MolEditRL: Structure-Preserving Molecular Editing via Discrete Diffusion and Reinforcement Learning
Authors: Yuanxin Zhuang
Dazhong Shen
Ying Sun
Venue: ICLR 2026 Poster (2026)
Keywords: Molecular Editing; Discrete Diffusion; Reinforcement Learning
Primary area: generative models

Abstract: Molecular editing aims to modify a given molecule to optimize desired chemical properties while preserving structural similarity. However, current approaches typically rely on string-based or continuous representations, which fail to adequately capture the discrete, graph-structured nature of molecules, resulting in limited structural fidelity and poor controllability. In this paper, we propose MolEditRL, a molecular editing framework that explicitly integrates structural constraints with precise property optimization. Specifically, MolEditRL consists of two stages: (1) a discrete graph diffusion model pretrained to reconstruct target molecules conditioned on source structures and natural language instructions; (2) an editing-aware reinforcement learning fine-tuning stage that further enhances property alignment and structural preservation by explicitly optimizing editing decisions under graph constraints. For comprehensive evaluation, we construct MolEdit-Instruct, the largest and most property-rich molecular editing dataset, comprising 3 million diverse examples spanning single- and multi-property tasks across 10 chemical attributes. Experimental results demonstrate that MolEditRL significantly outperforms state-of-the-art methods in both property optimization accuracy and structural fidelity, achieving a 74% improvement in editing success rate while using 98% fewer parameters.

Published as a conference paper at ICLR 2026
MOLEDITRL: STRUCTURE-PRESERVIIN R EDITING VIA DISCRETE DIFFUSION AND REINFORCE- MENT LEARNING
Yuanxin Zhuang', Dazhong Shen"; Ying Sun
1 Artificial Intelligence Thrust, Hong Kong University of Science and Technology (Guangzhou)
2 College of Computer Science and Technology, Nanjing University of Acronautics and Astronautics aahuan4436cconnee hkust taasstgz.eeee www.ga...c..oo shendazhong edu. cn, suryinggilly@gmai com
ABSTRACT
Molecular editing aims to modify given molecule to optimize desired chemical properties while preserving structural similarity. However, current approaches typ- ically rely string-based continuous representations, which fail to adequately capture the discrete, graph-structure nature molecules, resulting in limited structural fidelity and poor controllability. In this paper, propose MolEditRL., a molecular editing framework that explicitly integrates structural constraints with precise property optimization. Specifically, MolEditRL consists of stages: (I) a discrete graph diffusion model pretrained to reconstruct target molecules condi- tioned on source structures and natural language instructions; (2) an editing-aware reinforcement learning fine-tuning stage that further enhances property alignment and structural preservation by explicitly optimizing editing decisions under graph constraints. For comprehensive evaluation, we construct MolEdit-Instruct, the largest and most property-rich molecular editing dataset, comprising million diverse examples spanning single- and multi- property tasks across 10 chemical attributes. Experimental results demonstrate that MolEditRL significantly outper- forms state-of-the-arr methods in both property optimization accuracy and struc- tural fidelity, achieving a 74% improvement editing success rate while using 98% fewer parameters.
INTRODUCTION
Designing molecules with tailored properties is essential for drug discovery Ma all (20224); Huang al. (2025). Unlike de novo molec- ular generation that creates molecules from
126
best Performance lowest FCD
scratch WWng et all (2022).. molecular edit- ing Hui et all (2022) focuses precisely mod- ifying existing molecules to optimize targeted properties while preserving known structure- activity relationships Hanscfi (1969).
Drughoust
Gallin
BirTS
Traditional molecular editing approaches fall into three main paradigms: (1) Rule-based methods apply predefined fragment transforma- tions Chen et all (2021); Fu et all (2021), but Figure 1: Performance, FCD, and parameter size their generalization is limited by manually de- comparison.
Galtrno
7.38
355H
RESIVENTT
252M
10
32
14
15
38
20
signed rules. (2) Latent generative modelslin et all (2018); Shi et all (202() optimize molecular
DDahhong Shen and Ying Sun co-corresponding authors.

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properties in continuous spaces, yet often struggle with fine- aineiiri control due to latent compres-
sion. (3) gequence-to-sqqeeeee methodsHe.cc ot all (202T); Locffler al (2022);; Wu et all (22222 frame editing as SMILES tramslation, enabling scalable learning but lacking structural precision, as small token changes can produce unpredictable or invalid edits Kusner et all (2017); Krenn et all
(2020)
Recently, language models have expanded the landscape of molecular editing by integrating natural language understanding with chemical representations, enabling models to leverage semantic in- structions for molecular modifications: (1) Graph-embeddiing approach Liu et all (2023) encodes both molecules and textual instructions into shared latent space via contrastive learning. (2) SMILES based models Ye et all (2025); Le & Chawle (2024);; Dey et all (20255) use retrieval- augmented generation or adopt instruction tuning to enhance editing relevance. (3) SELFIES-bass method Fang et al] (2024) incorporates chemical feedback to reduce syntax errors and improve property alignment during generation. Despite their promise, these language-basse struggle to preserve scaffolds or perform precise, localized modifications, due to the non-uniqueness of textual molecular representations. Structurally similar molecules may appear textually distant, leading to inconsistent and unreliable edits Noutahiera all (20224); Aniis all (2019).
methods often
There are several critical challenges in molecular editing for ensuring structural preservation and editing precision: First, molecular editing must explicitly align with the discrete, graph nature of molecules [ong & Sun; Shen et (2022).. String-based representations fail to explicitly encode topological constraints, often limiting the models ability to preserve scaffolds and perform localized modifications. Second, existing methods trained solely on fixed datasets lack mechanisms for actively exploring novel editing strategies, restricting generalization and adaptability complex or underexplored regions of chemical space. Third, performing discrete edits directly on molecular graphs while preserving structural fidelity and aligning with natural language instructions is techni- cally challenging due to the non- non-differentiable nigh-dimensional nature of graph representations.
In this paper, we propose MolEditRL, structure-aware molecular editing framework that combines discrete graph diffusion with reinforcement learning. MolEditRI. first employs discrete diffusion to reconstruct target molecules conditioned simultaneously on source molecular structures and natural language instructions, effectively capturing both structural and semantic relationships. To further en- hance the precision of property optimization and alignment with instructions, we introduce editing- aware reinforeement learning guided by explicit property rewards, while incorporating constraints to preserve structural integrity. To enable comprehensive evaluation, we introduce MolEdit-Instrus large-scale molecular editing dataset containing 3 million editing examples spanning 10 diverse chemical properties, including biological activities, physicochemical attributes, and synthetic ac- cessibility. Compared to existing datasets et (2025); Dey et al, (20251)) MolEdit-Instrut provides broader property coverage and more realistic single- and multi-property editing scenarios. We release MolEdit-Instruce publicly on Hugging Face to facilitate future research.
Experimental results demonstrate that MolEditRL MolEEiitLssignnficanntt outperforms sate-of-theearr methods in both editing accuracy and distributional fidelity (measured by Fréchet ChemNet Distance, FCD). Remarkably. MolEditRL achieves 74% improvement in editing success rate over leading baselines while requiring 98% fewer parameters (Figure MI.. Our contributions are summarized as follows: (1) We propose MolEditRL,, a molecular editing framework explicitly designed to maintain structural integrity during editing. (2) We introduce a two-stage training strategy that combines discrete dif- fusion pretraining with reinforcement learning fine-tuning, achieving precise property optimization with structural constraints. (3) MolEditRL achieves SOTA editing performance with substantially fewer parameters and the lowest distributional distance (FCD) compared to existing methods.
2 RELATED WORKS
Molecular Editing. Molecular editing aims to modify a given molecule to enhance specific chemi- cal properties while preserving its structural similarity. Formally, given a source molecule Gree and textual instruction S describing desired modifications, the goal is to generate edited molecule Gigs that satisfies both the semantic intent of S and structural similarity constraints. This formula- tion enables flexible, user-centric molecular design where optimization objectives can be expressed intuitively. Existing molecular editing approaches typically fall into three main paradigms: (1) Rule-based Graph Editing. These methods directly manipulate molecular graphs using predefined
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or data-driven transformation rules, such fragment replacements or bond editing templates, in- spired by Matched Molecular Pairs (MMP) Dalke al (2018); Chen at (2021;; all (2022). While offering high chemical interpretability and precise local modifications, their generalizability is limited by the coverage and flexibility of manually or heuristically derived rules. (2) Latent Gen- erative Graph Editing. Approaches such JT- lin et all (2018) and GraphAF all (2020) encode molecules into continuous latent spaces and decode edited structures by sampling. Hierar- chical decoding techniques like HierG2G Jmn et all (2020); Zhuang et alj (2025) enhance structural preservation by generating molecules in coarse-to to-fine manner. However, these methods frequently face issues such as information loss due to latent compression. resulting limited accuracy and in- sufficient control over edits. (3) Sequence-based Generation. These approaches treat molecular editing as a sequence translation task, converting source SMILES strings into target SMILES us- ing Transformer-basee models suffer from syntactic instability and renreeepeeee representation ambiguity: structurally similar molccules can have significantly different SMIIEE representations, and small token-level edits may lead to un- predictable or chemically invalid outputs, limiting their precision and structural controllability. (4) Language-basse based models. MOLGEN Fang al. (2022) addresses SMILES fragility by adopting the SELFIES representation. Methods such as ChatDrug Lill et all (20224) DrugAssia eY all (2022)) and Re2DF & Chawla (20ZZ) utilize retrieval-augmented generation instruction tuning en- hance editing relevance. Additionally, embedding- methods leveraging diffusion Xiong et all (2024) or contrastive learning Lu (2023) have been proposed. Nonetheless, these approaches continue to rely on textual or continuous representations that lack explicit alignment with discrete molecular graph structures, compromising structural fidelity and editing accuracy.
