Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Structure-Based Drug Design
Authors: Kangyu Zheng
Kai Zhang
Jiale Tan
Xuehan Chen
Yingzhou Lu
ZAIXI ZHANG
Lichao Sun
Marinka Zitnik
Tianfan Fu
Zhiding Liang
Venue: Accepted by TMLR ()

Abstract: Currently, the field of structure-based drug design is dominated by three main types of algorithms: search-based algorithms, deep generative models, and reinforcement learning. While existing works have typically focused on comparing models within a single algorithmic category, cross-algorithm comparisons remain scarce. In this paper, to fill the gap, we establish a benchmark to evaluate the performance of fifteen models across these different algorithmic foundations by assessing the pharmaceutical properties of the generated molecules and their docking affinities and poses with specified target proteins. We highlight the unique advantages of each algorithmic approach and offer recommendations for the design of future SBDD models. We emphasize that 1D/2D ligand-centric drug design methods can be used in SBDD by treating the docking function as a black-box oracle, which is typically neglected. Our evaluation reveals distinct patterns across model categories. 3D structure-based models excel in binding affinities but show inconsistencies in chemical validity and pose quality. 1D models demonstrate reliable performance in standard molecular metrics but rarely achieve optimal binding affinities. 2D models offer balanced performance, maintaining high chemical validity while achieving moderate binding scores. Through detailed analysis across multiple protein targets, we identify key improvement areas for each model category, providing insights for researchers to combine strengths of different approaches while addressing their limitations. All the code that are used for benchmarking is available in https://github.com/zkysfls/2025-sbdd-benchmark

Published in Transactions on Machine Learning Research (02/2026)
Beyond Affinity: A Benchmark of 1D, 2D, and 3D Methods Reveals Critical Trade-offs in Baacturr-Based Drug Design
Kangyu Zheng
zhengk5@rpi.co
Deportment of Computer Science Rensselaer Polytechnic Institute
Kai Zhang
kaz321 @lchigh. edu
Department Computer Science and Engineering Lehigh University
Jiale Tan
jialeton Duscc.nnuussc.o edu
Department of Computer Science University of Southenn California
Xuehan Chen
Xuc321 @lehigh. edu
Department of Computer Science and Engineering Lehigh Universily
Yingzhou Lu
byz66@sttnforrt edu
Stanford Medicine Department of Pathology Stanford University
Zaixi Zhang Princeton Universily
=28680@princelom.co edu
Lichao Sun
lis221 @lehigh edu
Department of Computer Science and Engineering Lehigh University
Marinka Zitnik
morinku@hms.harraaee edu
Harvard Medical School
Tianfan
Seiianfon@nnueeduu.co
State Key Laboratory for Novel Software Technology at Nanjing University Nanjing University
Zhiding Liang
zliang Coss. cuhk. was.gg.ccc.ccck..ee edu.hk
Department of Computer Science and Engineering
The Chinese University of Hong Kong
Reviewed on OpenReview: https: opwreevee... net/ nor/f? id= gaTuwirrCCs. Cuw
Abstract
Currently, the field of structure-based drug design is dominated by three main types of algo- rithms: search-based algorithms, deep generative models, and reinforcement learning. While existing works have typically focused on comparing models within single algorithmic cate- gory, cross-algorithm comparisons remain scarce. In this paper, to fill the gap, we establish a benchmark to evaluate the performance of fifteen models across these different algorith- mie foundations by asscssing the pharmacoutical properties of the generated molccules and their docking affinities and poses with specified target proteins. We highlight the unique ad advantages of each algorithmic approach and offer recommendations for the design of fur

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Published in Transactions on Machine Learning Research (02/2026)
ture SBDD models. We emphasize that 1D/2D ligand-centric drug design methods can be used in SBDD by treating the docking function as black-box oracle, which is typically neglected. Our evaluation reveals distinct patterns across model categories. 3D structure- based models excel in binding affinitics but show inconsistencics in chemical validity and pose quality. 1D models demonstrate reliable performance in standard molecular metrics but rarely achieve optimal binding affinities. 2D models offer balanced performance, main- taining high chemical validity while achieving moderate binding scores. Through detailed analysis across multiple protein targets, identify key improvement areas for each model category, providing insights for researchers to combine strengths of different approaches while addressing their limitations. All the code that are used for benchmarking is available in https:: httt:::///gihbbbb cttpliooocoozzeeee foom/zkysflic 1112222222bbs benchhark
1 Introduction
Novel types of safe and effective drugs are needed to meet the medical needs of billions worldwide and improve the quality of human life. The process of discovering a new drug candidate and doveloping it into approved drug for clinical use is known drug discovery (Sinha & Vohora 2018). This complex process is fundamental to the development of new therapies that can manage, cure, or alleviate the symptoms of various health conditions.
Structure-based drug design (SBDD) Bohacek et al. 1996 is a core strategy that accelerates this process by using the three-dimensional (3D) structures of discaso-related proteins to develop drug candidates. This approach is grounded in the "lock and key" model (Tripathi & Bankaitis 2017), where molecules that bind more effectively to target protein more likely to modulate its function, principle validated by mumerous
experimental studies (Honarparvar et al. 2014 Blundell 1996 Lu et al. 2022
Currently, three main algorithmic approaches dominate the drug design field (Bronn et al. 2019 Gao
let al.
2022 Du et al. (022)): search-based algorithms like genelic algorithms (GA) (Jensen 2019 Spiegel &
Durrant 2020 Tripp & HernándezzLobato 2023: Fu et al. 2022a), deep generative models such as variational
autocncoder
[AE) (Gómcz-Bonninn Bombarelli et al. 2018 and autorcgressive models (Luo et al.
2021
Peng
et al.]
2022
Zhang el. al. 2023), and reinforcement learning (RL) models (Olivecrona. et al.
2017 Zhou
et al.
2019 While these models, particularly those using 3D protein representations (Zhang al., 2023;
Luo et al. 2021 Fu et al.) 2022a Peng et al.) 2022), are considered state-of-the-art for generating valid and diverse molecules, a clear comparative understanding across these different algorithmic foundations is lacking, Existing benchmarks often focus on comparing models within the same category (typically deep generative models) and prioritize molecular properties over the crucial evaluation of protein-ligant interactions. (Dul
et al. 2022 Brown et al., 2019 Gao el al., )
A preliminary study first highlighted that 1D/2D models can achieve competitive performance against 3D methods in structuro-basch drug design (Zheng et al., 2024 Our work substantially extends these initial findings by introducing more rigorous and multi-faceted evaluation framework. We curates a comprehensive benchmark that encompasses fifteen models spanning all three algorithmic approaches. We assess model performance through multiple dimensions: traditional heuristic molecular property oracles, docking scores, and pose evaluations all critical metrics for understanding the quality of protein-ligand interactions in the context of drug discovery. Our analysis reveals a notable dichotomy in 3D models' performance: while they excel in docking score optimization, achieving consistently superior binding affinity predictions, they show comparable or sometimes inferior performance in pose quality assessments and other heuristic property evaluations. These findings highlight the need for future structure-based models to better balance docking score optimization with other crucial molecular properties and structural validity metrics.
2
Related Works
Significant progress has been made in benchmarking drug design models Brrwn et al. 2019
Tripp et al.
2021 Huang et al.] 2021 Gao
al. 2022 Polykovskiy et al.] 2020 Harris et al. 2023 Zheng et. al.
2

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Table 1: Representative structure- based drug design methods, categorized based on the molecular assembly strategies and the optimization algorithms. Columns are various molecular assembly strategies while rows are different optimization algorithms.
ID SMILES /SELFIES
2D Molecular Graph
3D Structure-based
Genetic Algorithm (GA)
SMILES-CE GA
Yoshikawa
Graph GA Jensen
2019
2018 ct
Hill Climbing
Brown
MIMOSA
Fu ct al.
2021
ct al
2019
Reinforcement Learning (RL)
REINVENT
Olivecrona
M
Zhou et al.
2019
et
al.
2017
Gradient Ascent (GRAD)
Pasithea Shen et
2021
DST Fu al. 2022b
SMILES SMILES/SELFIES /SELFIES-VAAL
JT-VAE Jin al.
2019
3DSBDD
Luo
al.
Generative Models
BO
Gómuz Gonez-Bombard Bombarelli
2021].
Pocket2mol
Peng
d
2018
ct
al.
2022]
Pocket
Flow Jiang ct al. 2024
ResGen (Zhang
al.
2023
Target
Guan
et
2023
Table 2: Top docking score for each target. Targets in CrossDocking are marked in red, and targets not in CrossDocking are blue.
Model
6CL8
LUWH
70TE
IKKQ
5WFD
7W7C
SJJL
7D42
7S1S
6AZV
2ocket2Mol
-11.56
-14.56
15.72
-14188
-11.30
-13.76
-13.27
-2.76
-2299
-12.36
PocketFlow
-9.42
-11.04
-10.27
-12477
-10.30
-1.52
-11.88
-12.79
-10.16
-11.83
ResGen
-9.71
-7.14
-9.81
-7.88
-8.94
-11.77
-9.71
3DSBDD
-8.61
-12.67
-10.52
-13.66
-11.28
-1..29
-1.67
-11.12
-10.19
-10.13
DST
-8.69
-11.09
-11.41
-10.92
-10.33
-12.14
-12.20
-11.87
-11.54
-10.31
graph-GIA
-8.47
-11.19
-11.15
-10.11
-9.66
-11.85
-10.72
-11.03
-10.47
-9.85
JT-VAE
100.66
-1238
-12299
12.25
-11.65
12.45
11.91
12.79
-11.53
10.60
MIMOSA
-8.64
-1.133
-1.49
11.00
99.11
11.72
-1.85
-1.72
-1..96
100.27
MolDQN
-6.63
-7.38
-7.49
-7.35
-7.79
-7.75
-8.94
-7.30
-8.42
-8.04
Pasithea
-9.25
-11.47
-1.56
-0.455
-10544
12.00
-1.87
-1.766
-11.35
-10.24
REINVENT
-9.06
-11.13
-12.03
-11.19
-10.18
-11888
-11.33
-1.23
-11.32
10066
SMILES-GA GA
-8.83
-10.74
-11.18
-10.47
-9.72
-11.74
-11.29
-11.93
-11.05
-10.46
SMMLES--LSTM-HE
-9.77
-11.50
-12.35
-12.40
-11.21
-13.93
-11.41
-12.84
-12.02
-10.61
SMILES- VAE
-9.35
-1..95
-1306
-1.91
-10.01
-12.11
-1.95
12.01
-12.14
-10.42
TifgetDiff
-7.30
-1.53
-10.07
-9.27
-8.91
-6.10
2024). Benchmarks like Guacamol, Molecular Sets (MOSES), Practical Molecule Optimization (PMO), and POSECHECK have been crucial for evaluating algorithms on molccular propertics and protein-ligand inter- actions. More recently, CBGBench (Lin al.) 2024) introdeeed a comprehensive framework for evaluating various 3D SBDD models across multiple generative tasks (de novo, linker design, scaffold hopping).
Our work builds upon these efforts but occupies distinct niche by providing the first large-scale, cross- paradigm benchmark specifically comparing 1D, 2D, and 3D methods side-by-side for the de novo structure- based generation task under unified evaluation conditions. Crucially, we extend beyond standard metrics by incorporating detailed pose quality assessments using both PoseBuster (Buttenschoen et al. 2024) and PoseCheck (Harris et al. 2023), alongside geometric consistency checks (redocking RMSD analysis), enabling focused diagnosis of the critical 'affinity-validity tradc-off". We benchmark fifteen models across these paradigms, grouping them into three categories based on their molecular representation analyze their distinct strengths and weaknesses in the context of SBDD.
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Average Number of Molecules Calculated by oracles across PDBs
1000
Number of Molecules
800
600
400
200
0
Pocket2Mol
PacketFlow
ResGen
3DSBDD
TargetDiff
Graph GA MIMOSA
MolDON
DST
STAVE
SMILE5 GA
SMILES LSTM-HC REINVENT
Paslthea
SMILES, VAE
Figure 1: The bar chart of average generated molecules that calculated by our selected oracles for each model across all target proteins under given time. 1D methods colored orange, blue is used to indicate 3D methods, and green represents 2D methods.
1D Molecule Design Methods ID molecule design methods use Simplified Molecular-Input Line- Entry System (SMILES) [Weininger 1988) or SELF-refte SeLF-referenning Embedded Strings (SELFIES) (Lo et al. 2023) strings as the representation of molecules. Most 1D methods produce molecule strings in an autoregressive manner. In this paper, we discuss several methods that were developed to produce molecule strings, ei- ther SMILES or SELFIES strings, including REINVENT (Olivecrona et al. 2017). SMILES and SELFIES al.] (2018). SMILES GA (Yoshikawa al. 2018), SMILES-LSTMHHG (Brown et al. 2019), and Pasithea (Shen et al.) 2021 Although SELFIES string has the advantage of enforcing chemical validity rules compared to SMILES, through thorough empirical studics, Gao et al. (2022 showed
VAE (Gómez-Bombarell
that SELFIES string-based methods do not demonstrate superiorily over SMILES string- based ones.
2D Molecule Design Methods Compared to ID molecule design methods, representing molecules using 2D molecular graphs is a more sophisticated approach. molecular 2D reprosentation, graphs are used to depict molecules, where edges represent chemical bonds and nodes represent aloms. There are two main strategies for constructing these graphs: atom-based and fragment-based. Atom-based methods operate one atom bond at time, scarching the entire chemical space. On the other hand, fragment- methods summarize common molecular fragments and operate one fragment at time, which can be more efficient. In this paper, we discuss several methods belonging to this category: MolDQN (Zhou et al. 2019). which uses an atom-based strategy, and Graph GA (Jiang et al. 2024), Multi-constraint Molecule Sampling (MIMOAA) (Fu et al.) 2021), Diffcrentiable Scaffolding Tree (DST) (Fu et al. 2022b JT-VAE (Jin et al. 2019) which use fragment-based..
3D Molecule Design Methods Both 1D and 2D molecule design methods are ligand-centric, focusing primarily on designing the molecule itself. In cenucturebaasee drug design, as pointed out in [Huang et al.) (2021), these models take the docking function as black box, which inputs molecule and outputs the binding affinity score. However, these models fail to incorporate target protcin structure information and consequently sulfer from high computational time (to find binding pose). In contrast, 3D structure-based drug design methods take the three-dimensiona. geometry of the target protein as input and directly generate pocket-aware molccules in the pocket of target protein. In this paper, we cover five cutting-odge structure- based drug design methods: TifgetDiff (Guan et al., (023)) PocketFlow (Jiang et al.) 2024), 3DSBDD Luo
et al. 2021 mmo (Peng et al. 2022 and ResGen (Zhang et al. 2023
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A critical challenge for these 3D methods is ensuring SE(3) equivariance, guaranteeing that rotations and translations of the input protein-ligand system lead to correspondingly transformed outputs, maintaining physical consistency. Different approaches tackle this differently. ResGen, Pocket2Mol, and PocketFlow utilize specialized equivariant components like Geometric Vector Perceptrons (Jing et al. 2021) or oquivari- ant attention processing scalar and vector features. In contrast, 3DSBDD relies on invariant inter-atomic distances for GNN message passing- TargetDiff specifically combines an equivariant GNN with Center of Mass normalization for overall invariance.
3 Models
In this paper, the models we select for evaluation are based on one a combination of the following algorithms. For ease of comparison, we categorize all the methods based on optimization algorithm and molecular assembly strategy in Table !
Genetic Algorithm (GA): Inspired by natural selection, genetic algorithm is a combinatorial optimization method that evolves solutions to problems over many generations. Specifically, each generation, GA will perform crossover and mutation over set of candidates to produce a pool of offspring and kcep the top- k offspring for the next generation, imitating the natural selection process. In our evaluation, we choose three GA models: SMILES GA (Yoshikawa et al. 2018) that performs GA over SMILES string- based space, Graph GA (Jiang et al. 2024) that scarches over atom- and fragment-level by designing their crossover and mutation rules on graph matching.
Variational Auto-Encoder (VAE) and Diffusion: The aim of variational autoencoder is to generate new data that similar to training data. In the molocule generation area, VAE learns a bidircctional map between molecule space and continuous latent space and optimizes the latent space. VAE itself generated diverse molecules that are learned from the training set. After training VAF. Bayesian optimization (BO) is used to navigate latent space efficiently, identify desirable molecules, and conduct molecule optimization. Diffusion models represent another class of generative models. They operate based on a two-stage process: a fixed 'fowward' or 'diffusion' process that gradually adds noise (e.g., Gaussian noise for coordinates, categorical noise for atom types) to the data over a series of time steps, eventually transforming it into a simple prior distribution. A learned 'reverse" or 'denoising' process then trains a neural network to reverse this, starting from noise and iteratively removing the noise at each time step to generate a sample that resembles the original data, distribution. In our evaluation, et al. 2018) uses SMILES string as the input to the
select three VAF-bbaed models and one diffusion-
based model: SMILES-VAE-BO (Gómez-Bomban) VAE model, and SELFIES-VAE-BE uses the same algorithm but uses SELFIES string as the molecular representation. JTTVAE (Jin et al. 2019) instead use a tree-structured scaffold over chemical substructures. TafgetDiff Guaa et al. 2023) design a targot-aware diffusion model that could generate molccules under given protein aloms.
Auto-regressive: An auto-regressive model a type of statistical model that is based on the idea that past values in the series can be used to predict future values. In molecule generation, auto-regressive model would typically take the generated atom sequence input and predict which atom would be the next. In our evaluation, we choose seven auto-regressive models: PocketFlow (Jiang et al.) 2024) is autoregressive flow-based generative models. 3DSBDD {Lo et al.) 2021) based on conventional Markov Chain Monte Carlo (MCMC) algorithms and 2ocket2mol (Peng et al. 2022) choose graph neural networks (GNN) as the backbone. Inspired by Pocket2mol, ResGen (Zhang et al. 2023) used a. hierarchical autoregression, which consists of a global autoregression for lcarning protcin-ligand interactions and atomic component autoregression for learning each atom's topology and geometry distributions.
Hill Climbing (HC): Hill Climbing (HC) is optimization algorithm that belongs to the family of local scarch techniques (eelann & Gomes 2006). It is used to find the best solution to problem among a set of possible solutions. In molecular design, Hill Climbing would tune the generative model with the reference of generated high-scored molecules. In our evaluation, we adopt two HC models: SMILES-LSTM, HC (Brown et al. 2019) uses an LSTM model to generate molocules and uses the HC technique to fine-tune it. Multt--onstraint MOlecule SAmpling (MIMOSA) (Fu et al. 2021 ) uses graph neural network instead and incorporates it with HC.
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Similar to gradient descent, gradient ascent also estimates the gradient direc- tion but chooses the maximum direction. In molecular design, the GRAD method is often used in molecular property function to optimize molecular generation. In our evaluation, we choose two GRAD-based models: Pasíthea (Shen et al., 2021 uses SELFIES as input and applies GRAD on an MLP-based molecular property prediction model. Differentiable Scaffolding Tree (DST) {Fu et al. 2022b) uses differentiable molecular graph as input and uses a graph neural network to estimate objective and the corresponding gradient.
Gradient Ascent (GRAD):
Reinforcement Learning (RL): In molecular generation context, reinforcement learning model would take partially-generated molecule (either sequence molecular graph) state; action is how to add token or atom to the sequence or molecular graph respectively; and reward is the property score of current molecular sequence. In our evaluation, we test on two RL-based models: REINVENT (Olivecrona et al. 2017) is a policy- gradiett method that uses RNN to generate molecules and MolDQN (Zhou et al. 2019 uses deep Q-network to generate molccular graph.
1.0
1.0
graph_gr _a
moldgn dst
0.8
0.8
Curullativ Probability
Cumulative Probability
Jt vae
0.6
0.6
0.4
0.4
smiles_0g __ga
smiles lstm_hc
0.2
0.2
reinvent
pasithea
smiles, vae
0.0
0.0
10a
10a
10a
10a
10
10a
10a
10
Strain Energy (UFF, kcal/mol)
Strain Energy (UFF, kcal/mol)
(a) 1D models
(b) 2D models
1.0
0.8
Cumulative Probability
0.6
0.4
3DSBDD
2a
0.2
PlcketFiew
Target
0.0
10a
10a
10a
10
Strain Energy (UFF, kcal/mal)
(c) 3D models
Figure 2: The cumulative density function (CDF) of strain energy of each model

