiff is applied the backbone of our classifier-driven paradigm, where the protein targets are 3KC1 (top) and 4FIM (bottom).
G GUIDANCE TORN MOLECULES
Here, we present some failure cases in Figure A7 in which molecules are torn into pieces as they receive overly strong guidance signals from SYNC. While some basic functional groups can still be observed, the scaffolds of molecules are completely broken, forming isolated molecule parts.
Figure A7: Visualizations of some failure cases, where molecules are generated with overly strong guidance, leading fatal errors.
18


Reviews:
[{"review_id": "IxbVOeRNTI", "reviewer": "Reviewer_ejoL", "summary": "The paper tackles a central bottleneck in structure-based drug design by focusing on synthesizability. It first assembles five datasets with easy- and hard-to-synthesize labels and benchmarks eight widely used metrics across eleven SBDD generators, showing large and sometimes contradictory judgments among existing metrics. To address this, it introduces SYNC, an SE(3)-invariant, 3D-aware classifier that predicts synthesizability from ligand conformations. SYNC is then used as a plug-and-play control signal in two paradigms, guided diffusion and direct preference optimization, to steer TargetDiff and DecompDiff toward molecules that are easier to make while largely maintaining docking affinity. Extensive experiments, ablations, and runtime measurements suggest SYNC attains strong accuracy with low inference cost and yields consistent gains in synthesizability without notable loss in binding metrics.", "questions": "-", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The strengths lie in the clarity and importance of the problem framing, the breadth of the benchmark, and the empirical finding that many classical synthesizability metrics disagree in practice. The proposed classifier is well motivated for SBDD because it explicitly consumes 3D information and is designed to be SE(3)-invariant and fast, which are practical requirements for generation-time control. The integration pathways are simple yet effective, and results are consistently favorable across multiple metrics and two different backbones, with helpful tables that isolate improvements and show that binding scores remain similar or sometimes improve. The ablations are thoughtful, covering 3D versus 2D or 1D inputs, edge modeling choices, guidance schedules, and DPO iteration behavior, which strengthens the case that the observed gains are not brittle. The work also pays attention to computational cost and scalability, an often overlooked but critical aspect for large-scale screening and iterative design.\n\nHere is the itemized summary for rebuttal and discussion:\n\n- Clearly frames the practical importance of synthesizability in structure-based drug design\n- Builds a broad benchmark with multiple datasets, metrics, and generators, revealing contradictions among common metrics\n- Introduces a fast SE(3)-invariant, 3D-aware classifier that matches SBDD needs\n- Provides two simple integration paths, guidance and DPO, that act as plug-and-play controls\n- Improves synthesizability consistently while largely preserving docking-based binding scores", "weaknesses": "The weaknesses stem from evaluation design and possible circularity. The ES and HS labels are proxies rather than wet-lab confirmations, so real-world synthesizability remains inferred rather than demonstrated. Several baselines require thresholding or stock assumptions that can bias outcomes, and FSScore’s threshold is tuned over the same datasets, which may inflate apparent fairness. Because SYNC both evaluates and guides generation, parts of the analysis risk becoming self-referential, even if cross-metric reporting partly mitigates this. The DPO pairs are constructed using SYNC’s guidance, which could further entangle training and evaluation signals. The guided diffusion approach introduces extra compute and hyperparameters, and the K-nearest-neighbor edge construction and valency heuristics during guidance, while pragmatic, could be fragile across chemistries or larger rings. Reported binding comparisons rely on docking scores, which are useful but imperfect surrogates; stronger tests such as pose validity checks or physics-based rescoring would raise confidence. Finally, although SYNC often ranks first, margins narrow on some datasets, and the method does not outperform retrosynthetic tools on their strongest cases, leaving open how best to combine fast 3D classifiers with pathway-level verification for end-to-end design.