architectures et (202T); Loeffler et al (2012)); [Wu et all (20Z2). These
Reinforcement Learning in Molecular Generation. Reinforcement learning (RL) provides a flexible framework for molecular optimization by formulating molecule generation as Markov Decision Process (MDP), in which agents sequentially modify molecular structures to maximize rewards associated with desired chemical properties Sridharan et all (2024)); Di et all (2025); Sun aL (2022);; D1 ct all (2026). SMILES ESalsed RL methods such ReLeaSE Popova et all (201) and REINVENT Olveerona et al (2 guide generative models using property predictors and prior policies. Graph-based RL methods, including MolGAN De Cao & Kipfi (201), GCPN You et all (2018), and MolDQN Zhou et all (2019), facilitate goal-directed graph construction through adversarial training, policy gradients, or Q-learning Although effective in exploring chemical space, these RL-based frameworks typically focus on de novo molecule generation and lack explicit mech- anisms enforce structural constraints derived from source molecules, limiting their applicability to structurally constrained molecular editing tasks.
METHOD
We present MolEditRL, structure-preserving molecular editing framework trained in two stages. First, molecules and instructions are encoded into unified graph-text representations. Then, a structure-aware editing network is trained via (1) discrete diffusion pretraining to reconstruct tar- get molecules from noisy graphs and instructions, and (2) reinforcement learning fine-tuning to optimize property alignment while preserving structural fidelity.
3.1 MOLECULAR TOKENIZING
As illustrated in Figure Q (a), a molecule is represented as an attributed graph = (V, E), where V denotes atom nodes with associated features, and E denotes bond edges with bond-type attributes. The editing instruction is a sequence of tokens S = [ sn). Given a source molecule graph Gen = (Vrre, F.sre), the model aims to predict the target graph Guy. (Vigt; Fage) that reflects the required edits. These components are embedded and concatenated into a unified input sequence:
ho [ ,n,, arC
sre
Agt #
lgt a
tgt
E BB(+k+m)xdd
where hı, h hn E dR are embeddings for the instruction tokens, here h hare E Rdh encode source atoms, and haat #n*k+1...... h lgt and variable-size graphs are handled via sequence serialization with dynamic padding.
a E Rd encode target atoms. d, is the models hidden size,
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(a)
(b)
Structure-Presetti Pretraining via Discrete Diffusion
Molecular Tokenizing
Instruction
ht
Forward Process
T
Gar
Structure
Dare
E
E
Aware
lat
Network
B
V
E
Reverse Process
E
5pt
E
(c) Eddting-Awark Fine- Fine-Tuning via Reinforcement Leaning
Guidance
Help Improve
t=
Synthetic Accessibility
this Molecule.
G
Oracle
r GGie; m.) lo (Gog Gige S, Gnd + 3KL
Ysa 0.32
Figure 2: Overview of MolEditRL.
3.2 STRUCTURR-PPEESFRVVIG EDITING NETWORK
Recent work has demonstrated the effectiivnness unified architectures for multi-modal learning X ang al] (2022);; Zhao et (2025).. Such unified frameworks enable better capture of cross-modal dependencies and shared representations while reducing architectural complexity. To enable pre- cise and structure-aware molecular editing, propose the Sinucture-Preservivee
Editing Network, which jointly encodes the semantic intent of natural language instructions and the topological fea- of source and target graphs. We initialize our transformer encoder with a pretrained RoBERTa model Lu al (2 but enhance it with a structure- aware attention mechanism. This mechanism injects bond-level connectivity priors into attention scores via learnable bias terms that encode graph connectivity, guiding attention flows to preserve structural integrity while selectively updating target representations. For tokens and j at layer I, the raw attention score AL,, is computed as:
1 (l(WW) (1)W)) + b
(2)
dd
where Wo, Wx E Red xd2 are learnable projection matrices. cies and better preserve structure during editing, we introduce 1/,,, to encode structural priors:
To incorporate graph-level dependen-
Fasrele n, n, Eigeli (n + k), j (n :) A
if i,j S
if = and n + < i,j,
b/1
=
if 1 > 0 and n + < i, otherwise.
(3)
0,
The bias term 6% preserves structure by injecting source adjacency all layers, using target adja- cency at the first layer, and propagating attention from previous layers to maintain topology- aware attention flow. The attention weights are then normalized, and the updated hidden states are caleu- lated as:
exp(Al)) A Σ. exp((A)))
h = -+1=-Ah (hWWW))
(4)
where Wv E Rd xd and Wo E dd xds are learnable parameters. After L transformer layers, the model outputs ((Vgg)) m for atom types and p( Egg) E Rmxmxb for bond types, where is the maximum number of atoms per molecule, the atom vocabulary size, and b the number of bond types. Edge predictions symmetrized (eij e/2 respect bond-direction constraints.
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3.3 STRUCTURE- PRESERVVNG PRETRAINING VIA DISCRETE DIFFUSION
As Figure D (b), we pretrain the editing network via a discrete denoising diffusion process condi- tioned on the source molecule and instruction, enabling topology-aware generation.
Forward Process. We define a discrete forward process that gradually corrupts the target molec- ular graph Gigs over T timesteps. At each step t, atom and bond features are independently masked with probability (()) = (T - 11-
Gla) = II T |
(Gggg
|
(Vigi Qf
Eig Q.¹).
(5)
where Qr is transition matrix that gradually increases the masking and g() denotes the transi- tion distribution of the forward diffusion process. At each step, each element remains the same with probability 1 1- Btt) or transitions to [MASK] with probability (t)). This process gradually converts Glg. into fully masked graph G igi
2. Reverse Process. To recover Glg. from the fully corrupted graph Glys train denoising model de to iteratively refine Gigi conditioned on the source molecule Gare and instruction S:
pe @Goor-- Gigy G S)
II 1=1 pe (Glyt
Gigu
Garer S).
(6)
At each timestep, do predicts the denoised graph Glgi using the editing network described earlier.
Training Objective. Although instruction S is not corrupted during diffusion, we include cross-entropy loss on instruction tokens enforce semantic alignment with the predicted molecule.
n+
Cpre
Σ
CE(U; (v()) +
Σ
CE(EES, ((s(e(g) + CCE(sE, fe(s:)) (7)
1=1+1
+J=++b+1
Sampling. At inference time start from fully masked graph Cl and iteratively apply the reverse process. Each step: (1) Graph-Text Encoding: The transformer dhe encodes Gige G and S. producing logits (VVig) and ((Egg). (2) Prediction: Following zg po-parameterization Austtin all (2021,, the model predicts the denoised graph as G = arg max pi Vig. Fag.)- (3) Sampling: The
next graph Gig is sampled from the posterior (( Gig Gly) by independently sampling atoms and bonds: Gog ~ Po II,, Po(c(
3.4 EDITING-AWARE MUN-IGG VIA REINFORCEMENT LEARNING
While the pretrained diffusion model captures molecular structure and ensures validity, it lacks ex- plicit optimization for property-
editing. We address this by introducing Editing-Aware Re- inforcement Learning, which fine-tunes the model using rewards computed from well-established chemical toolkits (RDKit Bento et all (2020) and TDC Huang et al. (202I)). A KI.-regularized objective guides optimization toward desired properties while preserving structural consistency.
MDP Formulation for Molecular Editing. We recast discrete graph denoising as Markov Decision Process (MDP) tailored specifically for molecular editing Uehara et all (20224); (1) State:
(S, G C'L'i includes the instruction, source molecule, and current noisy target graph. (2) Action: 0, Gig is sampled from the models predicted distribution over denoised graphs at the next step. (3) Initial State: Po(su) = p(S S) p(Gse) q (( combines an instruction, a source molecule, and a fully masked target graph. (4) Transition: Given sampled action a,, the next state becomes $1+1 = (S, Gaee az). (5) Policy: The stochastic policy Ty (a+ sx) outputs a categorical distribution over atom and bond types, from which ot is sampled. (6) Reward: A scalar reward is
assigned only at the final step (f = T 1) to evaluate editing success:
R(s, a.) = ( (r(G S, Gaw),, i T-1,
(8)
0.
otherwise,
where r(-) equals the generated molecule successfully performs the required edit, 0.2 it is chemically valid but does not fully satisfy the instruction, and 0 if it is invalid.