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Published in Transactions on Machine Learning Research (02/2026)
4 Experiments
In this section, we demonstrate the experimental results. We start with the description of experimental setup. Then, we present and analyze the experimental results, including protein-ligand bindings and pose, pharmaceutical properties of generated molecules (e.g., drug- wing-iikeness and synthetic accessibility), and other qualitics of generated molccules (c.g., diversity, validity).
4.1
Experiment Setup
4.1.1 Oracle
In drug discovery, we need to evaluate the pharmaceutical properties of the generated molecules, such as binding affinity to certain target proteins, drug-likeness, syuthetic accessibility, solubility, etc. These property evaluators are also known as oraele. In this section, we introduce the oracle we chose to evaluate these models. The oracle functions are sourced from either the Therapeutics Data Commons (TDC) (Huang et al. 2022; established Python packages.
2021
(1) Docking Score: Molecular docking is measurement of free energy exchange between a ligand and target protein during the binding process. A lower docking score means the ligand would have a higher potential to pose higher bioactivity with a given target. Compared with other heuristic oracles, such as QED (quantitative estimate of drug-likeness), and LogP (Octanol-vy water partition coefficient), docking reflects the binding affinitics between drug molccule and target (Graff et al. 2021). Our experiments use AutoDock Vina (Eberhardt et al.] 2021) python package calculate the docking score for each generated molecules. We selected ten representative and diverse target proteins, with five sourced from CrossDock (Francoeur) et al. 2020) and five from other datasets. The CrossDock-derived PDB IDs are 6GL8, 1UWH, 70TE, IKKQ, and 5WFD, while the remaining structures are 7WC7, 8JJL, 7D42, 6AZV, and 7S1S. These crystallography structures are across different fields, including virology, immunology, and oncology (Huang et al. 2022 2021
Chang et al. 2019). They cover various kinds of discases such as chronic myclogenons leukemia, tuberculosis, HARS-COVI-22, etc. They represent a breadth of functionality, from viral replication mechanisms to cellular signaling pathways and immune responses.
(2) Pose: Pose evaluation is another crucial oracle in structurc-based drug discovery. It primarily focuses measuring physical and chemical validity aspects of docking, such as chemical consistency, geometric plausibility, and energy-based checks. These assessments help eliminate physically implausible poses that might score well in Vina but are chemically or geometrically invalid. Additionally, it evaluates intermolecular interactions between the ligand and protein, such as steric clashes, to ensure that the predicted poses are not only energetically favorable but also physically realistic in the protein environment. In our experiments, we employed two pose evaluation packages: PoseBuster (Buttenschoen et al., (2024) and PoseCheck (Harris et al. 2023). PoseBuster contains 19 Frae/Faase-style metrics spanning chemical validity and consistency, intramolecular validity, and intermolecular validity. PoseCheck provides numerical values for clashes and
strain energy for each docking pose generated by Vina.
(3) Heuristic Oracles: Although heuristic oracles are considered to be "trivial" and too easily optimized, we still incorporate some of them into our evaluation metrics for comprehensive analysis. In our experiments, we utilize Quantitative Estimate of evaluates & molecule's drug-likeness on a scale from 0 to 1, where indicates minimal drug-likeness and I signifies maximum trug-likeness, aligning closely with the physicochemical properties of successful drugs. SA, Synthetic Accessibility, assesses the ease of synthesizing a molecule, with scores ranging from 1 to 10; a lower score suggests easier synthesis. LogP measures a compound's preference for lipophilic (oil-like) phase over a hydrophilic (water-like) phase, essentially indicating its solubility in water, where the optimal range depends on the type of drug. But mostly the value should be between 0 and 5.
(QED), SA, and LogP as our hcuristic oracles. QED
(4) Molecule Generation Oracles: While docking score oracles and heuristic oracles focus on evaluating individual molecules, molecule generation oracles assess the quality of all generated molecules a whole. In our experiments, we choose three metrics to evaluate the generated molccules of each model: diversity,

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Published in Transactions on Machine Learning Research (02/2026)
validity, and uniqueness. Diversity is measured by the average pairwise Tanimoto distance between the Morgan fingerprints (Benhenda) 2017). Validity is determined by checking atoms' valency and the consistency of bonds in aromatic rings using RDKit's 's molecular structure parser (Polykovskiy et al. 2020). Uniqueness is measured by the frequency at which a model generates duplicated molecules, with lower values indicating more Irequent duplicates (Polykovskiy et. al. 2020).
4.1.2 Model Setup
Inspired by Liu et al. 2024), upt our experiment as follow: For each model, we aim to generale 1,000 molecules for each given target protein. In case there models fail to sample sufficient enough molecules at one run, we will rerun models that failed at most three times with different seed. We do not retrain each model and instead we directly use the checkpoints they provided. We use the receptor information from Liu et al. 2024) when setting target. Aft After we get enough molecules for each model, we apply the four oracles mentioned above to every molecule and collect the results. None of the tested models have prior knowledge of these oracle functions.
Top 10 Average Docking Scores Heatmap
7W7C 5WFD 1KKQ 7OTE 1UWH 6GL8
-8.28
-9.25
-13.87
11068
9.23
-12.41
-10.08
-10.12
-11.12
-1052
-6.99
11..12
-10.72
-10.33
-11.02
-11.13
-14.78
-9.55
-7.03
-10.41
-10.76
-9.94
-11.27
-10.2
-6.93
-10.84
-10. 76
-10.27
-11.54
-11.35
13.53
11.67
99.56
12.71
9.96
9..3
10.90
10.16
6.75
10.13
10.51
9.00
111.11
10.31
11.14
Docking Score
-10.72
-10.26
-9.35
-7.37
-9.20
-10.41
99 74
10
-7.56
11.14
10.37
12.20
8JLL
11.42
20.81
10.77
11.13
11.01
11.09
--11
6AZV 7515 7D42
:12.10
12.25
-8.60
-10.4
-10.92
110.43
-11.22
11.03
6.53
111.15
111.40
-11.01
-11.47
-11.42
--12
:12.49
-9.46
-11.63
9.84
-10.73
.9.98
-11.04
.10.78
.8.01
-11.06
-10.99
-10.46
-11.47
-11.14
--13
11.87
11.49
9.47
9.66
19.78
9.14
10.18
9.78
6.73
10.03
-9.92
.9.41
10.39
10.11
Packet2Mol
PocketFlow
ResGen
SBDD3D
dst
graph ga
Jt vae
mimosa
moldgn
pasithea
reinvent
smiles ga
smiles Jstm hc
smiles vae
targetdiff
Figure The heatmap based on the average of each model's Top-10 docking score for each target protein.
4.2 Experiment Results
In this section, present experimental results. First, analyze the generation performance of each model, including the number of molecules successfully generated and stored, as well generation speed. We then evaluate the oracle results for the generated molecules. Finally, we discuss our observations regarding model performance based on these analyses.
4.2.1 Generation Performance and Efficiency
In our analysis of model generation capabilities, we examined both generation speed and the total number of successfully stored molecules. As specified in the model setup section, each model was tasked with generating
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1,000 molecules per run. We measured generation speed in molecules per minute, calculated by dividing the total generation time by 1,000. The speed results are presented in Table 3
across different targets. 'error' means we fail to record the time due to model error. Targets in CrossDocking are marked in red, and targets not in CrossDocking are in blue.
Table 3: Comparison of model running times
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
8JJ.L
7D42
7S1S
6AZV
SMILES GA
6.23
4.44
5.71
5.16
8.63
6.00
4.58
5.61
6.25
6,60
SMILES-LSTM-HC
1.20
0.97
0.94
1.01
1.11
1.10
1.43
1.31
0.95
0.96
REINVENT
0.54
0.50
0.54
0.54
0.52
0.50
0.50
0.52
0.52
0.59
Pasithea
1.88
етrоr
eτTOr
1.79
1.87
етrоr
error
error
1.78
1.83
SMILES- VAE
0.75
0.68
0.71
eтror
0.75
0.76
0.70
0.78
0.72
0.73
graph GA
3.21
2.92
eLTOr
3.20
3.33
3.47
error
3.40
3.28
3.26
MIMOSA
1.55
1.35
1.46
1,51
1,56
1.38
1.40
1.60
1.48
1.52
MolDQN
eтror
DST
0.48
0.43
0.48
0.48
0.48
0.48
0.43
0.48
0.48
0.48
JT-VAE
1.40
1.07
1.29
1.36
1.39
error
1.21
1.47
1.42
1.35
Tifget
1.18
0.65
0.66
0.64
0.22
0.49
0.45
0.79
0.67
0.28
3DSBDD
1.85
3.02
5.95
2.26
4.67
2.82
3.50
1.68
3.06
2.94
2ocket2mol
66.66
40
40
37.03
90.91
37.52
43.47
45.45
43.47
43.47
PocketFlow
33.33
33.33
27.78
28.57
15.15
16.39
18.86
21.74
23.26
19.23
ResGen
error
225.35
359.71
125
error
333.3
error
172.41
125
111.11
Several models encountered execution issues with specific targets, resulting in 'error' designations. These failures occurred for various reasons: immediate execution errors (as with Pasithea), infinite loop conditions (as observed with MolDQN), and initialization failures due to unsuitable molecular configurations (as seen with ResGen). Notably we observe that ResGen and Target failed to generate molecules on serval targets. For ResGen, we hypothesize the issue comes from its sensitivity to the input pocket definition coupled with its residue-level multiscale protein representation. the specific receptor configurations used our benchmark deviate significantly from ResGen's training assumptions, the initial step of placing the first atom may fail, For TifgetDiff, suspect the low yield of valid molecules to main factors: Inaccurate prediction of the number of atomss to generate, which is done prior to generation based estimated pocket size relative to training data bins; And failures in the post-hoc bond reconstruction step when the diffused atom cloud geometry is unrealistic. It is important to note that the absence of recorded time does not necessarily indicate a complete failure to generate and store molecules.
Among successfully executing models, we first examined 1D and 2D models from the PMO benchmark (Gao et al. 2022), which underwent identical processing. Most of these models achieved average speeds of 1- 3 molecules per minute. SMILES-GA demonstrated superior performance with speeds of 6-7 molecules per minute, followed by Graph-GA at. 3-4 molecules per minute. The relatively modest speeds may be attributed to the computational overhead of PMO's internal oracle calculations, particularly for complex molecular structures.
In the 3D model category, ResGen achieved the highest average speed of approximately 300 molecules per minute. However, failed initiate generation for several targets (6GL8, 5WFD, 1UWH) given our receptor specifications. Pocket2mol maintained the second-highest performance at 40-60 molecules per minute, while PocketFlow operated at approximately 30 molecules per minute.
Regarding the number of stored molccules (Table D), several models consistently approached or reached the 1,000-molecule larget. SMILES-LSTM-HC,, REINVENT, MIMOSA, and Pocket.Fow demonstrated notably stable performance, consistently generating and storing the full 1,000 molecules across most targets. How- ever, some models showed significant variability. TargetDiff particularly struggled, storing fewer than 100 molecules for most targets, with its best performance being 557 molecules for 1KKQ. JT-VAE showed incon- sistent performance, notably generating only 200 molecules for receptor 7WC7 despite strong performance