\n\n---\n\n1. Uses proxy labels rather than wet-lab validation, leaving real-world synthesizability unconfirmed\n2. Several baselines depend on thresholds and stock assumptions that can bias comparisons, with possible tuning leakage\n3. Risk of circularity since SYNC both evaluates and guides, and DPO pairs derive from SYNC-guided samples\n4. Guidance adds compute and hyperparameters; KNN edges and valency heuristics may be brittle across chemistries\n5. Reliance on docking scores without stronger pose checks or physics-based rescoring limits confidence\n6. Advantages narrow on some datasets and do not exceed retrosynthesis tools on their best cases\n7. External validity to more diverse targets and chemotypes remains to be demonstrated", "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": "Ersv9OSZnD", "reviewer": "Reviewer_FJHL", "summary": "The authors compared classical synthesizability metrics for classical SBDD methods and observed inconsistencies in the rankings. They thus devised a framework called SYNC as a new metric that achieved more consistent reports on various datasets. Also, since they designed such a framework to be fast, differentiable, and SE-invariant, they demonstrated that such a metric can be incorporated into diffusion guidance and DPO. Experiments showed that such incorporation benefits the synthesizability without hurting much of the affinity. They performed ablation studies to justify their choice for the framework as well as guidance, such as the use of 3D information and multistep guidance.", "questions": "1. Why is SYNC so low on the generative models’ results compared to the constructed datasets such as TS1 and TS2? I wonder how much of this drop is due to the limited capacity of the model and how much is caused by its lack of generalization to out-of-distribution samples.\n\n2. Why is there a sudden spike in SYNC during the final steps of the multi-step process? Figure 4 may require additional explanations.", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "1. The presentation is straightforward and clear. \n2. The designed framework and model are well-suited for this task and provide even more benefits, such as speed and robustness. \n3. The experiments are strong and support the claims well.", "weaknesses": "1. Since SYNC is trained on the same datasets shown in Table 1, it is not entirely fair to call it \"surprisingly outperforms other baselines by a wide margin\", as it is data-centric. This can be related to Question 1.\n\n2. In Table 2, many color markings are off. Minuses are marked as both green and red. This could potentially lead to false interpretation. \n\n3. SYNC's framework can be better shown with some illustrations. So far, it was only described in plain text and mentioned only halfway down in the paper. If this is one of the main contributions, please be more explicit in the main section.", "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": "UjF4fkJh1Y", "reviewer": "Reviewer_kwwZ", "summary": "The paper introduces SYNC, a 3D-aware, SE(3)-invariant classifier for estimating molecular synthesizability in Structure-Based Drug Design (SBDD).\nIt benchmarks eight classical synthesizability metrics across 11 SBDD methods, finding strong inconsistencies, and proposes SYNC as a unified, differentiable, and efficient alternative.\nSYNC is further embedded into two SBDD paradigms—guided diffusion and Direct Preference Optimization (DPO)—to generate ligands that are both synthesizable and high-affinity.\nExtensive experiments (five curated datasets, large-scale SBDD benchmarks) show SYNC achieves higher accuracy and efficiency than existing metrics and improves molecule synthesizability without hurting docking scores", "questions": "1. How sensitive is SYNC’s performance to the training distribution—does it generalize to unseen chemical scaffolds or pocket types?\n\n2. Can SYNC outputs be calibrated to provide probabilistic synthesizability scores usable in Bayesian optimization?\n\n3. How does SYNC compare with recent LLM-based synthesis predictors (e.g., ChemGPT retrosynthetic models)?\n\n4. Does guided diffusion introduce mode collapse toward overly simple molecules?\n\n5. Are there interpretability tools (e.g., attention maps or feature attribution) to rationalize why SYNC deems a molecule synthesizable?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "This paper makes a timely and practical contribution to structure-based drug design by systematically benchmarking existing synthesizability metrics and introducing a fast, 3D-aware, SE(3)-invariant classifier (SYNC) that effectively bridges molecular generation and realistic synthesis feasibility. The integration of SYNC into both guided diffusion and DPO paradigms is elegant, demonstrating consistent improvements in synthesizability across multiple SBDD backbones without sacrificing binding affinity. The experiments are comprehensive, covering five datasets, eleven baselines, and detailed ablations, making the work both reproducible and impactful for real-world drug discovery.", "weaknesses": "While technically sound, the core innovation of SYNC is incremental, primarily reusing standard EGNN architectures for classification. The reliance on proxy “easy/hard-to-synthesize” labels without experimental validation limits the practical reliability of results. Certain methodological aspects, such as gradient guidance stability, parameter sensitivity, and DPO training dynamics, lack clarity. Moreover, evaluations are restricted to two SBDD backbones and omit comparisons with more recent LLM- or synthesis-pathway-based approaches, slightly constraining the generality and novelty of the proposed framework.", "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)