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2. KL-Regularized RL Objective. To optimize molecular editing while preserving structural fi- delity, we adopt KL-regularized reinforcement learning objective. Formally, the objective is:
(())= = -Bp(s) p(G.)) (Gg) [r( S, Gare)
+ B Σ (os( (po Gigs Ge Ppre (Gg Gigs S, Gw
(9)
where pø is the current policys distribution denoised target molecules timestep t. and Ppec is the pretrained diffusion model, acting as a structure-aware prior. The coefficient ,3 balances reward maximization and structural consisteney and is set to 0.1 in our experiments. To stabilize training, we normalize the final reward of each trajectory within each batch: À r-mean(r) std())+10- To reduce
computation and improve efficiency, we apply policy updates at a fixed stride ts. rather than every timestep. Specifically, we define the update gradients only at E T. The resulting policy gradient becomes:
T {t [1, T)
mod t, 0}, and compute
VeJ = E E
[A. ErET Vg log pe Po Gige S, Gsee) - BBET VoDKL (pe Pma)) (10)
3. Gradient Estimation via zo Parameterization. We estimate the gradient of the reward term (i.e., the first term of Eq. H). By the policy gradient theorem, the gradient is:
Vallogr [r] = Egg [r(Cke
[r S, Guee)
Σney
Vo Volog po Po Cigh: Gate)
(11)
Since rewards are only available t=0, directly estimating the gradient suffers from high vari- ance atl (20244). To reduce this, we adopt zo- parameterizzaion Austin all (2021), rewriting the reverse transition as:
po pC G S, Garc)
-
((G G
Gog
) po (G G S, Gac),
(12)
where g(-) denotes the corruption distribution from the forward process. This approximation yields: G
a log pe (Gozz Gi S, Gare) se V log
G S, Gsee).
(13)
A detailed derivation of this approximation is provided in Appendix Q Under this formulation, the gradient of the reward term can be approximated via reward-weighted cross-entropy loss:
Eierr(Cy S, Gwe) (I, CH(ef, po(. G S, Ga.))) ) + En CE(eT)J pe(: Gigs: S, Gavl)) (14)
(E,CH(e),
where of and ei atoms and bonds in the final predicted molecule G reused supervision targets at each selected step E T.
EXPERIMENTS
4.1 DATA CONSTRUCTION
We construct MolEdit, large-scale and property-rich dataset specifically tailored for molecular editing with natural language instructions. Existing datasets, such as MolOpt-Instructions Ye all (2025), MuMOInstruct Dey al] (20251)) and C-MuMOInstruet Dey et al, (2024)) are limited in either property coverage, task diversity, or data scale. MolEdit addresses these gaps by extending the property set to 10 diverse chemical attributesspannigg biological activity, physicochemical charac- teristics. and synthetic accessibility Zhang et al] (2025)aand defining 20 representative editing tasks (10 increases and 10 decreases). It contains million high-quality molecular pairs (967K unique), each exhibiting substantial property shifts while maintaining high structural similarity (Tanimoto scores from 0.650 to 0.982). This provides a more realistic and comprehensive testbed for training and evaluating editing models. Further dataset construction details are provided in Appendix RI The model architecture is described in Appendix H. and the training setup is detailed in Appendix CC Ad- ditionally, further validate approach against existing methods, also conducted pretraining and evaluation experiments on the C-MuMOlnstrret dataset, with comprehensive results presented in Section 8.5.

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Table 1: Comparison of molecular editing models across tasks. Bold indicates best performance.
Arrows (t, J) denote desired property increase or decrease.
Moded
Taok
Bis15
0
11
0/0
D10
0.086
0/86
13.00
0.0034
0.004
0
13:40
MalCinn
0:124
0.024
0.08
0248
0.016
0.015
0.020
0385
14.19
Reinertt
0.723
0:130
0.153
0.106
0.001
0.35
10.58
0.582
0.010
0.07
0.063
0.066
0.081
Gellar'o 0.34
GSK3 0T
0.924
0.976
0.799
0.164
0,175
01343
0.258
0.254
0.307
0.264
0.122
0.313
0.232
0.32
10.32
9.42
SA.
0.988
0936
01537
0.250
0.581
0.582
0.382
0.551
0.363
0.558
0.232
0.552
0.305
0.346
8.55
M:06
Ocliae
0.114
0.125
0250
0.170
0.298
0.885
0.238
0.276
0.194
0.208
0.145
0.162
0.250
0.545
0.318
0.245
0.104
1967
MuIBBrR
0.92
0,342
0.389
0.364
0.363
0.243
0.254
7.99
0.988
0,628
0.636
0.694
0.743
0.245
0.251
7,30
BasTs
0
0
13:31
0
1.00
0.101
0
MalsoriSTM
0.341
0094
0.98
01966
0.40
0.04
0,052
13:41
0.622
0.084
0.005
0.179
0.146
0.811
0.132
Reimernst
0:301
0334
0111
0.1M
0.006
0::99
11.70
0.581
0.173
6.00
0.189
0.507
0.10%
0.109
13.15
DropAssis
QED
0:950
0.532
0.433
0,40
CA56
0.200
0255
0.58
Haccept)
0.84
0.372
0.578
0.325
0:335
0.12
0152
12:35
SAy
0.01
0,014
0:00
9:13
Light
0:906
0.324
0.263
0,07
C.
0.183
0.197
0.192
0.158
0.135
0340
0:905
0.257
0.343
0.254
0.55
0.169
0.176
0.904
0.309
0.216
0.248
0220
0.006
0.125
13.00
0:911
01.200
0.221
0.24
0.51
0.154
0,192
19.53
Mallddaa
0/914
0.622
0.649
0.675
0.715
0.366
0.271
7.54
8:946
01316
0.334
0.34
0.356
0.234
6:332
18.11
MelGin
0.029
0189
0,75
0.12
0032
0.083
0,31
13.75
MalookeSTM
0.603
0.03
0.44
0.000
0.109
0,076
0081
11.43
0.635
0.014
6005
0.031
0.035
0.40
0/046
14.37
Drog/siis
Reieveast
DRD0L
0.522
0.033
0:423
0.200
0-31
0.153
0.163
0.124
0.366
0.35
0242
11.40
Hocceptt
0,635
0.956
0.230
0.017
0.16
0.24
0.103
0111
0,91
0.16
0.00
1200
Gollefo
0.005
0.115
0.104
0.509
10.19
Q877.
0.886
0.042
0.667
0.00
0.02
0/699
15200
Gaim C_M
0.916
0.022
0.890
0.121
0.144
0.116
5.188
11.21
0.997
0.125
0.131
0.119
0.125
0.94
0.89
11.26
Gelteff-C I.
0.909
155
0
0.188
0.718
0.164
0.58
10.07
0.813
11183
6196
0.198
0.355
0144
0.161
10.35
MaRMERR
0.986
0
0,535
0.548
0.566
0.352
0.361
7.38
6.958
0.69)
0.649
0.432
0.436
0.238
6.332
9.79
4.2 EVALUATION METRICS
To comprehensively assess molecular editing performance, we use the following metrics to evalu- ate chemical validity, editing accuracy under structural constraints, and overall molecular quality, Chemical validity and property values are computed using RDKit Bento all (2I2I) and Therapeu- ties Data Commons (TDC) Huang et all (2021), two widely used and trusted toolkits for molecular analysis: (1) Validity is the proportion of generated molecules that are chemically valid, reflecting the model's ability to produce syntactically correct molecular structures. (2) Óverall Accuracy (A)can(7)) and Valid Accuracy (Accvatia(T)) jointly measure editing success under structural simi- larity constraints. We employ three complementary structural similarity metrics with corresponding thresholds: Tanimoto similarity (TS > 0.65) Bajusz et al, (2015), Maximum Common Substructure similarity (MCS 2 0.6) Cao et all (200)), and Graph Edit Distance (GED < 4) Gao et all (2010). For each threshold т, (TC the percentage of all outputs that satisfy both the desired prop- erty changes and structural similarity constraints; AcCvaia(T) restricts this to valid molecules only. This multi-metric approach provides comprehensive evaluation of structure preservation: TS cap- tures fingerprint-basee similarity, MCS quantifies shared molecular scaffolds, and GED measures the minimum structural editing operations required. (3) Fréchet ChemNet Distance (FCD) Prenei etall (201)) quantifies the distributional distance between generated and reference molecules. Lower FCD values indicate better alignment in chemical space, capturing both diversity and realism.
4.3 EXPERIMENTAL RESULTS
We compare MolEditRL against publicly released, large-scaletrained molecule-editing models, eval- uated in their released form; baseline details are provided in Appendix A Table MI compares the performance of various molecular editing models on single-property and multi-property tasks using our comprehensive multi-metric evaluation framework. MolEditRL consistently achieves the high- est editing accuracy across all tasks and structural similarity metrics (TS20.65, MCS>0.6, GED<4). Although SELFIES-baad models (BioT5, MolGen) guarantee perfect chemical validity, they fail to maintain structural similarity, achieving zero accuracy on most metrics, which reflects a lack of structural alignment. Arugss based on SMILES and LLM fine-tuning, maintains high validity but performs significantly worse than MolEditRL on both Accall and Accvalid. This indicates that chemical correctness alone insufficient for precise, property-aligned editing. Although DrugAs- sist generates valid molecules, it struggles to retain scaffold similarity while optimizing properties. All baseline models yield substantially higher FCD scores than MolEditRL, suggesting greater di- vergence from real molecule distributions. In contrast, MolEditRL generates molecules that are both valid and distributionally faithful, benefiting from aructure-aware graph editing. For multi- property tasks, evaluate scenarios aligned with real- world drug discovery objectives, such improving
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Table 2: Generalization to unseen properties. Results on editing three held-out molecular properties (BBBP, HIA, hERG) that are excluded from the pretraining dataset.
Model
Task
Validity
(0.65)
Actrut (0,65)
(0.15)
FCD
Validiyy
Actia (0.65)
Actiuaa
(0.65)
(0.15)
Accin
(0.15)
PCD
BarT3
1,0
0.452
21,4599
DrugAssist
0:9719
0,5006
0.3155
0.3727
7.4848
0.9879
0,3952
7,9316
GeLLMPO_M BBBP.