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with other targets. 3DSBDD displayed varying success rates, ranging from 222 to 1,000 stored molecules across different targets.
4.2.2 Binding Affinities and Poses
Method
Median Strain
graph-GA
14.99
MIMOSA
32.01
MolDQN
11.75
DST
32.06
JT-VAE
49.70
SMILES-GA
38.66
MIILES-LSTM-HG
41.95
REINVENT
33.11
Pasithea
31.99
SMILES- VAE
30.35
3DSBDD
157.11
Pocket2Mol
60.55
PocketFlow
19.21
ResGen
12.05
Tafget
487.72
Table 4: Median strain energies across different methods.
We first take a look about the docking score performance by the Top-1 (Table 2) Top-10 average (Table 10 and Figure 3) and Top-100 average (Table 11]. Overall, the docking scores generally range from -14 to -5, with more negative values indicating better binding affinity. Pocket2Mol consistently demonstrates superior performance across all three tables, with score ranging from between -10 and -15. In contrast, MolDQN shows the weakest performance, with scores consistently around -5 -7. The remaining models typically score between -8 and -12, with 3D models generally achieving the best scores across most targets.
1D models: The 1D models (SMILES-GA, SMILES-SSTM-HC, REINVENT, SMILES- VAE) show consis- tent performance across targets. In particular, SMILES-VAL and REINVENT demonstrate strong results with scores around -10 to -11 in the Top-10 average. SMILES-LSTM-HC exhibits slightly better perfor- mance for CrossDocking targets compared to non-CrossDocking ones. SMILES-GA maintains performance comparable other models but shows higher variability across different targets.
2D models: The 2D models exhibit more diverse performance patterns compared to ID models. DST and MIMOSA achieve scores comparable to 1D models, demonstrating competitive performance. Graph-GA maintains consistency but performs slightly below the top 1D models. VAE scores well but shows notable variance across targets. MolDQN consistently underperforms relative to other 2D approaches.
3D models: Among 3D models, Pocket2Mol emerges as the clear leader, consistently achieving top scores. PocketFlor also performs strongly but shows higher variance between targets. ResGen delivers moderate but consistent performance. 3DSBDD displays the most variable performance among 3D models, achieving some excellent scores but with significant variance.
To statistically validate these observations, performed paired t-tests comparing the average top- 10 docking scores across shared targets for representative models: SMILES-LSTM-HE for 1D model, JT-VAE for 2D model and Pockel2mol for 3D model. We found that. Pocket2mol achieved significantly better scores than both SMILES-LSTM-HC (1D) (absolute mean difference = 1.65 kcal/mo mol, t -5.86, = 0.0002) and JT- VAE (2D) (absolute mcan difference = 1.96 kcal/mol, -6.58, p 0.0001). We also observe that JT-VAE performs slightly better than SMILES-LSTM-HE (absolute mean difference 0.31 kcal/mol, t= 2.34, p 0.044). These results statistically confirm the superior binding affinity prediction of the 3D models.
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The above analysis demonstrates that while 3D models, particularly Mocket2Mol, can achieve the best overall scores, each category of models has its distinct performance characteristics and trade-offs.
We next examine the pose-related results, beginning with the Posebusters evaluation results presented in Table 27 Each value in the table represents the pass rate for all molecules generated by the corresponding model. For Chemical Validity and Consistency tests (including mol pred loaded, mol cond loaded, sanitiza- tion, inchi convertible, and all atoms connected); 2D models demonstrate consistently perfect performance, while 3D models show notable variations. Specifically, 3DSBDD and Pocket2Mol exhibit lower pass rates in all-atom connectivity and inchi convertibility tests. EMILES-basee models show lower but consistent performance in this category, particularly in inchi convertible and all-atom connectivity metrics. Regard- ing Intramolecular Validity tests (covering bond lengths/ /angles, internal steric clash, aromatic ring flatness, double bond flatness, and internal energy), most models perform well except in the Internal Energy category. Here, significant variations are observed, with some models (3DSBDD, Pocket2Mol) showing very low pass rates while others (DST, graph-GA) achieve excellent results. For Intermolocular Validity tests (encompass- ing prolein-ligand maximum distance, minimum distances to (ooleein/oofccoorss /otlaaccorss/ttrrs,,.o.o /aters, and volume overlap with protein/ poteii/ccofactors/ coffccorss/wteess). nearly all models achieve perfect pass rates, with Target being the no- table exception, showing poor performance in minimum distance to protein metrics. In summary, while most models excel at intermolecular validity checks, the key differences emerge in chemical validity and intramolec- ular metrics. 2D models maintain the most consistent performance across all categories, whereas 3D models and SMILES-based approaches display greater variability, particularly in chemical validity assessments.
We also use PoseCheck to calculate the strain energy and the steric clash of the generated poses. A clash occurs when the pairwise distance between protein and ligand atoms falls below the sum of their van der Waals radii, which is physically implausible (Ramachandran et al.) 2011 Buonfiglio et al., 2015). Strain energy represents the internal energy within a ligand during binding, with lower energy typically being more favorable (Perola& & Charifson 2004).
First, we examine strain energy results in Figure and Table which reveal distinct patterns across model dimensions. 3D models show wide variation, with ResGen performing best (median: 12.05 kcal/mol) and TifgetDiff worst (median: 487.72 kcal/mol). 2D models generally perform better, led by MolDQN (11.75 kcal/mol) and graph-GA (14.99 kcal/mol). 1D SMILES-based models show consistent performance, with SMILES-VAE (30.35 kcal/mol) outperforming others in this category.
Turning to clash results shown in Figure 5 Figure G Figure 7 and Table 28 we observe significant variation among 3D models. ResGen demonstrates the bost performance with consistently low clash counts (median 2-4) across most. receptors, while 2oc and Tifg exhibit high variability. In contrast, 1D and 2D models show stable performance with most models having median clash counts no higher than 10. Interestingly, receptor 8JJL consistently proves challenging for models across all categories.
To further evaluate the quality of the poses generated by the 3D models, performed redocking exper- iment. For each molecule, we calculated the Root-Mcan-Square Deviation (RMSD) between the model's original generated pose and the pose found by AutoDock Vina. This metric serves as a sanity check to determine if the generated geometries are consistent with a physics physics-based scoring function. A lower RMSD indicates a better alignment. The results are show in Figure 4 and Table 3 We observe that ResGen pro- duces poses most consistent with Vina, with a median RMSD of 2.21 A and 42.7% of its poses falling under the 2.0 Á success threshold. PocketFlov also performs reasonably well. In contrast, models like 2ocket2Mol and 3DSBDD, which often achieved the highest binding affinities, perform poorly this metrics.
Model
Median RMSD (A)
Std. Dev. (A)
% Poses < 2.0 A
3DSBDD
5.35
2.38
2.97%
2Mol
4.03
1.40
5.40%
Pocket Flow
2.75
1.61
29.69%
ResGen
2.21
1.47
42.73%
TifgotDiff
4.63
1.09
0.16%
Table 5: RMSD statistics comparison of all 3D models
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1.0
3DSBDD
Pocket2Mol
PocketFlow
ResGen
0.8
targetdiff
Cumulative fraction
0.6
0.4
0.2
0.0
4
8
10 12
RMSD (A)
Figure 4: The cumulative donsity function (CDF) of RMSD for all 3D models
In summary, while 3D models often achieve better docking scores, they do not necessarily produce better protein-ligand geometric compatibility.
4.2.3 Pharmaceutical Properties
In this section, we report and analyze the pharmaceutical properties of the generated molecules.
Overall, nearly all the models tested produced the majority of their molecules within the to range. For example, SMILE--LTM-HHC demonstrates remarkable consistency, maintaining a LogP score of 3.39 + 0.60 across all targets. Several models, including Pasithea, SMI,.S-VVA,, MIMOSA, and DST, show stable performance with LogP values between 2.40 and 2.60, indicating good potential for drug-like properties. However, there is significant variability among other models. Pocket2Mol and PocketFlow exhibit wide fluctuations in their LogP scores, ranging from -0.05 to 5.24, suggesting inconsistent control over molecular hydrophobicity. MolDQN consistently generates highly hydrophilic molccules, with LogP scores consistently below -1.0 across largets. 3DSBDD shows the most extreme variability, with scores ranging from -11.36 3.73, and notably large standard deviations.
LogP:
:aDt the results in table 17 those models demonstrate varying levels of performance across different targets. The majority of models, including SMILES- VAE, MIMOSA, and DST, consistently achieve high QED scores between 0.90-0.91, indicating their robust ability to generate drug-like molecules. A second tier of models, including SMILES-GA, SMILES-LSTM-HC, and JT-VAE, maintains reliable performance with scores ranging from 0.86 to 0.88. The graph-GA model shows moderate consistency with scores around 0.84 across all targets. Notably, there are significant. performance disparities among certain models. MolDQN demonstrates considcrably lower performance, with QED scores consistently falling between 0.43-0.49 across all (argets. Pocket.low and 3DSBDD show variable performance, with scores fluctuating between 0.60 and 0.80, suggesting less stability in generating drug-like molecules. The performance patterns remain relatively consistent whether evaluating targets in CrossDocking those not included in CrossDocking, indicating that the models' capabilities are generally independent of the target selection.
SA: Overall, most the models generate molecules with scores between to 3. Several models maintain consistent performance with relatively low variance: SMILES-ISTM-HC (1.93 0.13), MIMOSA (1.95 + 0.12), and DST (1.95 0.14) demonstrate stable synthetic accessibility scores across all targets. However, 3DSBDD shows significantly higher scores with substantial variance, ranging from 2.89 + 0.73 to 6.10 + 0.96.
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MolDQN consistently generates molecules with higher SA scores (2.83-3.28) and notable standard deviations (0.50-0.55). The data indicates that while most models can generate synthetically accessible compounds, there are significant variations in their consistency and the complexity of the proposed synthetic routes.
To provide a more modern assessment of synthetic complexity beyond the heuristic SA score, we also cal- culated the SCScore (Coley al. (018)) and RAscore (Thakkar et al. 2021) for the generated molecules. SCScore predicts the complexity of molecule based on reaction precedence data, with lower scores indicating potentially easier synthesis (typically ranging from to 5). RAscore predicts the retrosynthetic accessibility of molecule based on whether Al synthesis planning tool can find route, with higher scores (near 1) indicating route was found and lower scores (near 0) indicating route was not found. The results of SCScore are in table 21
22 23 and the results of RAscore are in table 24
25 26
We first observe that most 1D and 2D models consistently generated molecules with lower SCscores (often < 2.5). In contrast, some 3D models, particularly 3DSBDD, tended to produce molecules with higher average SCscores (e.g., 3.5 on several largets), indicating potentially greater synthetic complexily. These findings generally reinforce the trends observed with the SA score, consistent with the strong correlation (Spearman p 0.88) between the two metrics in our dataset. RAscoree the other hand lacks of differentiation. The values are consistently very high (close to 1.0) across almost all models and targets and therefore show minimal variation. Its correlation with SA Score (Spearman = 0.68) and SCScore (Spearman = 0.69) was also weaker than the SA vs. SCscore correlation.
Furthermore, we report the numerical values of top- K docking, QED, SA, and LogP scores for all the methods across different target proteins in the Appendix A
4.2.4 Molecule Generation Quality
Model
Diversity
Uniqueness
Validity
SMILES -AS
0.90
0.60
1.00
L-SMM-CC
0.88
0.17
1.00
REINVENT
0.89
0.70
1.00
Pasithea
0.91
0.28
1.00
SMILES-VEL VAE
0.88
0.54
1.00
graph-GA
0.92
0.71
1.00
MIMOSA
0.88
0.09
1.00
MolDQN
0.93
0.71
1.00
DST
0.88
0.10
1.00
JT-VAE
0.89
0.77
1.00
Target
0.89
1.00
1.00
2ocket2Mol
0.84
1.00
1.00
PocketFlow
0.89
0.84
1.00
ResGen
0.85
0.97
1.00
3DSBDD
0.89
0.80
0.80
Table 6: average Molecule Generation Metrics across all target
We record the results Molecule Generation Oracles Table and below are the breakdown analysis.
In the diversity oracle, all models demonstrates strong capabilities with scores consistently above 0.84. MolDQN achieves the highest diversily 0.93, followed closely by graph-GA (0.92) and Pasithea (0.91). This high-performing cluster suggests that these models are particularly adept at generating structurally varied molecules. The remaining models maintain robust diversity scores between 0.84 and 0.90, indicating generally strong performance in exploring chemical space.
In the validity oracle, nearly all models shows exceptional performance with fourteen out of fifteen models achieving perfect scores of This indicates these modcls consistently generate chemically valid molecular structures. 3DSBDD stands as the sole exception with validity score of 0.80, suggesting some room for improvement. in ensuring chemical validity of its generated structures.
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Table 7: Model ranking summary
Model
clash
docking
generation
heuristic
posebusters
strain
overall rank
PocketFlow
5
graph-GA
3
12
3
3
2
SMILES-VAB
7
5
11
3
MolDQN
1
15
2
11
4
ResGen
2
14
9
5
Pasithea
8
7
12
6
REINVENT
4
JT-VAE
12
12
8
MIMOSA
9
13
DST
10
10
12
3
8
10
SMILE-LISTM-HC
13
2
10
2
11
11
Pocket2Mol
14
11
13
12
SMILES-GA
6
11
13
10
13
3DSBDD
11
14
TifgetDiff
15
13
12
15
15
Uniqueness reveal the most significant, variations among the three metries. Target and Pocket2Mol achieve perfect uniqueness scores of 1.00, while ResGen (0.97) and PocketFlow (0.84) also demonstrate strong performance. However, there is notable performance gap, with some models showing substantially lower uniqueness scores. MIMOSA (0.09), DST (0.10), and SMILESLLSTMHHC (0.17) generate relatively high proportions of duplicate structures. This variance in uniqueness scores suggests that while models can generate valid and diverse molecules, ensuring uniqueness remains a challenging aspect of molecular generation.
This comprehensive analysis indicates that while the field has largely solved the challenge of generating valid molecular structures and can consistently produce diverse molecule sets, the ability to generate unique molecules varies significantly across different approaches.
5
Discussion
5.1
Overall Ranking
While the primary goal of this benchmark is to highlight the trade-offs between different approaches, pro- viding summarized view can offer additional guidance. To this end, we calculated an overall ranking based the models' performance across the diverse metrics evaluated. We first ranked the models within each of the six major categories: clash count, docking score, generation quality (diversity, uniqueness, validity), heuristic properties (QED, SA, LogP), PoseBusters overall pass rates, and strain energy. These six cate- gory ranks were then averaged for each model, applying equal weight to each category, to determine the final overall rank presented in Table It is crucial, however, to interpret this ranking with cantion. The assumption of equal weighting across metric categories is subjective. Different drug discovery campaigns may prioritize certain aspects much more heavily. Therefore, while this table provides a useful summary, the detailed results across individual metrics remain essential for selecting the most appropriate model for specific scientific goal.
5.2 Metric Limitations
We acknowledge certain scope limitations in this benchmark. Due to computational and time constraints inherent in evaluating fifteen distinct generative models, we focused on AutoDock Vina as the primary docking oracle for cross-paradign comparison and utilized of diverse protein targets. Integrating alternative state-of-the-art docking affinity prediction methods, such as DiffDock (Corso et. al. 2023
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and AF3 (Abramson et al. 2024), or substantially expanding the target set remain important directions for future work but were infeasible for this initial comprehensive comparison.
5.3 Analysis and Future Approaches
Our benchmark experiments reveal a nuanced performance landscape across 1D, 2D, and 3D algorithmic families. This subsection provides a deeper analysis of these results, explaining the underlying reasons for the observed performance tradc-offs and offering insights into the strategic application of these different approaches drug discovery.
Intuitively, 3D models should have a decisive advantage, as they explicitly use the 3D coordinates of the target's binding pocket imput. This allows them to learn the geometric and chemical constraints imposed by the pocket's s shape, directly optimizing for steric and electrostatic complementarity. Our results confirm this, showing that 3D models consistently generate molecules with superior binding affinity. However, we also observe inconsistent chemical validity and poor pose quality. There are several possible factors that can explain this vafiiity-validiity trade-off":
Sequential Error Accumulation: Many 3D models are autoregressive, building molecules atom- by-atom. This sequential process can suffer from 'exposure bias," where small geometric prediction errors in early steps accumulate, resulting in final structures with high internal strain or steric clashes, even the overall shape fits the pocket.
Primacy of Coordinates over Chemistry: The intense focus on optimizing 3D coordinates can come at the expense of enforcing fundamental, discrete rules of chemistry. This can lead to geometrically plausible but chemically unfavorable arrangements, such as strained or uncommon ring structures. Some models successfully mitigate this by explicitly incorporating chemical knowledge, like valence rules, into the generation process to improve validity.
Practical Hurdles: 3D models often face practical constraints that can limit their use. They can be computationally intensive and may lack robustness, sometimes failing to generate molecules for novel protein targets without specific configurations an issue we observed in our experiments.
On the contrary, ID and 2D models have better performance on chemical validity may because they are using more chemical 'language" such as SMILES string or fragments and scaffolds. However these methods treat larget as a 'black-box oracle', generaling molecule first and only then checking its fit. This indirect optimization is inefficient for discovering high-affinity binders.
Given the complementary strengths and weaknesses of each approach, the most promising direction for future research lies the development of hybrid models. To test this hypothesis directly, we evaluated recent hybrid model, TamGen (Wu al.) 2024), which uses 3D protein context to guide 1D molecule generation. The results, summarized in Table & provide a crucial and nuanced perspective. Contrary to simple 'hybrid is best" narrative, binding affinity was lower than our representative 1D, 2D, and 3D) models. More significantly, it produced molecules with the highest median strain energy and the lowest chemical validity of the group. These results suggest that this particular hybrid implementation struggles with enforcing correct chemical geometries, a challenge we had primarily associated with pure 3D models.
found that TamGen did not resolve the core trade-offs observed in our benchmark. Its
This finding is highly instructive. It demonstrates that simply combining model modalities is not guaran- teed solution. The specific architectural choices for how 3D structural information is integrated with 1D/2D chemical grammars are critically important. This underscores the complexity of the SBDD challenge and refines our conclusion: the future lies not just in hybrid models, but in hybrid architectures that explicitly and robustly enforce chemical validity throughout the generation process.
6 Conclusion
Currently, the landscape of structure-based drug design models is vast, featuring various algorithmic back- bones, yet comparative analyses across them scarce. In this study, we design experiments to evaluate
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Table 8: case study comparing the hybrid model (TamGen) against representative models. The results are chosen from the best of each selected models achieved
Metric
1D Model (SMILES-LSTM-HC)
2D Model (JT-VAB)
3D Model (Pocket2Mol)
Hybrid Model
(TamGen)
Top-10 10 Docking Score (Affinity)
-12.20
-11.52
-14.78
-0066
Median Strain Energy (leal/mol)
41.95
49.70
60.55
76.26
Top-10 10 generation metries (Divensity/Uniien iatiiqueenss/Vaaiiitty
0.88/0.17/11
0.89/0.77/1.0
0.84/1.0/1.0
1.0/0.10/0.42
Top-10 heuristic (SA/L))P/QED)
1. 1.66/44.64/0.99
1.60/3.87/0.93
1.05/4.38/0.99
1.99/3.38/0..99
the quality of molecules generated by each model. Our experiments extend beyond conventional heuristic oracles related to molecular properties, also examining the affinity and poses between molccules and selected larget proteins. Our findings indicate that different algorithmic approaches exhibit distinet strengths and limitations. 3D models, particularly Pocket2Mol, demonstrate superior performance in generating molecules with strong binding affinities, evidenced by docking scores. However, they show more variability in chemn ical validity checks and pose evaluations. 1D SMILES-based models maintain consistent performance across most metrics but rarely achieve the highest binding affinities. 2D graph-based models offer the most bal- anced performance, showing strong consistency across chemical validity, pose quality, and moderate binding affinity scores.
These results suggest that while significant progress has been made in structure-based drug design, there remains room for improvement in developing models that can simultaneously optimize binding affinity while maintaining high chemical validity and pose quality. Future development of struclure-based models should focus on bridging these gaps, potentially by incorporating mechanisms that better balance the trade-offs between binding affinity optimization and other crucial molecular properties. This comprehensive evaluation provides valuable insights for researchers working to advance the field of computational drug discovery.
Acknowledgement. Tianfan Fu is supported by Young Scientists Fund (C Class) of the National Natural Science Foundation of China (Grant No. 62506154) and Nanjing University International Collaboration Initiative.
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A
Appendix
Table 9: Number of successfully generated and stored molecules by model. Targets CrossDocking are marked in red, and targets not CrossDocking are in blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7WC7
SJJL
7D42
6AZV
7S1S
SMILES-GA
832
800
819
892
580
791
652
662
629
558
SMILES-LSTM-HC
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
VENT
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
Pasithea
1000
800
900
1000
1000
800
900
800
1000
1000
SMILES-VAE
1000
1000
1000
600
1000
1000
1000
1000
1000
1000
graph GA
1000
1000
400
1000
1000
1000
500
1000
1000
1000
MIMOSA
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
MolDQN
700
690
700
800
700
700
700
700
700
696
DST
1000
900
1000
1000
1000
1000
900
1000
1000
1000
JT-VAE
1000
1000
1000
200
1000
1000
1000
1000
TifgetDiff
0
1
557
70
4
10
0
0
3DSBDD
898
708
669
668
769
1000
1000
1000
222
673
Pocket2mol
1000
968
1000
790
1000
1000
860
884
868
1000
PocketFlow
1000
1000
1000
1000
1000
1000
1000
1000
1000
1000
ResGen
0
676
290
785
0
259
0
805
1000
1000
20