0.9
0.17
0.1889
0.33
0.3667
7,8632
BBBP
0.508
0.06
0.0661
0.356
0.3921
9,8042
GeLLMPOLL
0.92
0.116
0.1261
0.292
0.3174
7,8038
0.91
0.254
0.2791
0.716
0.7868
7,2142
MelEdRiRL
0,944
0,326
0.337
0,516
0.5347
7,0901
0.954
0.409
0,418
0,782
0,8075
6,7043
Bro15
100
0.0
0.0
DrugAssist
0,982
0.976
0.2725
0,4068
0.168
9,7314
GeLLMPO M
HIA
0.904
0.124
0.1372
0.286
0.3164
8,2303
0.904
0.348
0.385
0.662
0.732
7,2866
GeLLMPO I.
0.894
0.134
1499
0.382
0-4211
8. 11844
0.922
0.223
0.2408
0.554
0.6009
7. 7,7484
MolEdaRL
0.98
0,374
0.3816
0.625
0.6396
6.8726
0.986
0.446
0.4523
0.738
0,7459
6,7938
BirT3
1.0
0.00
0.0
0,396
0.396
16.312
1.0
0.0
0.0
0.368
0.368
13,0537
DrugAssist
0:9639
0,299/
0.3098
7,815
0.9933
0.2294
0,4064
9,9241
GeLLM
BERGI
0.162
7,9945
0.194
0.218
0.554
7,5913
G
0.914
0.144
0.1575
0.424
0:4639
3.0866
0.922
0.062
0.0672
0.361
0.3948
11.7363
MolEdieRL
0.986
0.474
0.4807
0.694
0,70339
6.0764
0.972
0.31
0.3189
0.59
0.6147
6.8384
Table 3: Performance comparison on C-MuMOInstruct dataset. Bold values indicate the best perfor- mance for each metric.
Propectios
OdLaMo-..
Validity
GeLLNTO-CCMM
Tital Accunccy GLLLMMO-CC.. MclbaiLL
GELLM"O-CUM
Volid Accerncy GCLMMO.....
I19118
[19076
(1952
0.3354
0
0.382
112261
03557
0,3172
argolbnr-qid
0.914
03925
11.82
01173
0:25
6.27%
181863
012310
(1229)
0.11
0.944
01.20
01218
0.239
6.342
013216
0.231
03314
B19128
(U958
11391
0.366
0L1465
0.3948
0.294
caee-dndZeery
03944
IU985
01133
021/4
0.286
11583
02300
0,276
ouporbiiiy insttgenicity shapp
015519
0,3569
0.1818
0.2055
0.296
bibpe reggege
0.938
0.3300
0.944
1.156
0.1887
0.336
0.2065
0.3373
0.375
Nes Kireirerrnnn Eneeetttttteei iphogpage
0:942
0.542
01.45
0.212
0.2213
0.203
0.225
0.3347
0.3135
stability and synthesis (Haccept4, LogPt), balancing drug-likeness and accessibility (QED1, SAI), and managing conflicting constraints (Hacceprt, MW1, QED4). MolEditRI, consistently outper- forms all baselines across all similarity metrics, with the convergent high performance across TS, MCS, and GED providing comprehensive evidence of effective multi-objective optimization while preserving molecular scaffolds. Extended results single-pre peopeetropert and multi-properyy tasks available in Appendix D and Appendix N. respectively.
4.4 GENERALIZATION TO UNSEEN PROPERTIES
We evaluate MolEditRL on three propertiesBBBP, HIA, and hERG inhibitionthat are entirely ab- sent from the MolEdit-Instreee Instruct pretraining corpus. These pharmacokinetic and safety-related at- tributes allow us to assess the models ability to adapt to new optimization objectives not seen dur- ing pretraining. In this setup, the pretrained model is fine-tuned via reinforcement learning using property-speciffic oracles, without requiring any prior data or moleculeproperty pairs for these tasks. MolEditRL only receives natural-language descriptions of the unseen objectives and relies on RL fine-tuning over arbitrary molecules, simulating realistic deployment scenarios where new prop- erties emerge after pretraining. Unlike baseline approaches, no additional pretraining or dataset construction is needed. As shown in Table 2 MolEditRL achieves the best performance across all unseen-properyy tasks, with the highest editing accuracy under both strict (r = 0.65) and relaxed (T 0.15) structural similarity constraints, while maintaining high validity and the lowest FCD. These results demonstrate that MolEditRI. can efficiently generalize to entirely new property objectives through task-specific reward oracleswithout retraining the model from scratch.
4.5 EVALUATION ON C-MUMOINSTRUCT DATASET
To further assess the generality of MolEditRL, we evaluate it on the publicly available C- MuMOInstruct dataset Dey el all (20258), a large instruction-tuning benchmark for controllable multi-property molecular optimization. Each task specifies which properties must increase de- crease to target thresholds while keeping others unchanged, requiring models to satisfy complex multi-objective constraints across 35 attributes simultaneously. We generate one edited molecule per instruction and measure chemical validity, total accuracy, and valid accuracy. As shown in Table EL MolEditRL. achieves competitive superior performance compared to the much larger
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GeLLM models. It obtains the highest validity in 8 of 10 tasks and outperforms both base- lines in 7 tasks in terms of total accuracy, with clear gains on challenging combinations such as urrl--hia-muteeeniiittyy-p-
and cere+drd2+erg. Although larger models show slight advantages on a few tasks, the differences remain small. These results demonstrate that MolEditRI. can match exceed the performance of models with over 500F more parameters (7B+ 125M), highlighting the effectiveness of our structure-aware diffusion and reinforcement learning framework for multi- property molecular optimization.
4.6 MULTTIPROPERTY EDITING PERFORMANCE
To assess model robustness under increasing task complexity, we evaluate performance molecular editing tasks involving 1, 2, or simultaneous property changes. For each setting, 10 combinations of editing objectives chemical validity, while (b) presents mean editing accuracy using TS similarity threshold of 0.15. As expected, accuracy drops for all models the number of target properties increases, reflecting the challenge of jointly satisfying multiple constraints while preserving molecular structure. Some mod- els maintain high validity but suffer from very low accuracy. indicating that generating chemically plausible molecules alone is insufficient for precise, property-aligned edits. MolEditRL consistently outperforms all baselines across all settings. average accuracy of 0.363, more than double the second-best baseline (DrugAssist, 0.165). These results demonstrate the effectiveness of our structure-awars diffusion framework and reinforcement learning fine-tuning in enabling scalable and precise instruction-baser molecular editing.
randomly sampled, and the results averaged. Figure B(a) shows mean
the most difficult 3-property scenario, it achieves an
Property Number
ropaao Number
1.000
0.973
0.900
0.901
0.975
0.294
0.393
0.307
0.356
0.748
1.000
0.979
0.95
0.265
0.560
0.2
1.000
0.971
0.904
0.908
0.974
0.091
0.165
0.097
0.099
0.363
BioT5
DrugAssist
Gellmo, M
Gellmo MolEditRL
0.90
BioT5
DrugAssist
Gellmo, M
Gellmo
MolEditAL
0.0
Model
Model
(a) Mean validity.
(b) Mean accuracy (Accat).
Figure 3: Performance by number of edited properties.
4.7
EFFECT OF FINE- TUNING STRATEGIES AND KL REGULARIZATION
1.00
0.8
Performance
0.75
MolEdItRL
Validity
0.50
GDPO wi GDPO
0.4
0.25
Valdta
DDPO
4c 65)
Accur0 1.)
0.2
0.00
Pretrain(50)
metrain(25)
Pretrain(10)
DIPO Wi KL.
GUPO wf sl
MotEdRoL w/e HL
MotEatio
0.0
200
400
600
800
Model
Fine-tuning Step
(a) Accuracy across models.
(b) Training stability.
Figure 4: Impact of step size, fine-tuning strategy, and KL regularization.
We conduct ablation studies on denoising step size, RL fine-tuning strategies, and KL regularization (Figure 2) Results are averaged over 20 single-property editing tasks. Subfigure (a) shows accuracy and validity; (b) shows training stability. "Pretrain(z)" denotes models without RL fine-tuning, where E (50, 25, 10} is the denoising step size. Smaller improves accuracy but increases computational cost. We use t, 50 as the policy update stride for efficiency. We compare two RL strategies: DDPO applies REINFORCE independently at each denoising step, while GDPO leverages Io tt-aarameterizttionn to optimize only the final output. DDPO performs joint fine-tuning across all tasks but suffers from instability since intermediate molecules GDPO improves stability but requires separate models for each task, limiting scalability. Neither method enforces structural constraints during fine-tuning. MolEditRI. introduces KI.-regularized
chemically meaningless.

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Published as a conference paper at ICLR 2026
LURO
10/1
110
TEAM
00
aad
Pereect
Degitandt
avro
Anotie
8.00
0.15
0.23
0.35
0.42
0.53
0.50
0.75
0.50
8.35
(a) Accuracy under increasing similarity thresholds. (b) Fréchet ChemNet Distance across models.
Figure 5: Performance comparison under structural constraints.
DIOR
HACCOFFT
HECROOR
PMCE
E-Q (30,070)
12,0000
lel 003
(-1 0009
[+0 00.000
DEO
NOIBONDS
1-2,000
(-02.155)
OED 101!
ROTRONEST
SAT
mm
V.Cm Ph
(a) Source
BioT5 (9/20 successful edits)
(c) DrugAssist (10/20 successful edits)
IADCOPT
IDOWORS
PO
110,000
112.000
12:000
13,000
ROTRONÓG
SAL
12220
(2334)
1-903500
143.00
685101
(d) GeLLMO_LA (11/20 successful edits)
(c) MolEditRL (13/20 successful edits)
Figure 6: Visualization of edits. Red highlights indicate structural changes from the source.
optimization over the entire diffusion process, enabling stable, structure-aware fine-tuning across diverse tasks. It consistently achieves higher accuracy and validity than both alternatives. Figure EX b) shows DDPO exhibits rapid validity degradation regardless of KL regularization, indicating inherent instability in step-wise optimization. GDPO shows improved stability with KI. regularization but plateaus below MolEditRLLss performance due to task-specific limitations.
4.8 STRUCTURE FIDELITY AND DISTRIBUTIONAL QUALITY
Figure 5(a) shows Accal across different Tanimoto similarity thresholds. MolEditRL consistently achieves the highest accuracy at thresholds, demonstrating its ability generate molecules that satisfy desired property changes while preserving structural similarity. In contrast, LLM--bssed baselines such as BioT'S and MolGen perform substantially worse, especially under stricter simi- larity constraints. Figure E(b) reports Fréchet ChemNet Distance (FCD) at fixed threshold of 0. Lower FCD indicates better alignment between the distributions of generated and real molecules.
4.9 QUALITATIVE ANALYSIS OF MOLECULAR EDITING
We visualize successful molecular modifications from four representative models across 20 single- property tasks using the same source molecule. As shown in Figure E. MolEditRL achieves the highest task success rate and is the only model that consistently preserves the core scaffold across all edits, demonstrating strong structural controllability.
CONCLUSION
We introduce MolEditRI, novel framework that integrates discrete graph diffusion with reinforce- ment learning to enable precise, stracture-preserviig molecular edits. It achieves state-of-thh-art performance on the MolEdit-Instruce benchmark while using significantly fewer parameters.
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ACKNOWLEDGMENTS
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62306255, 92370204, and 62406141), the National Key Research and Development Program of China (Grant No. 2023YFF0725000), the China Postdoctoral Science Foundation (Grant Nos. GZC20252740 and 2025M784335), the Guangdong Basic and Applied Basic Research Foundation (Grant No. 2024A1515011 and the Education Bureau of Guangzhou Municipality.
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APPENDIX
This appendix provides extended technical and experimental details that support the findings in the main paper. It is organized as follows:
(1) Appendix At Baseline methods (BioT5, DrugAssist, GeLLMO, MolGen, REINVENT MoleculeSTMD.
(2) Appendix B: Model architecture (RoBERTa, embeddings, diffusson modules).
(3) Appendix C Training setup (hyperparameters, optimization, hardware).
(4) Appendix D KL regularization weight ablation (3 values).
(5) Appendix E: Policy-update stride ablation (update frequency).
(6) Appendix B: Top-k sampling ablation (sampling diversity).
(7) Appendix & Partial-success reward ablation (reward values).
8 Appendix Ht Structure-aware attention ablation (graph-level constraints).
Appendix & Pretrain vs. RL fine-tuning comparison.
(10) Appendix D Chemical realism metrics RL baselines (synthesizability. liugelikeness).
(11) Appendix K: Prompt sensitivity analysis (instruction robustness).
(12) Appendix D
Complex localized editing (fine-grained operations).
(13) Appendix M:
Noisy oracle robustness (cstimation errors).
(14) Appendix N
Oracle-query efficiency (limited oracle budgets).
(15) Appendix Inference efficiency (denoising steps vs. runtime).
(16) Appendix B Computational efficiency (accuracy vs. cost).
(17) Appendix Q
Gradient derivation parmmmmerrrizaiinn)..
(18) Appendix R Dataset statistics (property ranges, prompts).
(19) Appendix S
Limitations and future work.
(20) Appendix II: LLM usage statement.
(21) Appendix Extended single-property results (10 tasks).
(22) Appendix V: Extended multi-property results (2-4 constraints).
(23) Appendix Y
Qualitative visualizations (structural modifications).
A BASELINES
(1) BioTS at (2023) leverages SELFIES and a T5-style architecture for cross-modal learn- ing between molecules and text. (2) DrugAssist Ye all (2025)
1lama2-7B-basse dialogue model for interactive molecule optimization. (3) GeLLM Dey ct all (2025B) uses instruction tun- ing on Mistral and Llama3 models for multi-property optimization; we evaluate both G M and G L. (4) MolGen |Fng et all (2024) is domain-agnostic language model trained with chemical feedback to reduce invalid generations. (5) REINVENT Loettler (2022) inte- grates reinforcement learning, transfer learning, and curriculum learning for molecular design using RNN and Transformer backbones.
(6) MoleculeSTM Luu et al (212)) is a multi-moda molecule structuretext model trained on large structuretext pairs enable zero-shot text- -uiddd retrieval and editing of molecules. (7) GeLLM Dey et al, (2024x)) is an instruction-tune tuned LLM on the C- MuMOInstruct dataset, built Mistral-7B and Llama3 models.
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B MODEL ARCHITECTURE
Table @ details the crructure-wware diffusion model used in MolEditRL. The architecture includes token and edge embeddings, RoBERTa-basee transformer with graph-aware attention, discrete diffusion module for masked demoising, and a reinforcement learning component guided by property- based rewards. During inference, we employ top-k sampling and apply policy updates at fixed stride intervals to improve efficiency. Table 4: Technical specifications of the structure-aware diffusion model in MolEditRL.
Module Types
Dimensions
Structures
Input Layer
Source Tokens [batch, seq_len] + Concal
Embedding
Vocab Size 51,933
TokenEmbedding [seq_len, 768] + PositionEmbedding
[scy_len, 768]
Edge Embedding
Edge Types 6
EdgeEmbedding [nodes, nodes, 768]
Input [batch, seq_len, len, 768]
RoBERTa
12 Layers
+ Sclf-Attention (12 CE 64) LayerNorm Residual
+ FFN (7683072768) - LayerNorm Residual
Forward: Input + Masked Tokens
Diffusion
2000 steps
Reverse (stride 50)
Denoising Network
seq_ len (E 51,933 nodes (E nodes
AtomLogits [batch, seq Leg_len, 51933]
Prediction
EdgeLogits [batch, nodes, nodes, 6)
Atom Categorical Sampling
Sampling
Top-k 15
Edge Structure Sampling
Reward Calculation (0, 0.2, 1.0)
Property-Guide Guided
Advantage Function Loss Weighting
C
TRAINING AND HYPERPARAMETER SETUP
We train MolEditRL on a multi-GPU cluster using PyTorch with Distributed Data Parallel (DDP). The model is initialized from RoBERRaa-baa encoder with 12 layers, 12 heads, and hidden size 768.
The tokenizer is extended to 51,933 tokens accommodate molecular and instruction-speet speciffc vo- cabulary, and the embedding layer is resized accordingly. For optimization, we use the Adam opti- mizer with learning rate of 5e-5, weight decay of 0.01, and linear warm-up scheduler over 10,000 steps. Mixed precision (FP16) is enabled to reduce memory usage and accelerate training. During pretraining, the model is trained for 100 epochs with a per-GPU batch size of 16. A discrete diffu- sion schedule with 2,000 denoising steps is used, following a mutual noise schedule 3 = 1/(T''1 t) where T = 2000. We apply word- and edge-level frequency weighting with sinusoidal modulation (A = 0.3) to guide denoising dynamics. The edge vocabulary includes 6 bond types. During re- inforcement learning fine-tuning, rewards are computed using property oracles (e.g., RDKit, TDC). key advantage of MolEditRL its remarkable oracle efficiency. The pretrained model achieves strong property optimization performance even without any oracle calls during inference, already outperforming most state-of-the-e baselines as demonstrated in our ablation studies. This effi- localized
ciency stems from our editing- based formulation, which performs diructure-constraigg modifications starting from known molecules. This approach drastically reduces the chemical search space and required oracle queries by orders of magnitude compared to de novo generation methods. Our empirical results confirm this efficiency under strict oracle budgets, where MolEditRL's perfor- mance quickly saturates with most improvements achieved within just 6,400 oracle queries during the standard 400-step fine-tuning protocol. We use top-k sampling with k: = 15 and a temperature of 1.0 during evaluation. To improve efficiency, the policy is updated every t. 50 steps, resulting in 40 updates over the 2000-step diffusion process. For consistency, inference also runs for 40 denois- ing steps, starting from a fully masked graph and progressively reconstructing the final molecule. All
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experiments were conducted single NVIDIA A6000 GPU using PyTorch and DGL. Pretraining on the MooEdiitInstrrue dataset (3M examples) took approximately 100 hours. RL fine-tuning for each task required 12 hours.