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Published in Transactions on Machine Learning Research (02/2026)
smiles ga
00
Number of Clashes
000
25
20
0000
cocoo
000
10
7W7C
6GL8
8jIL
1UWH
SWPD
7D42
6AZV
11KQ
7515
70TE
smiles Istm hc
100
80
0.00
40
7W1C
6GLB
ejiL
1UWH
SWFD
7D42
6AZV
IKKQ
7515
70TE
reinvent
000 0.00
Number of Clashes
0000
20
00000000
000000
0000
00
10
7W7C
6GL8
8JIL
1UWH
5WFD
7D42
6AZV
1KKQ
7515
70TE
pasithea
Number of Clashes
40
30
20
000
10
7W7C
6GL8
BIJL
10WH
SWPD
7D42
6AZV
I1KQ
7515
70TE
smiles vae
40
0000000 00 0
Number of Clashes
30
10000 0.
20
10000000
000
1000
7W7C
6GL8
ejiL
1UWH
SWFD
7D42
6AZV
IKKO
7S15
70TE
Figure 5: The clashes box plot for 1D models
21

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Published in Transactions on Machine Learning Research (02/2026)
graph.ge ga
50
40-
Number
20
0000
10
6GL8
BIiL
1UWH
SWFD
7D42
GAZV
1KKQ
7515
70TE
mimosa
40
30
20
1000000
6GL8
BJLL
1UWH
5WFD
7D42
6AZV
1KKO
7515
7OTE
moldan
20
Number of Clashes
000
0000
5
6GL8
BIlL
1UWH
5WPD
7D42
6AZV
1KKQ
7515
70TE
dst
40
Number Clashes
30:
20
10000
0000
000
6GL8
BJJL
1UWH
SWPD
7D42
6AZV
IKKQ
7515
70TE
jte
60-
Number
20
6GL8
BJLL
1UWH
SWFD
7D42
6AZY
IKKO
7515
70TE
Figure 6: The clashes box plot for 2D models
22

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Published in Transactions on Machine Learning Research (02/2026)
3DSBDD
40
Number of Clashes
30
20
10
7515
70TE
1kKQ
1UWH
6GL8
6AZV
7D42
Pocket2Mol
100
Clashes
80
40
20
6GL8
1KKQ
8j/L
6AZV
70TE
7$15
10WH
5WFD
7D42
7W7C
PocketFlow
Number of Clashes
40
100000
7W7C
8ILL
6GL8
6AZV
5WFD
7D42
IUWH
70TE
7515
1KKQ
ResGen
Number of Clashess
1.5
00000
000
000.0.
10
0.0
000
000
6AZV
7W7C
1KKQ
70TE
7042
1UWH
7515
TargetDiff
150
100
500
Number
50
1UWH
7D42
5WFD
BIlL
7W7C
1KKQ
Figure 7: The clashes box plot for 3D models
23

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Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
IKKQ
5WFD
20l
-10.53 0.40
-1.88 0.34
-1.778 + 0.44
-13.53 0.30
-11.06 + 0.15
Pocket
-8.98 + 0.22
-10.68 + 0.23
-9.55 0.36
-1167 + 0.38
-9.31 = 0.35
ResGen
-9.23 + 0.20
-7.03 = 0.06
-9.66 + 0.18
3DSBDD
-8.19 + 0.22
-12.41 = 0.18
-10.41 0.09
-12.71 0.30
-10.72 = 0.22
DST
-8.23 +: 0.15
-10.48 1 0.16
-10.76 + 0.21
-9.96 1: 0.32
-9.22 1 0.28
graph-GA
-7.94 + 0.18
-10112 1 0.37
-9.94 + 0.36
-9.63 1 0.32
-9.03 0.18
JT-VAR
+ + 0.42
-11.12 0.41
-11.27 + 0.34
-10.90 0.47
-10.26 + 0.42
MIMOSA
-8.28 0.13
-10.22 +
-10.82 0.25
-10.16 + 0.25
-9.35 0.19
MolDQN
-6.13 +113+0011 0.17
-6.69 + 0.25
-6.93 + 0.20
-6.75 + 0.21
-7.37 0.14
Pasithea
-8.73 + 0.24
-1.12 =
-10.84 = 0.26
-10.13
-9.88 0.33
REINVENT
-8,65 + 0.24
10.72 = 0.28
-10.76 + 0.44
10.51 + 0.28
-9.56 0.24
SMILES-CE GA
-8.44 1 0.23
10.33 1 0.27
-10.27 0.39
-9.81 0.38
-9.20 0.30
SMILES-LSTM-R
-9.26 1 0.29
-1.02 1 0.25
-11.54 1 0.40
-11.21 1 0.51
-10.41 1 0.42
SMILES VAE
-8.76 + 0.25
-11.13 + 0.37
-11.35 = 0.61
-10.31 + 0.31
-9.74 = 0.16
Tifget
-11.14 + 0.26
-8.29 0.69
(a) Targets CrossDocking.
Model
7W7C
8JJL
7D42
7S1S
6AZV
2ol
-13.56 0.11
-12.79
0.33
-12.10 0.33
-12.49
0.26
-11.87 + 0.26
FlcketFlow
-13.02 + 0.23
-11.42 + 0.28
-12.25 + 0.30
-9.46 + 0.34
-11.49 0.15
ResGen
-7.56 + 0.18
-8.60 = 0.20
-11.63 0.08
-9.47 0.13
3DSBDD
-10.78 + 0.28
10.90 + 0.35
-10.43 2 0.29
9.84
-9.66 0.32
DST
-11.11 + 0.37
-00.79 + 0.48
-1.92 0.32
-1..73 1 0.30
-9.78 = 0.16
graph-GA
-10.60 0.39
-10.23 1 0.19
-10443 - 0.20
-9.98 + 0.16
-9.14 0.21
JT-VAE
-10.81 + 0.54
-10.81 + 0.39
-11.52 0.41
-11.01 0.16
-10.18 + 0.14
MIMOSA
-11.14 + 0.21
-10.77 0.36
-11.03
-10.78 0.33
-9.78 0.18
MolDQN
-7.33 + 0.16
-7.90 + 0.35
-6.53 = 0.26
-8.01 0.16
-6.73 0.39
Pasithea
-11.41 0.26
-11.24 0.32
-11.15 - 0.26
-1.06 + 0.18
-10.03 0.12
REINVENT
-11.52 1 0.19
-11.13 1 0.25
-11.40 1 0.34
-10.99 0.20
-9.92 1 0.30
SMILES-CE GA
-10.97 0.29
-1101 1
-11.01 0.43
-10.46 1 0.35
-9.41 0.40
SMIT.ES-ISTM-HC
-12.20 + 0.60
-11.09 + 0.18
-11.47 + 0.46
-11.47 + 0.24
-10.38 + 0.13
SMILES-VAR
-11.42 + 0.33
-11.14 + 0.42
-11.42 0.27
-11.14 0.34
-10.11 + 0.19
TifgetDiff
-7.99 + 0.53
(b) Targets not in CrossDocking.
Table 10: Top 10 Vina score for each target.
24

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Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
Pocket2Mol
-9.90 0.31
-12.11 0.48
-13.14 + 0.78
-12.36 + 0.52
-10.45 + 0.29
Fook
-7.92 = 0.52
-8.12 = 1.43
-7.74 + 0.85
-10.33 + 0.60
-8.14 + 0.56
ResGen
-8.06 0.56
-6.30 + 0.48
-8.82 + 0.35
3DSBDD
-7.22 = 0.44
-11.52 0.44
-9.67 = 0.38
-1..59 + 0.51
-9.86 + 0.40
DST
-7.50 0.28
-9.41
-9.47 = 0.49
-9.00 0.40
-8.41 0.31
graph-GA
-6.81 = 0.40
-8.32 = 0.68
-8.25 + 0.65
-8.07 1 0.59
-7.65 1 0.49
JT-VAE
-7.93 0.48
-9.82 =
-9.65 + 0.59
-9.62 0.53
-9.00
MIMOSA
-7.49 = 0.27
-9.54 0.38
-9.49 + 0.48
-8.99 + 0.40
-8.46 + 0.32
MolDQN
-484 0.47
-5.46 = 0.47
-5.87 + 0.40
-5.65 + 0.43
-5.79 0.56
Pasithea
-7.93 = 0.39
-9.94 0.51
-9.98 + 0.43
-9.36 + 0.36
-8.92 + 0.44
REINVENT
-7.82 = 0.36
-9.70 = 0.43
-9.77 = 0.48
-9.40 + 0.51
-8.80 0.36
SMILES-GA GA
-7.37 0.46
-8.94 0.66
-8.95 = 0.64
-8.64 + 0.50
-8112 0.44
SMILES-LSTM-HIC
-8.29 = 0.42
-10.18 1 0.11
-10.40 + 0.50
-10.10 + 0.49
-9.43 0.43
SMILES-VAL VAE
-7.97 0.37
-9.97 0.50
-10.09 0.56
-9.08 1 0.52
-8.84 0.40
Tiffet
-9.84 + 0.58
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
Pocket2Mol
-12.55 + 0.49
-11.63 = 0.51
-10.88 + 0.56
-11.51 + 0.46
-11.11 + 0.36
PocketFlow
-11.94 = 0.47
-10.35 0.46
-11.11 0.48
-7.82 + 0.74
-10.56 + 0.40
ResGen
-7.04 = 0.24
-7.71 + 0.44
-11.06 0.31
-8.75 + 0.32
3DSBDD
-8.67 = 1.14
-9.58 0.57
-9.10
-8.95 = 0.42
-8.28 0.64
DST
-10.06 = 0.41
9.74
-9.92 = 0.41
-9.78 + 0.37
-8.90 + 0.30
graph-GA
-8.89 = 0.64
-8.52 0.61
-8.82 + 0.57
-8.46 0.52
-7.88 0.48
JT-VAE
-8.63 = 0.79
-9.78 0.43
-10330 + 0.48
-1.007 + 0.35
-9.02 + 0.42
MIMOSA
10.08 = 0.40
0.45
-9.88 = 0.45
-9.77 + 0.41
-8.85 + 0.34
MolDQN
.6.12 0.43
-5.41 = 0.42
-6.38 + 0.53
-..78 + 0.38
Pasithea
-0.53 1 0.42
-10.22 = 0.45
-0.31 0.40
-1.233 1 0.42
-9.30 1 0.35
REINVENT
-10.66 + 0.48
-10.26 0.41
-10.32 0.48
-1.08 0.41
-9.19 1 0.34
SMILES-GA
-9.78 = 0.55
-9.61 + 0.62
-9.39 + 0.66
-9.30 + 0.50
-8.50 0.43
SMIT.FS-LSTM-HCC
-11.09 + 0.50
-10.16 0.43
-10.62
0.43
-10551
0.44
-9.52 0.37
0.37
SMILES-VAE
-10.44 + 0.42
-10.17 = 0.45
-10.34 0.51
-10.22 - 0.44
-9.32 + 0.36
TifgetDiff
b): targets not in CrossDocking
Table 11: Top 100 Average docking score for each target
25

---

Published in Transactions on Machine Learning Research (02/2026)
Table 12: Top LogP score for each target. Targets in CrossDocking are marked in red, and targets not CrossDocking are in blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
SJJL
7D42
7S1S
6AZV
SMILES-GA
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
3.59
SMI-FF--ISTM-HCC
6.88
5.60
6.88
6.88
6.88
6.88
6.88
6.88
6.88
6.88
REINVENT
3.55
3.85
3.85
3.73
3.39
3.39
3.39
3.39
4.04
3.39
Pasithea
3.35
3.48
3.24
3.38
3.24
3.24
3.24
3.24
3.24
3.24
SMILES-VAL VAE
3.55
3.82
3.87
3.84
3.86
3.55
3.73
3.56
3.63
4.20
graph-GA
3.59
3.59
3.66
3.96
3.78
3.59
3.81
3.59
3.59
MIMOSA
4.15
4.15
4.15
4.15
4.15
4.15
4.15
4.15
4.15
4.15
MolDQN
0.49
1.00
1.07
1.07
0.56
1.00
1.00
0.29
1.00
1.45
DST
3.44
3.31
3.44
3.44
3.44
3.44
3.31
3.44
3.44
3.44
JT-VAE
3.93
3.91
3.91
4.37
4.70
3.75
4.10
3.75
3.75
4.36
TifgetDiff
-0.67
4.43
1.30
3.08
0.15
-0.28
Mocket2Mol
5.49
3.72
5.23
6.31
4.22
3.30
4.27
4.04
3.27
5.04
Pocket.loww
4.92
3.94
3.86
6.76
4.89
6.19
4.87
5.80
3.04
6.85
ResGen
3.29
2.18
3.75
2.90
2.90
2.79
2.90
3DSBDD
2.41
3.51
0.72
3.30
1.23
2.01
6.93
3.79
1.14
2.67
26

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
2.58 + 0.44
2.55 + 0.42
2.53 + 0.44
2.43 + 0.51
2.57 + 0.43
SMILES-LSTM-HCC
1.64 1.03
4.52 0.41
1.03
4.61 1.03
4.61 +
REINVENT
3.14 = 0.25
3.22 0.27
3.13 = 0.33
3.12 0.29
3.02 = 0.23
Pasithea
3.08 0.12
3.04 = 0.188
3.06 = 0.09
3.06 0.16
3.06 = 0.09
SMILES- -VE
3.33 0.09
3.36 0.188
3.33 = 0.26
3.34 0.20
3.47 = 0.19
graph-GA
2.78 1 0.33
3.04 = 0.32
3.00 1 0.34
3.29 1 0.51
0.51
3.20 T 0.32
MIMOSA
3.34 + 0.28
3.34 0.28
3.34 + 0.28
3.34 0.28
3.34 0.28
MolDQN
0.27 + 0.20
-0.13 + 0.49
0.27 + 0.49
0.50 +
0.02 - 0.32
DST
3.14 + 0.14
3.09 0.11
3.14 + 0.14
0.14
3.14 0.14
JT-VAE
3.45 + 0.28
3.54 0.24
3.51 = 0.20
3.71 + 0.29
3.87 = 0.46
TafgetDiff
-0.67 = 0.00
3.79 0.36
-0.11 0.87
Pocket2Mol
5.00 + 0.21
3.33 0.18
4.93 + 0.18
6.00 + 0.15
3.88 + 0.21
PocketFlow
4.35 0.27
2.29 0.73
0.58
0.40
3.78 T 0.49
ResGen
2.83 0.18
1.78 1 0.22
3.58 1 0.08
3DSBDD
1.98 + 0.23
2.72 0.47
-0.59 = 0.49
2.93 0.24
0.33 - 0.56
a): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA
2.55 1 0.50
2.84 +: 0.28
2.59 + 0.43
2.45 + 0.49
2.54 1: 0.45
SMILES-LSTM-HC
4.64 1.03
4.64 1.03
4.64 + 1.03
4.64 = 1.03
4.64 + 1.03
REINVENT
3.07 = 0.22
3.13 0.18
3.02 + 0.23
3.38 = 0.37
3.07 + 0.21
Pasithea
3.06 0.09
3.06 = 0.09
3.06 + 0.09
3.06 = 0.09
3.06 + 0.09
SMILES-VAL
3.31 0.10
3.38 0.15
3.35 = 0.13
3.37 0.11
3.32 + 0.30
graph-GA
3.07 = 0.39
2.92 = 0.34
3.00 + 0.40
3.03 = 0.27
2.80 + 0.29
MIMOSA
3.31 0.28
3.31 0.28
3.34 + 0.28
3.34 0.28
3.34 + 0.28
MolDQN
0.15 = 0.38
-0.09 = 0.54
-0.23 + 0.34
0.04 = 0.36
0.06 + 0.65
DST
3.14 0.14
3.09 0.11
3.14 = 0.14
3.14 0.14
3.14 + 0.14
JT-VAE
2.94 = 0.36
3.47 0.34
3.38 0.16
3.37 = 0.18
3.58 + 0.41
Target Diff
1.35
-7.95 20.45
-0.28 0.00
Pocket2Mol
2.78 + 0.29
3.98 = 0.13
3.54 0.27
2.74 + 0.23
4.38 1 0.32
PooketFlow
5.50 0.36
3.84 0.51
5.48 + 0.21
1.15 - 0.79
6.40 + 0.26
ResGen
2.59 0.16
2.48 + 0.19
2.36 - 0.15
2.58 + 0.20
3DSBDD
0.91 0.60
6.19 0.35
2.94 - 0.43
-0.19 = 0.64
1.71 + 0.41
b): targets not in Cross
Table 13: Top 10 LogP score for each target
27