D ABLATION STUDY ON KL REGULARIZATION WEIGHT
Table D reports ablation study the KL-regularization weight 3, evaluating models trained with E {0, 0.1, 0.2, 0.3, 0.4, 0.5} under fixed fine-tuning budget of 500 steps. The results highlight the importance of balancing structural preservation and reward-driven optimization. When 0, the policy is no longer anchored to the pretrained diffusion prior, resulting in unstable behavior and overly aggressive edits that harm structural similarity. Conversely, larger B values impose exces- sive regularization, restricting the policys ability to improve the target property and increasing FCD. Across both LogP and SA tasks, moderate regularization consistently yields the best trade-off be- tween validity, cimiiartty-oostraainee accuracy, and distributional quality. Based on these trends, we adopt 3 0.1 the default setting in all experiments, it provides stable structure-preserving updates while enabling effective property optimization. Table 5: Ablation study on KL regularization weight 8
Task
β
Validity
Accan (TS20.65)
Accan (MCS20.6)
Accal (GED<4)
FCD4
LogP
0.0
0.986
0.278
0.376
0.186
9.142
0.1
0,976
0,462
0,498
0,214
7.812
0.2
0.920
0.416
0.458
0.196
9.762
0.3
0.884
0.386
0,414
0.206
9.649
0.4
0.840
0.350
0.402
0,210
10.812
0.5
0.842
0.356
0.380
0.194
10.700
SA
0,0
0.982
0.602
0.700
0.208
7.295
0.1
0.988
0.608
0.680
0.258
6.735
0.2
0.942
0.576
0.582
0.186
8.503
0.3
0,956
0.574
0,576
0.192
8.679
0.4
0.928
0.562
0.558
0.186
8.900
0.5
0.918
0.544
0.548
0.194
9.100
E ABLATION STUDY ON POLICY-UPDATE STRIDE
Table & presents sensitivity analysis of the policy-update stride, comparing stride values in [1, 2, 5) under an identical fine-tuning budget 500 steps. Fach table entry reports Validity / (TT > 0.65) / FCD, enabling joint assessment of chemical correctness, staccural-similaiityossttaa accuracy, and distributional fidelity. The results show that stride = consistently achieves the best performance across both LogP1 and SAJ tasks, providing the highest accuracy and lowest FCD throughout the training trajectory. Increasing the stride reduces the frequency policy updates, which slows optimization progress and leads to noticeable degradation in both property alignment and structural quality. These findings demonstrate that frequent policy updates are essential for stable and effective reinforcement learning in the discrete diffusion setting, and we therefore adopt stride = as the default configuration for all experiments.
F ABLATION STUDY ON TOP-K SAMPLING
Table / analyzes the effect of the top-k sampling parameter by evaluating settings from k to k= 25 under otherwise identical inference conditions. Across both the LogP and SA tasks, model perfor- mance remains largely stable, with only mild fluctuations in validity, dructural-similann accuracy, and FCD. Smaller top-k values restrict sampling diversity and slightly reduce structural flexi-
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Table Sensitivity analysis of policy-update stride
Task
Step
Stride=1
Stridc=2
Stride=3
Stride=4
Stride=5
LogP
99
0.9100.43368%
0.816/0.326/1 16164
0.802/0.3344 1.037
0,776/0.332/1...
0.7800029001 .597
199
0.933/0.442/8.
0.842/0.318/11 1...1....
0.834/0.358/
133400.358/1.4433
0.796/0.346/1
0.824/0.344/0..44
299
0.958/0.438/8.299
0.860/0.37/110.55
08866/0.66899.4
0.838/0.372/9.990
0.828/0.348/1 03330.338//0
399
0.956/0.456/7. 7.665
0.914/0.364/9.888 844
3862/0.356/10.44
0.860/0.358/
100.333810...3 10.736
499
0,976/0.46277.812
0.938/0.406/9
0,898/0.382/9.0 884
0.866/0.372/9..0
0.888/0.3666 10,348
99
0,984/0.58771
0.848/0.526/9
0.844/0.516/9
0.780/0430/11 1.379
0.788/0. 436/ 0.665
199
0.982/0.580/7.55
0.872/0.536/9
0.876/0.522/9.41
0.818/0.456/ 01/.800456/106999
0.834/0.4866 101.44.6/00445
299
1997.0.591/7.322
0.916/0.55688. 556/8.818 818
0.904/0.550/9.1
0.862/0.480/0..0 199
099600.58/8... 881
399
0.971/0.599/7.2 599/7.251
0.946/0.599 006.00599/8..0 333
00010/0.58/8..00 757
0.904/0504/9 925
0. 0.904/0 558/8. 636
499
0.988/.608866.35
0.956/0.592/8.200
0.952/0.512/9.737
0.924/0.30/9.55
0.920/0.5748 904
bility, while excessively large values introduce unnecessary stochasticity that may weaken property alignment. Overall, moderate top-k values achieve the best balance between diversity and reliabil- ity. Based on the observed trends, we adopt top-k = 15 as the default sampling configuration in all experiments.
Table Sensitivity analysis of top-k sampling parameter
Task
Top-k
Validity
Accan (TS20.655
Accau (MCS20.6)
Accan (GED<4)
FCD
LogP1 f
0.956
0.460
0,462
0.208
7.924
10
0.950
0.458
0.438
0.202
7.977
15
0.976
0.462
0.498
0.214
7.812
20
0.946
0.448
0.490
0.216
8.110
0.940 25
0.456
0.492
0.224
8.082
SA +
5
0.982
0.600
0.642
0.232
7.043
10
0.990
0,578
0,564
0,202
7.192
15
0.988
0.608
0.690
0.258
6,735
20
0.980
0.638
0.684
0,252
6.657
25
0.990
0.616
0.671
0.242
6.921
G ABLATION STUDY ON PARTIAL-SUCCESS REWARD
Table N presents an ablation study on the partial-success reward, evaluated using values in (0, 0.2, 0.4, 0.6, 0.8, 1.0). This reward is assigned to molecules that are chemically valid but fail to satisfy the editing objective, allowing the model to differentiate between invalid outputs and structurally plausible but suboptimal edits. The results show that removing this reward entirely (0.0) leads to unstable optimization and decreased validity, as the model receives no guidance for valid-but- incorrect molecules. Conversely, overly large partial-success rewards (2 0.6) diminish the incentive accuracy and higher FCD. Moderate values in the range 0.20.4 provide the most effective balance between stability, structural fidelity, and property optimization. Based on these observations, we adopt 0.2 as the default partial- success reward in all experiments.
to complete the desired edit, resulting lower smmilarit--consrrinee
H ABLATION STUDY ON VURUCTURE-AWANL ATTENTION MECHANISM
Table / presents an ablation study evaluating the structure-aware attention bias during both the pretraining and fine-tuning stages. Including the bias during pretraining yields modest gains in structural-similariy accuracy and FCD, reflecting its role as helpful but not dominant inductive prior when the model is learning general molecular distributions. However, the benefit becomes significantly more pronounced after reinforcement learning fine-tuning. Models equipped with the structure-awar bias during fine-tuning achieve substantially higher accuracy across all similarity- constrained metrics and markedly lower FCD for both LogP and SA tasks. These results indicate
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Table Sensitivity analysis of partial-success reward values
Task
Partial Reward
Validity
Accanl (TS20.65)
Accau (MCS20.6)
Accal (GEDS4)
FCD4
LogP1 f
0.0
0,952
0.484
0.508
0,202
7.906
0.2
0.976
0.462
0.498
0.214
7,812
0.4
0,962
0,464
0,528
0.218
7.859
0.6
0,958
0.480
0,504
0.202
8.234
0,8
0,964
0.434
0.468
0.208
8,890
1.0
0.964
0,402
0,438
0.216
9,458
SA +
0.0
0.976
0.600
0.634
0,234
7.110
0.2
0.988
0.608
0.680
0.258
6.735
0.4
0.984
0.626
0.656
0.220
7.142
0,6
0,980
0.542
0.582
0,208
7.773
0.8
0.984
0.590
0.594
0.212
7.998
1.0
0.984
0.530
0.556
0.196
8.530
that while structural bias provides useful guidance during pretraining, its primary impact emerges during RL optimization, where explicit graph-level constraints help the model perform chemically edits and avoid drifting away from realistic molecular structures. Table 9: Ablation Study on Structure-Awant Attention Mechanism Across Pretraining and Fine- tuning Stages
valid, ropology-preserving
Setting
Validity
Accaii (TS20.655
Accan (MCS20.6)
Acc,i (GED<4)
FCD,
LogP f
Pretrain w/o Structure Bias
0.744
0.176
0.208
0.182
13.714
Pretrain w/ Structure Bías
0.758
0.316
0.232
0.196
11,896
Finetune Structure Bias
0.890
0.212
0.266
0.213
12.486
Finetune w/ Structure Bias
0.976
0.462
0,498
0.218
7,812
Pretrain Structure
0.836
0.196
0.224
0.128
12.364
Pretrain w/ Structure Bias
0.842
0.213
0.256
0.195
10.522
Finetune w/o Structure Bias
0.904
0.412
0.468
0.176
8.452
Finelune w/ Structure Bias
0,988
0.608
0.680
0.258
6.735
I COMPARISON OF PRETRAINED DIFFUSION AND RL FINE-TUNED MODELS
Table I0 presents a detailed comparison between the pretrained diffusion model and the RL fine- tuned version of MolEditRI. across five representative editing tasks. The results show that the dif- fusion model alone already achieves strong structural fidelity, as reflected by the consistently high MACCS FFS, RDK FTS, and Morgan FTS scores, indicating that pretraining successfully learns stable and realistic structural prior. After RL fine-tuning, these structural similarity metrics remain largely unchanged, demonstrating that the KL-regularized optimization preserves the learned molec- ular topology instead of distorting it. In contrast, RL fine-tuning brings substantial improvements in validity and property-aligned accuracy, and consistently reduces FCD across all tasks, confirming that the edited molecular distribution becomes closer real molecules while isfying target properties. Overall, Table MD highlights the complementary nature of the two stages: diffusion pretraining establishes reliable structure-awart aware foundation, and RL fine-tuning delivers targeted property optimization without compromising structural integrity.
effectively
COMPARISON WITH RL-BASED METHODS ON CHEMICAL REALISM METRICS
Figure Ø provides a comprehensive evaluation of the distributional shifts in key physicochemical properties between the source molecules (Input) and the molecules optimized by MolEditRL (Out- put). The green shaded regions delincate the ideal ranges for drug-like compounds according to medicinal chemistry standards (e.g., Lipinski's Rule of Five). The results demonstrate that MolEd-
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Table 10: Performance comparison between pretrained diffusion model and RL fine-tuned model
Task
Validity
Ace, (TS20..5)
Acc,au(SS200655)
MACCS_FTS
RDK FTS
Morgan_
FCD,
QED1
Pretrain Finetune
0.567
0.684
10.518
0,974
0.604
0,620
0,834
0.735
0.667
7,678
Haccept
Pretruin
0,750
0.266
0.355
0,789
0,688
0.609
9,489
Finetune
0.968
0,484
0,500
0.798
0.691
0.623
7.316
LogPt
Pretruin
0.758
0.417
0.776
0.672
0.643
11.896
Finetune
0.964
0,578
0.599
0.795
0.682
0.620
7.012
DRD21
Pretrsin
0,850
0.220
0.259
0,796
0.698
0.637
11.194
Finetune
0.966
0.308
0.319
0.791
0.677
0.629
9.389
MW1
Pretrain 0.774
0.142
0,184
0.783
0,562
10.890
Finetune
0,960
0.404
0.421
0.805
0.673
0.576
6.588
itRL does not merely preserve the validity of the source molecules but actively optimizes their pharmacological quality. Specifically, the Quantitative Estimate of Drug-likeness (QED) shows a substantial improvement, with the mean value increasing from 0.44 to 0.59, shifting the distribution significantly into the highly desirable range (> 0.5). Similarly, the Synthetic Accessibility (SA) score decreases from 3.48 to 3.16, indicating that the generated molecules are chemically casier to synthesize. Furthermore, fundamental properties such Molecular Weight (MW) and LogP shift towards more favorable, central values within the ideal windows, avoiding the property drift often ob- served in generative models. Most notably, the compliance ratio with Lipinski's Rule of 5 improves dramatically from 52.7% in the source molecules to 84.7% in the output, underscoring MolEditRL's ability generate structures that not only target-optimized but also highly realistic, stable, and developable.
Stability and Synthesizability Analysis
SA Score (lowar is better) (Input: 3.48, Output: 3. 3.16)
QED (higher is better) (Input: 0.44, Output: 0.59)
Molecular Weight
LagP (Input: 3.68, Output: 2.94)
ado
[pput 453.44, Output: 362.77)
Score (lower is better)
8
QED (higher is belter)
0.8
7.5
Molocular Woight
600
5.0
400
g
2.5
0.4
0.0
200
2
0.2
-2.5
Input
Output
Input
Output
Input
Output
Input
Output
H-Bond Donors
H-Bond Acceptors
TPSA
Lipinski Rule of 5 Complience Innutt: 0.527, Output: 0.847)
(Input: 2.63, Output: 2.24)
20
(Input: 5.23, Cutput 4.63)
200
Input: 93.58, Output: 84.35)
1.0
12.5
0.8
H-Bond Donors Donors
H.Bond Accopiers
15
Compliance Ratio
10.0
TPSA
0.6
7.5
10
0.4
5.0
2.5
0.2
Input
Oulpul
Input
Output
Input
Oulput
Input
Output
Figure 7: Stability and Synthesizability Analysis. Comparison of property distributions between source molecules (Input) and MolEdidRL-gennratter outputs (Output). The green shaded areas rep- resent ideal value ranges for drug-like candidates.
Table I compares MolEditRI. with three representative RL-based molecular optimization frame- worksGCPN You et all (2018) MolDQN iZhou et all (2019) and REINVENT 4 Loeffler et all (2022)under strict oracle budget of 5,000 queries on the HIAf task, which fully held out from pre- training. Across all chemical realism metries, including synthesizability. drug-likeness, and Lipinski compliance, MolEditRL significantly outperforms existing RL methods and even improves beyond the source molecules themselves. While traditional RL approaches tend to suffer from distributional drift and generate chemically implausible structures when optimizing unseen properties, MolEditRL maintains high validity, low FCD, and superior realism due its KL-regularized objective. By
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choring policy updates to the pretrained diffusion prior, the model avoids degenerate exploration and consistently produces realistic, synthesizable, and pharnacologically relevant molecules. Table 11: Comparison with RL-based methods on chemical realism metrics
Method
Validity
Acean (TS20.65)
FCD4
Synthesizable
Is Druglike
Lipinski ROS
Source Molecule
1,000
0,278
0.376
0.527
GCPN
0.858
0.000
20.260
0.047
0.153
0.205
MolDQN
1.000
0.003
13.152
0.092
0.178
0.246
Reinvent4
0.835
0.124
13.786
0.278
0.370
0.428
MolEditRL
0.958
0.466
7.964
0.460
0.681
0.847
K
PROMPT SENSITIVITY ANALYSIS
Table TM evaluates the prompt robustness of MolEditRL on the SA task by testing five distinct para- phrased natural- language instructions. Although Table 19 presents only one representative template, the ColEdit-Instrus pretraining corpus contains many alternative linguistic formulations for each editing objective, exposing the model broad variability syntax, vocabulary, and semantic em- phasis. To explicitly assess the effect of such variation, we evaluate the following five prompts: (1) Pl: Reduce the synthetic accessibility easier to synthesize. (3) P3: Adjust the structure of SMILE to lower its synthetic complexity. (4) P4: Modify SMILE so that its overall synthetic accessibility score decreases. (5) P5: Transform the molecule SMILE into form that is simpler to assemble synthetically. The results show that MolEditRL sustains consistently high validity, structural similarity accuracy, and competitive FCD across all prompt variants, with only minor performance fluctuations. This indicates that the model does not rely on any specific phrasing pattern; instead, it benefits from the diverse paraphras- ing present during pretraining, enabling a prompt-invariant and semantically robust understanding of user instructions.
molecule SMILE. (2) P2: Make this molecule SMILE
Table 12: Prompt sensitivity analysis on SAT task with diverse instruction formulations
Prompt
Validity
(Cc,:(S))00.55)
Accan(GED<<4) AcC.n(C))20.6)
FCD4
Pl
0.986
0.636
0.696
0.244
7.107
P2
0,974
0.604
0,618
0.253
7.307
P3
0.979
0.652
0.636
0,224
7.157
P4 0.984
0.629
0.614
0.244
7.052
P5
0.980
0.646
0.642
0.256
7.339
L COMPLEX LOCALIZED EDITING INSTRUCTIONS
To assess the flexibility of MolEditRL in interpreting complex, localized natural- natural-language editing in- structions, we fine-tuned the pretrained model for 500 steps using five structurally explicit prompts. Unlike simple property-baae -based commands, these instructions specify concrete chemical operations such as functional group removal, fragment addition, and scaffold simplification. The five prompts used are: (1) Pl: Remove CO2H group from SMILE and decrease its H-bond donor character- istics. (2) P2: Add an additional amide fragment to SMILE to increase its molecular weight. (3) P3: Remove an aromatic ring from SMILE lower its structural complexity and improve synthetic accessibility, (4) P4: Reduce the synthetic accessibility of molecule SMILE. (5) P5: Eliminate CONH unit from SMILE to make the scaffold easier to assemble. Table I3 reports the results us- ing two key metrics: FG Editing Success, which evaluates whether the required functional group operation correctly executed, and Accan(TS > 0.65), which additionally requires structural sim- ilarity and successful property alignment. Across all prompts, MolEditRL consistently achieves higher validity, substantially better ceactional-grou editing accuracy, and more realistic molecular outputs than Reinvent4, while also maintaining strong synthesizability, frug-likeness, and Lipinski compliance. These findings demonstrate that MolEditRL can reliably interpret fine- tne-grained chem- ical instructions and execute highly localized editscapabilities that cannot be captured using scalar
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property targets alone. This highlights natural language powerful and expressive interface for precise, interpretable molecular manipulation. Table 13: Performance on complex localized editing instructions demonstrating natural language flexibility
Model
Validity
Edining Success
Asc,(1520.655
FCD
MACCS_FIS
K__Sythesizable
[t Druglikee
Lipinski RO5
0.376
Retimente
0.656
0.252
0.078
15.77
0,428
0.157
0.267
MulEdiiRI.
0.970
0.810
0.572
7.01
0.7W9
0.396
0.658
0.787
P2 Reinvenst
0,512
0.328
0.102
14,58
0.387
0.185
0.252
0.389
MotEdiiRL
0.968
0,798
0.360
8,98
0,785
0.332
0.574
0,884
0.358
0.505
Reiments
0,632
0.344)
0.126
12.86
0.379
0.104
0.185
0.288
MolEdiRLL.