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
1.25 + 0.62
1.36 0.57
1.24 + 0.63
0.93 + 0.75
1.32 + 0.63
SMILES-LSTM-HC
3.39 + 0.60
3.38 = 0.55
3.39 + 0.60
3.39 + 0.60
3.39 + 0.60
REINVENT
2.21 + 0.47
2.11 0.58
2.12 - 0.51
2.11 + 0.53
2.08 + 0.49
Pasithea
2.44 = 0.35
2.40 = 0.31
2.50 = 0.30
2.42 = 0.32
2.50 = 0.30
SMILES- VAE
2.56 0.40
2.55 0.38
2.57 0.34
2.45 = 0.44
2.58
graph-GA
1.61 + 0.61
1.81 + 0.58
1.64 1 0.64
1.89 1 0.65
1.99 T 0.57
MIMOSA
2.55
2.55 0.38
0.38
2.55 + 0.38
2.55 1
MolDQN
-1.41 + 0.72
-1.67 + 0.66
-1.51 + 0.75
-1.17 + 0.76
-1.62 + 0.71
DST
2.60 + 0.28
2.57 0.27
2.60 + 0.28
2.60 + 0.28
2.60 + 0.28
JT-VAE
2.25 + 0.54
2.54 + 0.49
2.52 + 0.47
2.47 + 0.56
2.46 + 0.63
TifgetDiff
-0.67 = 0.00
2.14 0.77
-255 + 1.65
Pocket2Mol
3.72 = 0.68
2.59 0.39
3.97 = 0.44
4.78 = 0.66
3.02 = 0.41
PocketFlow
3.02 + 0.65
0.57 = 0.75
1.85 1 0.54
4.40 + 0.74
1.86 1 0.88
ResGen
2.19 0.32
0.77 1 0.52
2.92 1 0.32
3DSBDD
-0.19 + 1.18
1.01 0.75
2.16 = 0.76
0.56 + 1.15
-2.66 + 1.25
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA GA
0.91 = 0.79
1.54 + 0.60
1.03 + 0.75
1.23 + 0.65
1.28 + 0.61
SMILES-ISTM-HC
3.39 = 0.60
3.39 + 0.60
3.39 0.60
3.39 0.60
3.39 + 0.60
REINVENT
2.08 = 0.53
2.16 + 0.53
2.08 + 0.50
2.21 + 0.57
2.15 + 0.50
Pasithea
2.50 = 0.30
2.50 = 0.30
2.50 0.30
0.30
2.50 = 0.30
SMILES-VAL VAE
2.54 = 0.35
2.55 = 0.40
2.57 = 0.38
2.53 - 0.39
2.47 = 0.39
graph-GA
1.81 = 0.62
1.58 + 0.66
1.83 0.55
+ 0.64
1.63 + 0.62
MIMOSA
2.55 = 0.38
2.55 + 0.38
2.55 + 0.38
2.55 + 0.38
2.55 + 0.38
MolDQN
-1.14 = 0.68
-1.68 0.70
-1.83 + 0.68
-1.49 = 0.69
-1.63 0.75
DST
2.60 0.28
2.57 = 0.27
2.60 0.28
2.60 = 0.28
2.60 = 0.28
JT-VAE
1.13 = 0.92
2.28 1 0.59
2.50 1 0.50
2.28 1 0.53
2.35 1 0.56
Targe(Difl
1.01 1.35
-7.95 + 20.45
-0.28 + 0.00
Mol
1.62 = 0.58
3.43 + 0.27
2.05 + 0.70
1.35 + 0.67
3.24 + 0.56
PocketFlow
4.30 0.52
2.52 + 0.64
4.51 0.46
-0.05 0.64
5.24
+ 0.57
ResGen
1.63 = 0.49
1.67 + 0.39
1.62 + 0.39
1.79
+ 0.36
3DSBDD
-1.36 11.32
3.73 + 1.18
0.04 1.80
-3.82 1.76
-0.32
1.20
b): targets not in CrossDocking
Table 14: Top 100 LogP score for each target
28

---

Published in Transactions on Machine Learning Research (02/2026)
Table 15: Top QED score for each target. Targets in CrossDocking are marked in red, and targets not in
CrossDocking are blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
SJJL
7D42
7S1S
6AZV
SMILES-GA
0.94
0.93
0.94
0.94
0.94
0.92
0.92
0.94
0.92
0.92
SMI-FE--ISTM-HIC
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
REINVENT
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
Pasithea
0.95
0.95
0.94
0.94
0.94
0.94
0.94
0.94
0.94
SMILES-VAL VAE
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
graph-GA
0.93
0.94
0.93
0.94
0.93
0.94
0.92
0.92
0.94
0.93
MIMOSA
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
0.95
MolDQN
0.65
0.75
0.58
0.63
0.59
0.55
0.66
0.53
0.72
0.55
DST
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
0.94
JT-VAE
0.94
0.94
0.94
0,94
0.94
0.94
0.94
0.94
0.94
0.94
TifgetDiff
0.06
0.86
0.72
0.28
0.51
0.38
Mocket2Mol
0.95
0.94
0.94
0.94
0.93
0.94
0.93
0.89
0.94
0.94
Pocket.loww
0.91
0.79
0.88
0.93
0.88
0.92
0.90
0.94
0.78
0.90
ResGen
0.78
0.65
0.93
0.70
0.89
0.93
0.89
3DSBDD
0.92
0.92
0.88
0.86
0.85
0.91
0.87
0.93
0.91
0.92
29

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
0.92 + 0.01
0.91 + 0.01
0.92 + 0.01
0.93 + 0.01
0.92 + 0.01
SMILES-LSTM-HHC
0.93 + 0.01
0.93 0.01
0.93 + 0.01
0.93 = 0.01
0.93 - 0.01
REINVENT
0.94 = 0.01
0.93 0.01
0.01
0.93 = 0.01
0.94 = 0.01
0.94 = 0.01
Pasithea
0.94 0.01
0.94
0.94 0.00
0.93 = 0.00
0.94 = 0.00
SMILES-VAL VAE
0.94 = 0.00
0.94 = 0.00
0.94 = 0.00
0.94 = 0.01
0.94 = 0.00
graph-GA
0.92 1 0.01
0.91 1 0.01
0.92 1 0.01
0.91 + 0.01
0.91 1 0.01
MIMOSA
0.94 + 0.00
0.94 0.00
0.94 + 0.00
0.94 = 0.00
0.94 + 0.00
MolDQN
0.60 + 0.03
0.67 + 0.03
0.54 + 0.02
0.60 = 0.02
0.53 - 0.04
DST
0.94 0.00
0.94 0.00
0.94 = 0.00
0.94 = 0.00
0.94 + 0.00
JT-VAE
0.93 = 0.01
0.93 + 0.01
0.93 = 0.01
0.92 = 0.01
0.93 = 0.01
Tiff
0.06 0.00
0.82 = 0.02
0.56
2oc
0.93 + 0.01
0.92 + 0.01
0.93 + 0.01
0.91 = 0.02
0.91 + 0.01
PocketFlow
0.90 0.01
0.74 0.03
0.02
0.90 1 0.02
0.85 1 0.02
ResGen
0.74 1 0.02
0.63 1 0.01
0.91 + 0.01
3DSBDD
0.90 - 0.01
0.88 0.02
0.82 - 0.03
0.79 = 0.03
0.79 = 0.03
a): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA
0.91 + 0.01
0.91 1 0.01
0.92 1: 0.01
0.91 + 0.01
0.91 + 0.01
SMILES-LSTM-HC
0.93 + 0.01
0.93 0.01
0.93 = 0.01
0.93 = 0.01
0.93 = 0.01
REINVENT
0.94 + 0.01
0.93 - 0.01
0.94 - 0.01
0.93 = 0.01
0.93 = 0.01
Pasithea
0.94 + 0.00
0.94 0.00
0.94 = 0.00
0.94 = 0.00
0.94 = 0.00
SMILES-VAL VAE
0.94 = 0.00
0.94 0.00
0.94 = 0.00
0.94 = 0.00
0.94 = 0.00
graph-GA
0.92 + 0.01
0.90 + 0.01
0.91 + 0.01
0.91 = 0.01
0.91 - 0.01
MIMOSA
0.94 + 0.00
0.94 0.00
0.94 0.00
0.94 0.00
0.94 - 0.00
MolDQN
0.52 + 0.01
0.56 + 0.04
0.50 = 0.02
0.67 = 0.03
0.52 - 0.01
DST
0.94 0.00
0.94 0.00 0.00
0.94 0.00
0.94 = 0.00
0.94 0.00
JT-VAE
0.91 + 0.01
0.93 + 0.01
0.92 = 0.01
0.93 = 0.01
0.93 = 0.01
TargeUDiff
0.17
0.23 1 0.18
0.00
2ocket2Mol
0.93 + 0.00
0.92 1 0.01
0.87 + 0.01
0.91 = 0.01
0.93 + 0.01
PocketFlow
0.89 + 0.02
0.88 0.01
0.87 0.03
0.72 + 0.03
0.88 + 0.01
ResGen
0.67 + 0.01
0.88 + 0.01
0.91 = 0.01
0.84 + 0.02
3DSBDD
0.78 = 0.06
0.79 0.04
0.90 - 0.02
0.85 = 0.04
0.90 = 0.02
b): targets not in Cross
Table 16: Top 10 QED score for each target
30

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
0.87 + 0.03
0.87
0.02
0.87 0.03
0.86 + 0.03
0.86 + 0.03
SMILES-LLTM--HC
0.88 + 0.03
0.88 + 0.03
0.88 + 0.03
0.88 = 0.03
0.88 - 0.03
REINVENT
0.89 + 0.02
0.89 0.02
0.89 = 0.02
0.89 = 0.02
0.90 = 0.02
Pasithea
0.90 = 0.02
0.90 0.02
0.90 = 0.02
0.90 = 0.02
0.90 = 0.02
SMILES VAE
0.91 0.01
0.91 0.01
0.91 0.02
0.89 = 0.02
0.91 0.02
graph-GA
0.84 1 0.04
0.84 1 0.04
0.84 1 0.04
0.84 1 0.04
0.84 + 0.04
MIMOSA
0.91
0.02
1 0.02
0.91 - 0.02
0.91
MolDQN
0.45 + 0.06
0.49 + 0.08
0.44 - 0.05
0.47 = 0.06
0.44 = 0.05
DST
0.91 + 0.02
0.90 0.02
0.91 + 0.02
0.91 = 0.02
0.91 + 0.02
JT-VAF
0.87 + 0.03
0.88 + 0.03
0.88 + 0.02
0.88 = 0.02
0.87 + 0.03
TiffetDiff
0.06 = 0.00
0.66 = 0.08
0.24 = 0.17
Pocket2Mol
0.86 = 0.04
0.81 0.05
0.84 = 0.05
0.75 = 0.09
0.83 = 0.05
PocketFlow
0.78 1 0.06
0.62 + 0.06
0.74 + 0.05
0.79 = 0.06
0.73 + 0.06
ResGen
0.68 0.03
0.58 1 0.03
0.87 1 0.02
3DSBDD
0.78 + 0.06
0.76 + 0.07
0.73 - 0.05
0.64 = 0.09
0.62 - 0.08
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA
0.86 + 0.03
0.86 + 0.03
0.86 + 0.03
0.86 = 0.03
0.86 + 0.03
SMILES-LSTM-HC
0.88 + 0.03
0.88 0.03
0.88 0.03
0.88 = 0.03
0.88 = 0.03
REINVENT
0.89 + 0.02
0.89 + 0.02
0.89 = 0.02
0.89 = 0.02
0.89 = 0.02
Pasithea
0.90
+ 0.02
0.02
0.90 = 0.02
0.90 =
SMILES VAE
0.91 + 0.02
0.91 + 0.02
0.91 = 0.02
0.91 = 0.02
0.91 = 0.02
graph-GA
0.84 + 0.04
0.83 0.0M
+ 0.04
0.81 = 0.04
0.83 + 0.04
MIMOSA
0.91 + 0.02
0.91 + 0.02
0.91 + 0.02
0.91 = 0.02
0.91 + 0.02
MolDQN
0.44 + 0.04
0.45 = 0.05
0.43 = 0.03
0.49 = 0.08
0.43 = 0.04
DST
0.91 = 0.02
0.90 + 0.02
0.91 = 0.02
0.91 = 0.02
0.91 = 0.02
JT-VAE
0.82 + 0.05
0.88 1 0.03
0.87 1 0.02
0.88 + 0.03
0.88 1 0.03
TafgeuDiff
0.17 + 0.09
0.23 1 0.18
0.18
0.38 + 0.00
Pocket2Mol
0.89 + 0.03
0.87 0.03
0.81 + 0.03
0.83 + 0.04
0.84 + 0.05
Pooket
0.80 0.05
0.79 0.05
0.75 0.06
0.60 + 0.06
0.78 + 0.05
ResGen
0.62 + 0.03
0.80 + 0.04
0.87 = 0.02
0.78 + 0.04
0.04
3DSBDD
0.49 0.16
0.62 0.07
0.75 0.08
0.52 =
0.73 0.09
b): targets not in CrossDocking
Table 17: Top 100 QED score for each target
31

---

Published in Transactions on Machine Learning Research (02/2026)
Table 18: Top 1 SA score for each target. Targets in CrossDocking are marked in red, and targets not in
CrossDocking are blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
SJJL
7D42
7S1S
6AZV
SMILES-GA
1.65
1.65
1.65
1.65
1.65
1.65
1.65
1.65
1.65
1.65
SMI-FF--ISTM-HCC
1.51
1.50
1.51
1.51
1.51
1.51
1.51
1.51
1.51
1.51
REINVENT
1.58
1.58
1.58
1.58
1.58
1.58
1.27
1.58
1.58
1.58
Pasithea
1.63
1.52
1.50
1.50
1.50
1.50
1.50
SMILES-VAL VAE
1.44
1.50
1.50
1.50
1.50
1.50
1.44
1.50
1.50
1.50
graph-GA
1.00
1.00
1.00
1.00
1.00
1.00
1.00
MIMOSA
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
MolDQN
1.51
1.75
1.65
1.51
1.85
1.62
1.62
1.85
1.51
1.61
DST
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
JT-VAE
1.50
1.38
1.50
1.38
1.25
1.50
1.50
1.11
1.50
1.41
TifgetDiff
4.05
1.76
2.96
3.86
3.68
3.41
Mocket2Mol
1.00
1.03
1.00
1.03
1.00
1.09
1.18
1.63
1.32
1.00
Pocket.llww
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.61
1.00
ResGen
1.00
1.18
1.00
1.00
1.00
1.16
1.00
3DSBDD
1.00
1.20
2.03
1.84
1.00
3.07
1.21
1.55
1.88
1.00
32

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
1.84 + 0.10
1.87
0.12
1.79 0.08
1.87 + 0.12
1.85 + 0.11
SMILES--STM-HHC
1.67 + 0.07
1.66 + 0.08
1.67 + 0.07
1.67 = 0.07
1.67 - 0.07
REINVENT
1.72 0.07
1.73 0.07
1.74 + 0.08
1.71 = 0.08
1.74 = 0.08
Pasithea
+
1.10
1.66 = 0.09
1.67 = 0.07
1.66 = 0.09
SMILES- VAE
1.64
1.67 +.07
1.67 0.08
1.64 = 0.10
1.64 = 0.08
graph-GA
0.25
1.07 0.09
1.35 1 0.24
1.12 1 0.13
1.08 1 0.05
MIMOSA
1.70
1.70 0.08
1.70 0.08
1.70 = 0.08
1.70 1
MolDQN
1.84 + 0.14
2.09 0.19
1.90 - 0.18
1.83 = 0.14
2.02 + 0.15
DST
1.66 + 0.09
1.66 0.09
1.66 0.09
1.66 - 0.09
1.66 + 0.09
JT-VAF
1.81 + 0.12
1.71 0.15
1.67 + 0.09
1.65 + 0.13
1.64 + 0.17
Tiffet
4.05 0.00
2.41 = 0.27
3.54 = 0.28
Pocket2Mol
1.20 + 0.13
1.50 = 0.22
1.10 = 0.08
1.45 = 0.20
0.20
1.05 = 0.06
PocketFlow
1.00 + 0.00
1.37 + 0.26
1.00 1 0.00
1.07 + 0.06
1.11 + 0.10
ResGen
1.00 0.00
1.40 1 0.12
1.05 1 0.06
3DSBDD
1.44 + 0.25
1.47 + 0.16
2.92 - 0.41
2.90 = 0.39
2.02 + 0.59
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA
1.89 + 0.14
1.85 0.11
1.87 + 0.11
1.86 = 0.11
1.85 + 0.09
SMILES-LSTM-HC
1.67 + 0.07
1.67 0.07
1.67 0.07
1.67 = 0.07
1.67 = 0.07
REINVENT
1.74 + 0.08
1.67 0.15
1.74 + 0.08
1.73 = 0.07
1.73 - 0.08
Pasithea
1.66 0.09
0.09
1.66 = 0.09
1.66 =
SMILES VAE
1.01
1.64
1.65 = 0.07
1.67 = 0.09
1.70 = 0.08
graph-GA
1.12 + 0.14
1.30 0.21
0.21
1.14 0.18
1.15 = 0.16
1.31 + 0.25
MIMOSA
1.70 + 0.08
1.70 + 0.08
1.70 + 0.08
1.70 = 0.08
1.70 + 0.08
MolDQN
1.85 0.16
2.01 0.27
2.08 = 0.20
1.92 = 0.18
1.96 = 0.20
DST
1.66 + 0.09
0.09
1.66 + 0.09
1.66 = 0.09
1.66 = 0.09
1.66 = 0.09
JT-VAE
1.88 0.13
1.83 1 0.14
1.70 1 0.24
1.78 + 0.16
1.60 1 0.10
TafgetDiff
4.59 + 0.67
5.23 0.86
3.41 + 0.00
Pocket2Mol
1.29 0.13
1.36 0.12
2.10 + 0.22
1.78 + 0.29
1.54 + 0.22
Pooket
1.10 0.08
1.16 0.09
1.00 0.00
1.77 + 0.08
1.00 + 0.00
ResGen
1.15 + 0.11
1.14 + 0.10
1.43 + 0.11
1.05 + 0.06
3DSBDD
4.40 0.57
2.76 0.76
0.27
2.65 = 0.48
1.80 0.41
b): targets not in CrossDocking
Table 19: Top 10 SA score for each target
33

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
SMILES-GA
2.55 + 0.35
2.50
0.33
2.53
0.37
2.77 + 0.49
2.52 + 0.37
SMILES--STM--HC
1.93 + 0.13
1.95 + 0.14
1.93 + 0.13
1.93 + 0.13
1.93 + 0.13
REINVENT
2.14 0.20
2.11
2.14 0.19
2.15 = 0.19
2.14 0.18
Pasithea
2.01 0.14
1.97 = 0.15
=
1.96 = 0.15
SMILES- VAE
1.96 0.14
0.13
0.14
2.02 0.17
1.96 = 0.15
graph-GA
2.10 1 0.36
1.39
2.15 0.37
1.94 0.35
1.96 1 0.43
MIMOSA
1.95 1 0.12
1 0.12
0.12
1.95 = 0.12
1.95 1
MolDQN
2.93 + 0.57
3.21 + 0.51
2.98 - 0.50
2.83 = 0.53
3.15 + 0.53
DST
1.95 + 0.14
1.96 0.14
1.95 - 0.14
1.95 + 0.14
1.95 + 0.14
JT-VAF
2.29 + 0.25
2.19 + 0.22
2.21 + 0.24
2.21 + 0.25
2.23 + 0.27
Tiffet
4.05 - 0.00
3.42 = 0.48
4.50 - 0.63
Pocket2Mol
1.69 + 0.25
2.07 0.29
1.92 = 0.37
2.05 = 0.29
1.43 = 0.18
PocketFlow
1.36 + 0.22
1.98 + 0.30
1.30
+ 0.23
1.60 - 0.29
1.86 + 0.34
ResGen
1.29 0.17
1.87 1 0.25
1.49 1 0.21
3DSBDD
3.00 + 0.74
2.89 + 0.73
4.39
- 0.65
4.20 = 0.61
4.40 - 1.03
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
SMILES-GA
2.77 + 0.48
2.48 + 0.32
2.66 + 0.43
2.53 + 0.35
2.55 - 0.38
SMILES-LSTM-HC
1.93 + 0.13
1.93
0.13
1.93
0.13
1.93 = 0.13
1.93 0.13
REINVENT
2.16 + 0.20
2.14 0.23
2.16 - 0.19
2.11 = 0.18
2.13 + 0.19
Pasithea
1.97 0.15
1.97 0.15
1.97 0.15
1.97 0.15
1.97 =
SMILES VAE
1.95 + 0.14
1.95 - 0.14
1.97
1.96 - 0.13
graph-GA
1.97 + 0.37
0.37
2.15 0.38
+ 0.37
2.01 + 0.38
2.13 + 0.35
MIMOSA
1.95 + 0.12
1.95 + 0.12
1.95 - 0.12
1.95 - 0.12
1.95 + 0.12
MolDQN
3.05 0.53
3.24 0.55
0.55
3.28 = 0.53
3.05 = 0.53
3.14 0.51
DST
1.95 0.14
1.96 + 0.14
1.95 = 0.14
1.95 = 0.14
1.95 = 0.14
JT-VAE
2.64 1 0.43
0.43
2.30 1 0.22
2.23 1 0.25
2.30 + 0.26
2.18 1 0.26
TafgetDiff
4.59 + 0.67
5.23 0.86
3.41 + 0.00
Pocket2Mol
2.05 + 0.35
2.19 + 0.39
2.72 + 0.28
2.57 + 0.39
2.14 + 0.29
Pooket
1.71 0.30
1.62 0.26
1.17 0.21
2.02 + 0.10
1.06 + 0.09
ResGen
1.76 + 0.35
1.63 + 0.24
1.86 + 0.21
1.47 + 0.21
3DSBDD
6.10 0.96
4.89 0.88
3.73 0.90
4.87 1.05
3.57 0.83
b): targets not in CrossDocking
Table 20: Top 100 SA score for each target
34

---

Published in Transactions on Machine Learning Research (02/2026)
Table 21: Top 1 SC score for each target.
Targets in CrossDocking are marked in red, and targets not in
CrossDocking are in blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
8JJL
7D42
7S1S
6AZV
Mol
1.27
1.57
1.00
1.18
1.16
1.26
1.38
1.23
1.26
1.30
PooketFlow
1.02
1.01
1.02
1.00
1.02
1.02
1.02
1.02
1.05
1.02
ResGen
1.00
1.00
1.19
1.15
1.13
1.13
1.12
3DSBDD
1.19
2.31
1.30
2.84
1.24
1.51
1.20
dst
1.56
1.56
1.56
1.56
1.56
1.56
1.56
1.56
1.56
1.56
graph ga
1.00
1.00
1.23
1.00
1.00
1.02
1.00
1.00
1.00
ji jae
1.00
1.00
1.08
1.00
1.00
1.74
1.00
1.08
1.01
1.00
mimosa
1.36
1.36
1.36
1.36
1.36
1.36
1.36
1.36
1.36
1.36
moldqn
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
pasithea
1.00
1.71
1.09
1.00
1.00
1.56
1.09
1.56
1.00
1.00
reinvent
1.22
1.00
1.26
1.00
1.26
1.00
1.15
1.19
1.01
1.00
smiles ga
1.74
1.98
1.12
1.28
1.55
1.28
1.28
1.28
1.81
1.28
smiles 1stm hc
1.20
1.08
1.20
1.20
1.20
1.20
1.20
1.20
1.20
1.20
smiles__aa vae
1.00
1.00
1.34
1.96
1.00
1.34
1.00
1.26
1.22
1.35
targetdiff
3.30
2.63
2.62
3.45
2.11
2.86
35

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
10WH
70TE
1KRQ
5WFD
Pocket2Mol
1.48 + 0.10
2.11 + 0.27
+.40 0.21
1.90 + 0.47
1.23 + 0.03
Pocket.Flow
1.02 0.00
1.02 + 0.01
1.02 0.00
1.14 = 0.08
1.09 0.06
ResGen
1.09 - 0.07
1.09
1.27 0.05
SBDD3D
1.58 0.32
1.69 = 0.37
0.37
0.40
2.48 = 0..5
1.59 = 0.27
dst
1.82 + 0.16
1.86 - 0.16
1.82 0.16
1.82 0.16
1.82 0.16
graph__s
1.01 1: 0.02
1.01 1 0.01
1.33 0.07
0.07
1.05 1 0.05
1.00 1 0.01
ja_
1.10 1 0.07
1.14 1 0.11
1.19 1 0.08
1.11 0.07
1.14 = 0.08
mimosa
1.59 + 0.15
1.59 + 0.15
1.59 0.15
1.59 + 0.15
1.59 + 0.15
moldqn
1.02 0.03
1.01 0.01
1.01 + 0.02
1.00 0.00
1.00 0.01
pasithea
1.05 = 0.07
1.96 + 0.13
1.23 0.10
1.15 = 0.11
1.09 = 0.06
reinvent
0.13
0.17
1.45
1.35 0.15
0.09
stmiles
1.98 + 0.09
2.02 + 0.04
1.67 + 0.37
1.95 + 0.26
1.95 1 0.17
smiles Istm
0.16
1.57 1 0.20
1.51 0.16
0.16
0.16
stilles
1.50 1 0.29
1.53 1 0.21
1.59 0.11
2.08 0.07
1.46 1 0.18
targetdiff
3.30 + 0.00
2.93 0.16
3.04
0.22
a.: targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
Pocket2Mol
1.36 1: 0.12
1.71 1: 0.19
2.05 1 0.58
2.41 1: 0.52
1.71 1: 0.20
PocketFlow
1.19 - 0.10
1.11 + 0.05
1.16 - 0.07
1.05 0.00
1.09 0.06
ResGen
1.31 + 0.08
1.22 0.05
1.39 + 0.11
1.19 = 0.04
SBDD3D
3.11 0.14
2.24 + 0.60
1 0.21
2.39 = 0.69
1.41 = 0.23
dst
1.82 + 0.16
1.86 0.16
1.82
1.82 0.16
1.82 = 0.16
graph_ ga
1.04 + 0.05
1.27 + 0.11
1.01 1.01
1.02 + 0.02
1.13 + 0.08
jt_
2.26 + 0.24
1.14 + 0.11
1.15 0.06
1.17 0.10
1.07 0.05
mimosa
1.59 + 0.15
1.59 + 0.15
1.58 0.13
0.13
1.59 = 0.15
1.59 = 0.15
moldqn
1.00 0.01
1.02 - 0.08
1.02 0.03
1.00 0.01
0.02
pasithea
1.87 + 0.17
1.23 0.10
1.87 + 0.17
1.15 = 0.09
1.16 = 0.09
reinvent
0.17
1.41 0.09
1.39 0.18
1.36 0.16
smiles
1.62 + 0.29
1.87 1 0.26
1.61 + 0.32
2.04 - 0.09
1.89 = 0.26
smiles lstm
1.51 0.16
1.51 + 0.16
1.51 + 0.16
1.51 0.16
1.51 0.16
smiles
1.56 + 0.13
1.48 + 0.24
1.57 + 0.15
1.60 + 0.16
1.65 - 0.14
targetdiff
4.09 = 0.45
3.65 + 0.68
2.86 - 0.00
b): targets not in Cross
Table 22: Top 10 SC score for each target
36

---

Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
Mocket2Mol
2.17 + 0.40
3.13 + 0.50
2.76 0.69
3.05 0.50
1.61 0.21
PocketFlow
1.10 + 0.09
1.25 + 0.10
1.15
1.30 = 0.10
1.27 + 0.08
ResGen
1.21 + 0.07
1.35
1.68 0.22
SBDD3D
2.60 + 0.46
3.44 + 0.80
3.69
3.44 = 0.43
3.08 = 0.74
dst
2.39 0.26
2.43 - 0.26
2.39 0.26
2.39 0.26
2.39 0.26
graph ga
1.28 1 0.13
1.25 0.13
2.07 + 0.39
1.32 1 0.133
1.26 1 0.12
vae
0.37
1.72 1
1.77
1.74 0.35
0.34
mimosa
2.12 + 0.23
2.12 + 0.23
2.12 0.23
2.12 + 0.23
2.12 = 0.23
moldqn
1.24 0.10
1.22 + 0.11
1.22 0.11 0.11
1.18 0.09
1.22 - 0.11
pasithea
1.67 + 0.33
2.46 + 0.25
1.85 + 0.30
1.64 + 0.27
1.70 + 0.31
reinvent
1.91 + 0.24
1.92 + 0.23
1.91 0.22
1.84 = 0.23
1.87 = 0.24
smiles ga
2.36 = 0.21
2.44 + 0.22
2.27 = 0.29
2.46 0.25
2.38 0.23
smiless_ lstm he
2.03 1 0.25
2.04 1 0.21
2.03 1 0.25
2.03 1 0.25
2.03 1 0.25
smiles
2.10 1 0.25
2.06 1 0.23
2.06 1 0.22
2.57 0.23
2.01 1 0.24
targetdiff
3.30 + 0.00
3.47 = 0.25
3.89 = 0.55
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
Pocket2Mol
2.11 + 0.43
2.89 + 0.55
2.92 + 0.37
3.45 + 0.45
2.87 + 0.53
PocketFlow
1.30 0.07
1.27 + 0.09
1.27 + 0.14
1.14 = 0.03
1.20 0.05
ResGen
1.77 + 0.27
1.49 + 0.15
1.93 = 0.24
1.40 = 0.13
SBDD3D
0.53
3.32 + 0.44
2.70 0.50
3.80 0.63
0.75
dst
2.39 + 0.26
2.43 + 0.26
2.39 + 0.26
0.26
2.39 = 0.26
2.39 = 0.26
graph ga
1.29 0.12
1.88 + 0.32
1.30 0.14
1.30 0.13
1.33 0.11
jt_
3.13 + 0.40
1.73 + 0.34
1.72 + 0.33
1.72 + 0.32
1.68 = 0.34
mimosa
2.12 = 0.23
2.12 + 0.23
2.11 0.23
0.23
2.12 = 0.23
2.12 = 0.28
moldgn
1.20 + 0.10
1.26 + 0.12
1.26 + 0.12
1.19 0.10
1.22 = 0.11
pasithea
2.44 1: 0.26
1.85 1 0.30
2.44 1 0.26
0.26
1.70 + 0.28
1.70 1 0.28
reinvent
1.84 +: 0.26
1.97 1 0.23
1.86 + 0.22
1.89 0.23
1.86 0.24
smiles__a
2.31 + 0.30
2.36 + 0.26
2.21 + 0.27
2.45 + 0.20
2.37 + 0.24
smiles lstm he
2.03 0.25
2.03 + 0.25
2.03 + 0.25
2.03 0.25
2.03 0.25
smiles_ vae
2.06 + 0.23
2.08 + 0.26
2.09 + 0.23
2.08 + 0.21
2.10 + 0.21
targetdiff
4.09 0.45
3.65 + 0.68
2.86
b): targets not in Cross
Table 23: Top 100 SC score for each target
37

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Published in Transactions on Machine Learning Research (02/2026)
Table 24: Top 1 RA score for each target. Targets in CrossDocking are marked in red, and targets not
CrossDocking are blue.
Model
6GL8
1UWH
70TE
IKKQ
5WFD
7W7C
8JJL
7D42
7S1S
6AZV
Mol
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
PooketFlow
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
ResGen
1.00
1.00
1.00
1.00
1.00
1.00
1.00
3DSBDD
1.00
0.99
0.98
0.99
0.98
0.98
0.99
0.99
1.00
dst
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
graph ga
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
ji jae
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
mimosa
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
moldqn
0.99
1.00
1.00
0.99
0.99
0.99
0.99
0.99
0.99
0.99
pasithea
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
reinvent
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
smiles ga
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
smiles 1stm hc
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
smiles__aa vae
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
targetdiff
0.04
1.00
0.99
0.37
0.88
0.73
38

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Published in Transactions on Machine Learning Research (02/2026)
Model
6GT8
10WH
70TE
1KKQ
5WPD)
Pocket2Mol
0.9983 + 0.00
0.9982 + 0.00
0.9980 + 0.00
0.9988 + 0.00
0.9985 + 0.00
PocketFlow
0.9984 + 0.00
0.9961
0.00
0.9983
0.00
0.9987 + 0.00
0.9972 + 0.00
ResGen
0.9980
0.00
0.9960 = 0.00
0.9970 = 0.00
3DSBDD
0.9966 = 0.00
0.9980
0.00
0.9601 = 0.01
0.9478 = 0.03
0.9828 = 0.00
dst
0.9993 = 0.00
0.9993
0.00
0.9993 = 0.00
0.9993 = 0.00
0.9993 = 0.00
graph_
0.9994 1 0.00
0.9994 1: 0.00
0.9986 1 0.00
0.9994 + 0.00
0.9994 T 0.00
jt_vae
0.9994 + 0.00
0.9994 0.00
0.9994 1 0.00
0.9994 + 0.00
0.9995 + 0.00
mimosa
0.9995 + 0.00
0.9995 + 0.00
0.9995 + 0.00
0.9995 + 0.00
0.9995 + 0.00
moldqn
0.9936 + 0.00
0.9942 0.00
0.9935
0.00
0.9932 + 0.00
0.9921 0.00
pasithea
0.9995 + 0.00
0.9994 + 0.00
0.9994 = 0.00
0.9995 = 0.00
0.9995 = 0.00
reinvent
0.9995 0.00
0.9995
0.9995
0.00
0.9995 = 0.00
0.9995
smiles ga
0.9993 + 0.00
0.9993 + 0.00
0.9995 +: 0.00
0.9993 + 0.00
0.9994 + 0.00
smiles Istan he
0.9996 1: 0.00
0.9996 0.00
0.9996
0.00
0.9996 1 0.00
0.9996
smiles vae
0.9994 + 0.00
0.9996 1 0.00
0.9995 1 0.00
0.9992 1 0.00
0.9995 + 0.00
targetdiff
0.0415 0.00
0.9902 - 0.00
0.8588 = 0.10
a):: targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
Pocket2Mol
0.9982 1: 0.00
0.9965 1 0.00
0.9962 1: 0.00
0.9971 1: 0.00
0.9982 1 0.00
PocketFlow
0.9986 + 0.00
0.9975 0.00
0.9973
0.00
0.9957 = 0.00
0.9985 = 0.00
ResGen
0.9963 + 0.00
0.9970 + 0.00
0.9964 + 0.00
0.9977 = 0.00
SBDD3D
0.5402 + 0.28
0.9697 = 0.01
0.9881 + 0.00
0.9678 + 0.02
0.9918 + 0.00
dst
0.9993 + 0.00
0.9993 0.00
0.9993 = 0.00
0.9993 = 0.00
0.9993 = 0.00
graph
0.9994 + 0.00
0.9987 + 0.00
0.9994 + 0.00
0.9991 + 0.00
0.9994 + 0.00
jt_
0.9990 + 0.00
0.9994 0.00
0.9994 + 0.00
0.9994 + 0.00
0.9995 0.00
mimosa
0.9995 + 0.00
0.9995 + 0.00
0.9995 = 0.00
0.9995 = 0.00
0.9995 = 0.00
moldqn
0.9929 0.00
0.9934 0.00
0.9927 0.00
0.9936 = 0.00
0.9928
0.00
pasithea
0.9993 + 0.00
0.9994 + 0.00
0.9993 + 0.00
0.9995 = 0.00
0.9995 = 0.00
reinvent
0.9995 0.00
0.9995 0.00
0.9995
0.00
0.9995 +: 0.00
0.9995
smiles ga
0.9993 + 0.00
0.9994 + 0.00
0.9993 1 0.00
0.9995 + 0.00
0.9993 + 0.00
smiles 1stm he
0.9996 + 0.00
0.9996 0.00
0.9996
0.00
0.9996
+ 0.00
0.9996 + 0.00
smiles smiles__ae
0.9995 + 0.00
0.9995 + 0.00
0.9996 + 0.00
0.9996 + 0.00
0.9994 + 0.00
targetdiff
0.1287 = 0.16
0.4119 0.33
0.7323 + 0.00
b): targets not in Cross
Table 25: Top 10 RA score for each target
39

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Published in Transactions on Machine Learning Research (02/2026)
Model
6GL8
1UWH
70TE
1KKQ
5WFD
Mocket2Mol
0.9939 + 0.00
0.9929
0.00
0.9910
0.01
0.9888 + 0.01
0.9967 + 0.00
PocketFlow
0.9961 + 0.00
0.9948 + 0.00
0.9963 + 0.00
0.9963 + 0.00
0.9949 + 0.00
ResGen
0.9964
0.00
0.9920 + 0.00
0.9949 + 0.00
3DSBDD
0.9885 = 0.01
0.9663 = 0.08
0.5018 = 0.33
0.3784 = 0.30
0.7572 = 0.20
dst
0.9984 0.00
0.9984 0.00
0.9984
0.00
0.9984 = 0.00
0.9984 0.00
graph
0.9984 T 0.00
0.9984 T 0.00
0.9957 1 0.00
0.9984 + 0.00
0.9984 T 0.00
jte vae
0.9985 1 0.00
0.9987 0.00
0.9987
0.00
0.9987 + 0.00
0.9987
mimosa
0.9991 + 0.00
0.9991 + 0.00
0.9991 + 0.00
0.9991 + 0.00
0.9991 = 0.00
moldqn
0.9862 + 0.01
0.9866 0.01
0.9853 + 0.01
0.9862 + 0.01
0.9845 + 0.01
pasithea
0.9990 + 0.00
0.9983 + 0.00
0.9987 + 0.00
0.9991 + 0.00
0.9990 + 0.00
reinvent
0.9990 + 0.00
0.9989 = 0.00
0.9990 = 0.00
0.9990 = 0.00
0.9989 = 0.00
smiles ga
0.9986 = 0.00
0.9983 0.00
0.9985 = 0.00
0.9985 = 0.00
0.9984 = 0.00
smiles Ista he
0.9991 1 0.00
0.9990 0.00
0.9991
1 0.00
0.9991 + 0.00
0.9991 + 0.00
smiles vae
0.9990 + 0.00
0.9991 0.00
0.9991 1 0.00
0.9982 1 0.00
0.9990 0.00
targetdiff
0.0415 + 0.00
0.8899 + 0.08
0.3424 = 0.29
a.): targets in CrossDocking
Model
7W7C
8JJL
7D42
7S1S
6AZV
Pocket2Mol
0.9946 + 0.00
0.9873 + 0.01
0.9753 + 0.01
0.9755 + 0.02
0.9839 + 0.01
PocketFlow
0.9961 + 0.00
0.9954
0.00
0.9946
0.00
0.9934 + 0.00
0.9966
0.00
ResGen
0.9889 + 0.01
0.9942 + 0.00
0.9922 + 0.00
0.9960 + 0.00
3DSBDD
0.1442 = 0.17
0.2492 0.31
0.8580
0.14
0.3142 = 0.32
0.9110
dst
0.9984 = 0.00
0.9984 + 0.00
0.9984 = 0.00
0.9984 = 0.00
0.9984 = 0.00
graph
0.9985 + 0.00
0.9917 0.00
0.9984 0.00
0.9981 + 0.00
0.9985 = 0.00
vae
0.9897 + 0.01
0.9986 # 0.00
0.9986 + 0.00
0.9987 + 0.00
0.9988 + 0.00
mimosa
0.9991 = 0.00
0.9991 = 0.00
0.9991 = 0.00
0.9991 + 0.00
0.9991 = 0.00
moldqn
0.9846 + 0.01
0.9859 0.01
0.9842 = 0.01
0.9860 = 0.01
0.9861 = 0.01
pasithea
0.9982 1 0.00
0.9987 1: 0.00
0.9982 1 0.00
0.9990 1: 0.00
0.9990 1 0.00
reinvent
0.9990 = 0.00
0.9990 0.00
0.9990 1: 0.00
0.9990 + 0.00
0.9990 + 0.00
smiles ga
0.9986 + 0.00
0.9987 + 0.00
0.9984 + 0.00
0.9984 + 0.00
0.9985 + 0.00
smiles 1stm he
0.9991 + 0.00
0.9991 0.00
0.9991
0.00
0.9991 + 0.00
0.9991 + 0.00
smiles__va vae
0.9991 + 0.00
0.9991 + 0.00
0.9991 + 0.00
0.9991 + 0.00
0.9991 + 0.00
targetdiff
0.1287 0.16
0.4119 0.33
0.7323 0.00
b): targets not in CrossDocking
Table 26: Top 100 R.A score for each target
40

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Published in Transactions on Machine Learning Research (02/2026)
Table 27: Posebusters evaluation results across models. The values represent the success rate of generated molecules for all receptor targets, indicating the proportion of molecules that meet the Posebusters validation criteria
All Atoms Connected
Aromatic Ring
Bond Angles
Bond Lengths
Double Bond Flatuess
Inchi Convertible
Model
Flainess
Pocket2Mol
0.763
0.990
0.989
0.988
0.970
0.359
PooketFlow
0.936
0.991
0.990
0.989
0.991
0.880
ResGen
0.947
0.988
0.989
0.989
0.987
0.913
3DSBDD
0.459
0.957
0.943
0.944
0.922
0.400
DST
1.000
1.000
1.000
0.999
1.000
1.000
graph-GA
1.000
1.000
1.000
0.999
1.000
1.000
JT-VAE
1.000
1.000
1.000
0.998
1.000
1.000
MIMOSA
1.000
1.000
1.000
0.999
1.000
1.000
MolDQN
1.000
1.000
0.968
0.950
0.998
1.000
Pasithea
0.956
0.767
0.766
0.765
0.767
0.738
REINVENT
0.915
0.803
0.802
0.802
0,803
0.733
SMILES-GA
0.954
0.720
0.719
0.719
0.719
0.699
SMIL.ES-SSTMHHC
0.930
0.902
0.900
0.899
0.902
0.839
SMILES- VAE
0.959
0.776
0.776
0.775
0.776
0.745
TargetDiff
1.000
1.000
0.758
0.903
1.000
0.998
Internal Energy
Internal Steric Clash
Min Dist To Inorg Cofactors
Min Dist To Org Cofactors
Min Dist. To Protein
Min Dist
Model
Waters
Pocket2Mol
0.471
0.968
1.000
1.000
0.935
1.000
PocketFlow
0.724
0,980
1,000
1.000
0.998
1.000
ResGen
0.711
0.983
1.000
1.000
1.000
1.000
3DSBDD
0.323
0.909
1.000
1.000
0.867
1.000
DST
0.972
1.000
1.000
1.000
0.996
1.000
graph-GA
0.970
0.999
1.000
1.000
0.998
1.000
JT-VAE
0.958
0.999
1.000
1.000
0.991
1.000
MIMOSA
0.968
L.000
1.000
1.000
0.997
1.000
MolDQN
0.947
0.990
1,000
1.000
1.000
1.000
Pasithea
0.724
0.763
1.000
1.000
0.996
1.000
REINVENT
0.717
0.800
1.000
1.000
0.998
1.000
SMILES-GA
0.678
0.714
1.000
1.000
0.998
1.000
HUILES-LSTM-HH
0.815
0.898
1.000
1.000
0.986
1.000
SMILES- VAE
0.735
0.775
1.000
1.000
0.997
1.000
Target Diff
0.746
0.933
1.000
1.000
0.470
1.000
Mol Cond Loaded
Mol Pred Loaded
Prot-Lig Max Dist
Vol Overlap
Vol Overlap
Vol Overlap
Model
Sanitization
w/ Inorg Cof
w/ Org Cof
w/ Protein
2ocket2Mol
1.000
1.000
1.000
0.992
1.000
1.000
1.000
PocketFlow
1,000
1.000
1.000
0.991
1.000
1.000
1,000
ResGen
1,000
1.000
1.000
0.989
1.000
1.000
1,000
3DSBDD
1.000
1.000
1.000
0.957
1.000
1.000
1.000
DST
1.000
1.000
1.000
1.000
1.000
1.000
1.000
graph-GA
1.000
1.000
1.000
1.000
1.000
1.000
1.000
JT-VAE
1.000
1.000
1.000
1.000
1.000
1.000
1.000
MIMOSA
1,000
1.000
1.000
1.000
L.000
1.000
1,000
MolDQN
1.000
L.000
1.000
1.000
L.000
1.000
0.996
Pasithea
1,000
1.000
1.000
0.767
L.000
1.000
1,000
REINVENT
1.000
1.000
1.000
0.803
1.000
1.000
1.000
SMILES-GA
1.000
1.000
1.000
0.720
1.000
1.000
1.000
OMILES-LSTM-HC LSTM-HC
1.000
1.000
1.000
0.902
1.000
1.000
1.000
SMILES. VAE
1.000
1.000
1.000
0.776
1.000
1.000
1.000
Diff
1.000
1.000
1.000
0.998
1.000
1.000
0.903
41

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Published in Transactions on Machine Learning Research (02/2026)
UQ
5
5
3
4
5
5
5
61
UQ
3
6
7
6
11
9
6
Mean
3.44
4.00
3.46
3.03
2.24
3.41
3.32
3.57
4.04
3.42
4.30
3.74
48.75
Mean
4.87
5.37
4.83
2.33
2.73
4.62
4.68
4.79
5.22
4.82
8.10
6.56
4.33
4.09
Table 28: Clash statistics across different models. An empty item (-) means the model either did not generate molecules for that target or the evaluation failed. UQ stands for Upper Quartile.
5WFD
6AZV
Max
Max
11
16
12
13
11
11
14
10
16
10
15
18
93
19
21
18
12
19
22
21
17
20
19
44
21
20
20
Min
0
19
Min
UQ
6
6
3
4
6
5
5
7
5
7
10
5
7
UQ
7
9
7
5
7
7
7
8
7
10
6
6
Mean
4.14
4.96
4.09
2.39
2.59
4.17
4.02
4.12
5.02
4.01
5.79
7.73
3.11
5.34
30.43
Mean
5.49
6.78
5.46
3.46
3.35
5.26
5.52
5.25
6.42
5.27
7.20
7.48
4.82
4.70
1KKQ
7S18
Max
17
16
13
20
18
16
27
17
15
27
17
16
115
Max
21
25
25
15
16
20
27
18
28
17
20
34
23
16
Min
Min
UQ
7
12
11
UQ
3
11
6
7
33
11
51
Mean
5.29
6.44
5.62
3.14
4.88
5.51
4.42
4.89
6.48
5.41
8.85
3.28
9,26
1.61
Mean
4.86
7.15
4.68
2.26
2.91
5.01
4.01
4.33
8.81
4.06
5.19
23 82
8.09
3.41
51 51.00
7OTE
7D42
Max
Max
19
30
22
20
20
29
22
28
34
19
28
66
38
13
33
30
36
22
88
26
12
84
64
14
51
Min
Min
0
0
0
0
0
51
UQ
12
88
UQ
13
18
13
13
14
13
13
26
13
130
Mean
5.54
6.28
5.35
2.78
3.34
5.58
4.42
5.15
6.38
5.45
5.06
9.09
2.73
2.32
88.0 00
Mean
10. 26
13.87
10.33
4.03
9.04
10.20 28
7.81
9.82
15.51
10.06
20. .2
8.64
112.90
1UWH
8JJL
Max
23
60
21
13
25
21
38
15
Max
39
74
20
51
52
31
40
103
48
176
Min
0
0
0
0
0
0
0
0
0
88
Min
0
0
0
0
0
0
0
0
0
0
61
UQ
UQ
170
4
5
4
5
6
2
Mean
3.14
3.56
3.15
2.74
2.34
3.02
3.09
3.34
3.56
2.93
3.31
4.14
2.72
Mean
4.13
5.69
4.23
1.77
2.57
4.14
4.18
4.46
6.99
4.14
8.67
5.75
1.64
160.00
6GL8
7W7C
Max
14
14
11
13
15
12
13
13
14
11
15
14
Max
25
47
27
9
29
33
32
25
69
30
64
26
6
173
Min
Min
140
EMILES- AMILES-LLTYEE LSTMM-HC HC
SMIL.SS-T SEL.FS--SSMAT LSTM-HC
SMILES- VAE
SMILES VAE
Model
JT-VAE
MIMOSA
MolDQN
grиph-GA
Pasithea
REINVENT
SMILES-GA
3DSBDD
Pocket 2Mol
PocketFlows
ResGen
TargetDiff
Model
J'T-VAE
MIMOSA
MolDQN
graph-GA
Pasithea
REINVENT
SMILES-GA
3DSBDD
Pocket2Mol
PocketFlov
ResGen
TargetDDiff
DST
DST
42


Reviews:
[{"review_id": "nQObVlMugY", "reviewer": "Reviewer_JKMZ", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "No", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "The paper presents a large-scale benchmark comparing fifteen molecule design models across 1D (SMILES-based), 2D (graph-based), and 3D (structure-based) paradigms, within the context of structure-based drug design. The study evaluates these methods using multiple oracles, including docking scores (AutoDock Vina), pose quality (PoseBuster, PoseCheck), and molecular heuristics (QED, SA, LogP, diversity, validity, uniqueness). The study reports systematic trade-offs: 3D models (e.g., Pocket2Mol, ResGen) achieve stronger docking affinities but lower chemical validity, while 1D and 2D models yield more chemically realistic but less potent binders. The authors conclude that hybrid models combining ligand-centric and structure-centric approaches could best balance these properties.\n\nKey strengths:\n* Provides a broad, cross-algorithmic comparison spanning multiple representation paradigms.\n* Uses consistent evaluation procedures and covers a relatively large set of models and targets.\n* Offers interpretable, qualitative discussion of observed trade-offs between affinity and validity.\n\nKey weaknesses:\n* Limited novelty: the benchmark largely replicates existing efforts (e.g., GuacaMol, PMO, CBGBench) without introducing new datasets, tasks, or metrics.\n* The TargetDiff model fails to generate molecules on most targets, yet no explanation or debugging is provided, raising concerns about fairness.\n* Evaluation metrics (SA Score, Vina Score) are outdated and weakly correlated with true synthesis feasibility or binding affinity; modern ML-based or physics-based alternatives are ignored.\n* No statistical significance analysis, correlation studies, or reproducibility artifacts are provided.", "claims_and_evidence_yn": "No", "claims_explanation": "While the benchmark is executed systematically, several critical methodological and analytical issues limit the credibility of the reported findings:\n\n1. TargetDiff generation failure unexplained.\n\nThe paper reports that TargetDiff — a major 3D baseline — successfully produced fewer than 100 molecules for most targets (Table 5), often zero, even on CrossDock targets. This is inconsistent with both the original TargetDiff paper and independent benchmarks such as CBGBench [1], where TargetDiff successfully generated ≈100 molecules per pocket with reasonable docking results.\nThe authors do not provide any technical explanation. The observed failure likely stems from pipeline incompatibility rather than algorithmic weakness. This omission undermines the fairness of the 3D model comparison.\n\n2. Benchmark redundancy and lack of novelty.\n\nThe paper largely reuses metrics already standardized in prior works such as GuacaMol [2], PMO [3], PoseCheck [4], and CBGBench [1]. For the CBGBench, which already benchmarked 3DSBDD, Pocket2Mol, ResGen, and TargetDiff under unified 3D conditions, the authors neither acknowledge this prior work nor clarify how their benchmark extends it — e.g., no new dataset, task type, or evaluation dimension is introduced. As such, the contribution is incremental rather than conceptually new.\n\n3. Outdated evaluation metrics.\n\n* SA Score [5] is a heuristic based on fragment frequency and molecular complexity; it poorly correlates with realistic synthesis feasibility. More modern alternatives exist, such as SCScore [6], RAscore [7].\n* Vina score is used as the sole proxy for binding affinity, yet it is known to correlate weakly (R² ≈ 0.2–0.3) with experimental ΔG. The paper performs no correlation or validation against experimental affinities or alternative scoring methods. Modern ML predictors such as DiffDock[8], AlphaFold3[9] derived self-consistency / confidence may present a better correlation with experimental ΔG\n\n4. Lack of statistical and qualitative analysis.\n\nReported metrics are given as raw means or medians without statistical significance testing or variance analysis. Moreover, there is no qualitative inspection (e.g., failed poses, invalid geometries) to support the “affinity–validity trade-off” claim.\n\n5. No reproducibility or release plan.\n\nThe authors mention using external code and receptors but provide no standardized evaluation scripts, parameter settings, or public benchmark suite. This weakens the claim of establishing a reusable benchmark.\n\n------\nReferences:\n1. Lin, H., Zhao, G., Zhang, O., Huang, Y., Wu, L., Tan, C., ... & Li, S. Z. CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph. In The Thirteenth International Conference on Learning Representations.\n2. Brown, N., Fiscato, M., Segler, M. H., & Vaucher, A. C. (2019). GuacaMol: benchmarking models for de novo molecular design. Journal of chemical information and modeling, 59(3), 1096-1108.\n3. Gao, W., Fu, T., Sun, J., & Coley, C. (2022). Sample efficiency matters: a benchmark for practical molecular optimization. Advances in neural information processing systems, 35, 21342-21357.\n4. Harris, C., Didi, K., Jamasb, A. R., Joshi, C. K., Mathis, S. V., Lio, P., & Blundell, T. L. (2023). Posecheck: Generative models for 3d structure-based drug design produce unrealistic poses.\n5. Ertl, P., & Schuffenhauer, A. (2009). Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. Journal of cheminformatics, 1(1), 8.\n6. Coley, C. W., Green, W. H., & Jensen, K. F. (2018). Machine learning in computer-aided synthesis planning. Accounts of chemical research, 51(5), 1281-1289.\n7. Thakkar, A., Chadimová, V., Bjerrum, E. J., Engkvist, O., & Reymond, J. L. (2021). Retrosynthetic accessibility score (RAscore)–rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chemical science, 12(9), 3339-3349.\n8. Corso, G., Stärk, H., Jing, B., Barzilay, R., & Jaakkola, T. S. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking. In The Eleventh International Conference on Learning Representations.\n9. Abramson, J., Adler, J., Dunger, J., Evans, R., Green, T., Pritzel, A., ... & Jumper, J. M. (2024). Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature, 630(8016), 493-500.", "audience_yn": "Yes", "audience_explanation": "Readers focused on AI-driven molecular generation and cross-modal benchmarks may appreciate the systematic comparison across algorithmic paradigms", "requested_changes": "* Provide a technical diagnosis to explain TargetDiff failure. \n* Upgrade evaluation metrics. Include more modern synthesis-feasibility predictors and consensus affinity predictors.\n* Add statistical analysis and visualization.\n* Clarify relation to prior benchmarks. Explicitly compare to CBGBench and other existing benchmarks, delineating what is truly new or improved in this work.\n* Release all receptor files, model scripts, and oracle evaluation code or provide a clear plan for doing so.", "broader_impact_concerns": "N/A", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "ledi5zaoYv", "reviewer": "Reviewer_6nHw", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper presents a benchmark study of 1D, 2D, and 3D representations-based models on the structure-based drug design (SBDD) task. A lot of machine learning papers for SBDD neglected 1D and 2D methods as baselines. This study fills in the gap. This work evaluated multiple representative models from each categories on docking scores of 10 PDB structures, conformational validity (PoseCheck, PoseBuster), and some other heuristics (QED, SA, diversity, etc).\n\nThis benchmarking study shows that: 1) 3D-based models can achieve better docking scores but struggles with chemical validity; 2) 1D-based models preserves chemical validity but struggle to generate molecules with good docking scores; 3) 2D-based models reach a balance between docking scores and validity. Therefore, this paper suggests future works should explore how to better combine 2D and 3D modeling.\n\nStrengths:\n\n- This work is a comprehensive benchmarking studies of generative models for SBDD. Its categorization is somewhat complete (1D, 2D, and 3D) and representative methods from each category are evaluated.\n- In Discussion section, the paper provides insights into the performance difference of different classes of models.\n\nWeaknesses:\n\n- Although this paper suggests a future direction of combining 2D and 3D representations, there have been recent works that combine 2D and 3D representations, for example TamGen [1], which represents protein in 3D and generates molecules in 1D. This paper didn't cover these recent advances.\n\n[1] TamGen: drug design with target-aware molecule generation through a chemical language model", "claims_and_evidence_yn": "Yes", "claims_explanation": "The claims about the performance of 1D, 2D, and 3D based models in SBDD are overall well supported by the evaluations conducted in this study.", "audience_yn": "Yes", "audience_explanation": "Computational biology has been a significant field of application in the machine learning community.", "requested_changes": "- Include more recent methods, especially those that combine 2D and 3D representations (e.g. TamGen). This will increase the uniqueness and timeliness of this study.\n- In 3D evaluation, in addition to docking scores and strain energy, is it possible to evaluate how similar are the docked poses to the generated poses? This is also an important sanity check metric: if the generated poses deviate too much from the docked poses, it would be unconvincing that the 3D model is actually aware of the 3D interactions.", "broader_impact_concerns": "N/A for this paper.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "FHAUWVeD2P", "reviewer": "Reviewer_8RPW", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This paper contributes to the field of Structure-Based Drug Design (SBDD) by establishing the first comprehensive, cross-algorithmic benchmark. Its primary contributions are:\n\n1. Comprehensive Benchmarking: It moves beyond comparing models within the same category (e.g., 3D vs. 3D) and instead evaluates fifteen distinct models across the three dominant algorithmic families: search-based algorithms, deep generative models, and reinforcement learning, using 1D, 2D, and 3D molecular representations.\n\n2. Holistic Evaluation Framework: The benchmark assesses models on a much wider range of criteria than previous works. It goes beyond standard molecular properties (e.g., QED, SA) to include critical, real-world metrics for drug discovery:\n1)Binding Affinity: Measured by docking scores (AutoDock Vina).\n2)Pose Quality: Assessed using specialized tools (PoseBuster, PoseCheck) to evaluate the physical and chemical plausibility of the generated protein-ligand complexes (e.g., strain energy, steric clashes).\n3)Generation Quality: Includes diversity, validity, and uniqueness of the generated molecules.\n\n\nKey Strengths:\n\n1. First cross-algorithm comparison: It breaks the tradition of comparing models only within the same paradigm and presents the first systematic evaluation of 1D, 2D and 3D methods for structure-based drug design.\n\n2. Comprehensive assessment: Beyond docking scores, it incorporates pose-quality metrics (strain energy, steric clashes), providing a realistic picture of what each method can deliver in practice.\n\n3. Clear trade-offs and actionable guidance: The study quantifies the affinity-validity trade-off and explicitly advocates hybrid pipelines, giving researchers a concrete direction for future model development.\n\nKey Weaknesses:\n\n1. Black-Box Oracle Assumption: The evaluation of 1D and 2D models relies on treating the docking software (Vina) as a black-box scoring function. In a real drug discovery pipeline, obtaining a docking score for a single molecule is computationally expensive, making this approach less practical than the integrated generation of 3D methods, despite the performance trade-offs identified.\n2. Pose Generation for 1D/2D Models: For 1D and 2D models, a docking pose must be generated after the molecule is created. The paper uses Vina for this, but the choice of docking configuration and software can significantly impact the results, adding a variable that is not present for native 3D generators.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The paper presents a comprehensive and well-structured benchmark study, and its conclusions are firmly grounded in extensive experimental data and rigorous analysis.\n\n1. 3D models excel in binding affinity but show inconsistencies in chemical validity and pose quality.\n\nEvidence:\n\n    Binding Affinity (Docking Score): Table 2 (Top-1 score), Table 6/7 and Figure 3 (Top-10/100 average) consistently show that 3D models, especially Pocket2Mol, achieve the best (most negative) docking scores across almost all protein targets.\n\n    Pose Quality and Chemical Validity: The evidence for the trade-off is even more robust:\n\n        Strain Energy: Table 3 and Figure 2 show that 3D models have the highest median strain energies (e.g., TargetDiff: 487.72, 3DSBDD: 157.11), indicating high internal energy and unstable conformations.\n\n        Steric Clashes: Table 18 and Figures 4-6 show that 3D models like Pocket2Mol and TargetDiff have significantly higher mean clashes (e.g., 20.42 for Pocket2Mol on 8JJL) compared to 1D/2D models (typically under 10).\n\n        PoseBusters Metrics: Table 17 provides definitive evidence. 3D models have low pass rates for key chemical and intramolecular validity checks (e.g., \"All Atoms Connected\": Pocket2Mol 0.763, 3DSBDD 0.459; \"Internal Energy\": ~0.3-0.7 for 3D models vs. ~0.95+ for top 2D models).\n\n2. 1D models demonstrate reliable performance in standard molecular metrics but rarely achieve optimal binding.\n\nEvidence:\n\n    Molecular Metrics: Table 4 shows that 1D SMILES-based models have perfect validity (1.00) and high diversity (~0.88-0.91). They also perform excellently on pharmaceutical properties like QED (Table 13, consistently ~0.9+) and SA (Table 16, consistently low scores ~1.9-2.1).\n\n    Sub-optimal Binding: The docking score tables (2, 6, 7) consistently show that while 1D models are competent (scores around -10 to -11), they are almost never the top performers, which are dominated by 3D models.\n\n3. Claim: 2D models offer balanced performance.\n\nEvidence: \n\n\tThis is convincingly demonstrated by the data. 2D models consistently perform well across all evaluated metrics without being the absolute best (and worst) in any single category.\n\n    Docking: They achieve good, but not top, docking scores (e.g., DST, JT-VAE scores in the -10 to -12 range in Table 2).\n\n    Molecule Quality: They have perfect validity and high diversity (Table 4).\n\n    Pharmaceutical Properties: They score excellently on QED and SA (Tables 13, 16).", "audience_yn": "Yes", "audience_explanation": "1. For Researchers in Generative Models and Structured-Output Learning\n\nThis is the core TMLR audience. The paper provides a crucial large-scale, cross-algorithmic benchmark for a complex, real-world structured-output task. Researchers developing new generative algorithms (especially for graphs, sequences, or 3D geometries) would be highly interested in:\n\nThe Performance Trade-offs: The clear, data-driven identification of the \"affinity-validity trade-off\" is a fundamental insight. It shows that simply having an inductive bias for 3D structure (e.g., via diffusion models) is not sufficient and can lead to downstream problems (high strain, bad poses). This informs future model design.\n\nThe Benchmark Itself: The paper provides a new, rigorous evaluation framework that moves beyond simple property metrics (like QED, SA) to include critical functional metrics like docking scores and pose quality. This sets a new standard for evaluation in the field that others will need to follow and compare against.\n\n\n2. For Researchers in Reinforcement Learning and Optimization\n\nThe paper evaluates methods based on RL, Hill Climbing, and Genetic Algorithms. The comparative results are valuable for these sub-communities:\n\nRL (Reinvent, MolDQN): The results show how RL-based strategies stack up against other paradigms like generative models and GAs on a complex, sparse-reward task.\n\nGenetic Algorithms (SMILES-GA, Graph-GA): The paper shows that these \"traditional\" optimization methods remain very competitive, especially in terms of diversity and reliability. This is an important reminder for a field often focused on deep learning.", "requested_changes": "1. Add experimental validation of hybrid models  \nThe paper proposes combining 1D/2D and 3D methods but provides no data.  \n\nSuggestion: Implement at least one hybrid pipeline (e.g., JT-VAE → Pocket2Mol) and report docking scores, validity, etc.  \n\n2. Explain model failures and address bias  \nSeveral models (e.g., TargetDiff, ResGen) fail on multiple targets, yet no diagnosis is given.  \n\nSuggestion: Analyze why these models fail (pocket compatibility, generalization, etc.) and discuss whether these failures skew the overall conclusions.  \n\n3. Include computational-cost analysis\n   Report runtime and memory usage for each model to clarify scalability.\n\n4. Expand target diversity  \n   Add more structurally diverse proteins (e.g., GPCRs, kinases) to test generalizability.\n\n5. Provide statistical-significance tests  \n   Apply t-test or Mann-Whitney U test to performance differences to reinforce claims.\n\n6. Release demo code and generated datasets  (at least confidentially to review)\n   Open-source scripts, checkpoints, and molecules to facilitate reproduction and follow-up studies.", "broader_impact_concerns": "There is no significant ethical risks are apparent since the study is a benchmarking exercise that uses only publicly available protein structures and open-source generative models, generates no new bio-active compounds, and involves no human or animal data.  \n\nThe only minor gap is that the current manuscript does not discuss possible dual-use if future users repurposed the evaluated 3D generators to design harmful ligands. A brief Broader Impact Statement (1–2 sentences) acknowledging this possibility and recommending responsible disclosure and safety screening would suffice.", "additional_comments": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "81v0gXJ1rp", "reviewer": "Reviewer_SjoD", "summary": "", "questions": "", "limitations": "", "rating": null, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "Yes", "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": "", "paper_topic_and_main_contributions": "", "excitement": null, "reproducibility": null, "ethical_concerns": "", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "summary_of_contributions": "This article summarizes various SBDD methods and provides a unified comparison of them. A key aspect of SBDD is how to handle 3D equivariance. The main contribution is to put all the methods together and conduct a fair comparison.", "claims_and_evidence_yn": "Yes", "claims_explanation": "The authors evaluated all assessable metrics in the field of SBDD, with the only remaining step being to conduct actual biological experiments.", "audience_yn": "Yes", "audience_explanation": "SBDD methods are currently quite popular in the machine learning community. However, as in all other AI fields, when a multitude of methods emerge at once—each claiming to achieve the best performance—it can create significant confusion for medicinal chemists and potentially lead to substantial economic losses. Therefore, an independent third party is needed to conduct a systematic evaluation of all existing methods.", "requested_changes": "AlphaFold2 addresses equivariance but later removes this processing. The authors should discuss how different methods handle equivariance (SE3, O3). Additionally, I believe it is more important to provide a comprehensive score, such as w1*drug-likeness + w2*binding affinity + ..., and ultimately present an overall ranking. What readers want to gain from this article is a prioritized order for using different software in small-molecule drug design. I understand that different methods have their own advantages in various aspects, but as a medicinal chemist, one always needs to prioritize which software to use first. Although hill climbing and RL can be used for drug design, the compared methods do not include these approaches. Instead, TargetDiff employs a diffusion model, so please include an introduction to diffusion in the methods discussion.", "broader_impact_concerns": "N/A", "additional_comments": "Thank you for addressing all these concerns. I have no further questions.", "extra_scores": {}, "extra_text": {}}]

Author rebuttal:

Decision: Accept (poster)