0.984
0,872
0.370
7.93
0.763
0.392
0,591
0.839
P5
Reimeent MolEdiiR1.
0.642
0.234
0.128
12.32
0.365
0,160
0.179
0.204
0.978
0.752
0.310
0.327
0.503
M ROBUSTNESS TO NOISY ORACLE
Table I4 evaluates the robustness of MolEditRI. when the optimization oracle imperfect or noisya realistic scenario in molecular design where property predictors often contain estimation errors. To simulate such conditions, we inject controlled label noise by randomly flipping oracle outputs at varying rates from 0 to 0.2. Across both the LogP and SA tasks, MolEditRL maintains high valid- ity and stable editing accuracy, with only marginal fluctuations even under the highest noise level. Importantly, structural similarity metrics and FCD remain largely unaffected, indicating that the model continues to operate within a realistic chemical distribution despite corrupted reward signals. These results show that MolEditRL does not rely a perfect or deterministic oracle; instead, its KL-regularized optimization anchors policy updates to the pretrained diffusion prior, preventing overreaction noisy rewards and ensuring consistent, reliable editing behavior.
Table 14: Robustness Noisy Oracle Across Different Noise Levels
Task
Noise Level
Validity
Accan (TS20.65)
Accal (MCS20.6)
Accan (GED<4)
FCD]
LogP
0.0
0.976
0.462
0,498
0.214
7,812
0.05
0.920
0.457
0.508
0.220
7.529
0.1
0.944
0,460
0,514
0.219
7.582
0.15
0.924
0.454
0.488
0.216
7.925
0.2
0.914
0.448
0.472
0.212
8.215
SA
0.0
0.988
0.608
0.680
0.258
6.735
0.05
0,980
0.618
0.658
0,236
7.174
0.1
0.986
0.612
0.668
0.236
7.146
0.15
0.984
0.618
0.672
0.238
7.055
0.2
0.980
0.592
0.666
0.242
7.172
N ORACLE-QUERY EFFICIENCY COMPARISON
Table LS presents detailed comparison of MolEditRL with three classical RL-based molecular optimization frameworksGCPN, MolDQN, and REINVENT4under progressively increasing oracle- query budgets from 1,000 5,000. We evaluate the HIA property, which is entirely absent from pretraining, making it an ideal benchmark for understanding real-world oracle efficiency when op- timizing previously unseen molecular attributes. Across all query budgets, MolEditRL. consistently achieves higher editing accuracy, lower FCD, and competitive validity compared to baseline RL. methods. This superior efficiency stems from the strong structure-aware prior established during discrete diffusion pretraining, allowing MolEditRL to begin reinforcement learning from already realistic and structurally faithful distribution. In contrast, conventional RL approaches must rely on uninformed trial-and-error exploration, requiring large number of oracle interactions discover viable editing strategics. The consistent gains observed across all budgets highlight MolEditRLs ability perform effective, low-cost molecular editing even when oracle access is limited.
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Table 15: Oracle-query efficiency comparison on HIA task across different oracle budgets
Oracle Queries
Method
Validity
Accan (TS20.65)
Accan (MCS20.6)
Accan (GED54)
FCDJ
1000
GCPN
0.842
0.000
0,000
0.026
22.140
MolDQN
1.000
0.002
0.138
15,598
Reinvent4
0.598
0.092
0.148
14.682
MolEditRI.
0.864
0,388
0.408
0.202
9.686
2000
GCPN 0.882
0.000
0.000
0.022
21,175
MolDQN
1.000
0.000
0.122
0.026
14.857
Reinvent4
0.608
0.104
0.167
0.052
14.236
MolEditRI.
0.898
0.396
0.448
0.218
9.489
3000
GCPN 0.878
0.000
0.000
0.016
21,324
MoIDQN
1,000
0.002
0.156
0.036
14.828
Reinvent4
0.711
0.114
0.187
0.080
13.997
MolEditRL.
0.902
0.430
0.472
0.216
8.609
4000
GCPN 0.850
0.000
0.000
0.022
20.672
MoIDQN
1,000
0.002
0.156
0.032
13,362
Reinvent4
0.769
0.118
0.192
0.105
13.868
MolEditRL
0,928
0.448
0.488
0.214
8.387
5000
GCPN
0.858
0.000
0.000
0.029
20.260)
MoIDQN
1,000
0.003
0.142
0.045
13.152
Reinvent4
0.835
0.124
0.196
0.139
13.786
MolEdiRRL
0.958
0.466
0.490
7.964
O INFERENCE EFFICIENCY COMPARISON
Table [IG compares inference efficiency across baseline models and MolEditRL under varying skip- step configurations during the reverse diffusion process. Here, step refers the stride of the denois- ing trajectory: with a total of 2000 diffusion steps, larger stride values (e.g., step 500) correspond fewer denoising updates, while smaller stride values (e.g., step 50) yield finer-grained refine- ment with more update iterations. The results show that MolEditRL maintains strong accuracy and favorable distributional fidelity even under coarse schedules, achieving lower FCD than large L..M-based baselines while preserving fast per-sample inference time on an A6000 GPU. As the stride decreases, performance gradually improves with moderate increases in computation. Overall, MolEditRL demonstrates robust behavior under step skipping, delivering high-quality generations across a wide range of schedules. Based on the balance between accuracy and runtime, we adopt step 50 the default configuration in the main experiments.
Table 16: Inference efficiency comparison across baselines and different denoising step settings
Method
Validity
Acc_n(MCS20.6 Accn((S200655)
Accar(GED54)
FCDJ
Time/Sample (s)
BioT5
1.000
0.000
0.004
0.000
13,4000
0.7550
DrugAssist
0.988
0.202
9.0500
1,5221
Gellm'o-C_J L
0.849
0.218
0.224
0.104
10.6700
4.5600
MolEditRI. (stedii))(stee=500000
0.900
0.536
0.590
0.220
7,9273
1.0608
(slep=400)
0.924
0.568
0.628
0.240
7,5318
1,0684
(step=250)
0.952
0.588
0.644
0.234
7,3480
1,0954
(step=200)
0.956
0.576
0.638
0.234
7.4078
1.1135
(step=100)
0.972
0.668
0.232
7.1624
1,2039
(stcp=50)
0.988
0.608
0.680
0.258
6.7350
1.3857
(step=40)
0.979
0.624
0.682
0.234
6,5894
1,4778
(step=20)
0.988
0.628
0.686
0,246
6.4828
1.9286
(step=10)
0.984
0.636
0.690
0.238
6.2055
2,8393
P COMPUTATIONAL EFFICIENCY COMPARISON
Table 7 compares MolEditRL with a range of RL-based molecular editing approaches in terms
of both editing performance and computational efficiency the HIA task, which is fully
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cluded from pretraining. Under fixed 5,000-oracle budget, traditional RL methods such GCPN, MolDQN, and REINVENT4 fail to achieve meaningful accuracy due to their reliance on costly trial- and-error exploration. In contrast, MolEditRL benefits from its pretrained structure-awan diffusion prior, enabling effective fine-tuning with far fewer oracle queries and delivering substantially higher kimilariy-connsrrine accuracy and lower FCD. Although MolEditRI. ineurs one-time fine-tuning cost, this investment yields performance that surpasses both RL-from-scratch and large LLM- baselines, which either suffer from poor editing precision or require heavy inference computation. At inference time, MolEditRL requires no oracle calls and achieves 1.39 s/sample, supporting real- time interactive molecular editing. These results demonstrate that MolEditRL achieves an advanta- geous balance of accuracy, robustness, and computational efficiency compared to existing baselines. Table 17: Computational Efficiency and Editing Performance Comparison Across Baselines
Method
Validity
Accas (TS:).65)
Accai (MCS20.6)
Acca (GED:4)
FCDL
Finetune Time (min)
Inference Time (s/sumple)
0.858
0.800
0.000
0.029
20,260
32.92
0.78
MelDQN 1,000
01,03
0.142
0.045
13,152
35.76
4.73
Reinvent4
0.196
13,786
40.51
MolEdaRL
0.958
0.466
0.490
0.226
7,964
58.14
1.39
Q GRADIENT DERIVATION UNDER PAPPARAMMTTRRZAAAION
This appendix provides a complete derivation for the step the main text going from the policy- gradient term Ve log pe po Gigu S, Gare) to its so-parameterized form and the resulting reward- weighted cross-entropy objective.
Q.1 SETUP AND ASSUMPTIONS
We consider discrete diffusion setting molecular graphs. The forward process defines known
corruption distribution with an analytic posterior qG o(G-1 Gigs Gig)) Let the conditioning vari- ables be C' (S, Garc). Our modeling assumptions are standard in conditional diffusion: (i) the reverse-step factorization uses the forward posterior kernel, which depends only on (Gig Gig.) and is independent of and C; and (ii) the model predicts pa (G G C).
Q.2 THE PRECISE VERSION OF THE MAIN-TEXT EQ. (12)
By the law of total probability and the above conditional independence,
po (Goz
Gige:
C)=
Σ
Gign Gog) po (G G C.)
(15)
G
Equation equation I is an equality in the discrete diffusion setting. The common single- sample/MAP form that replaces the sum by Go then becomes an approximation.
Q.3
LOG-GRADIENT OF A MIXTURE: WEIGHTED EXPECTATION
Taking the logarithm of equation 15 and differentiating w.r.t. 0, using that does not depend on 0, yields the exact identity
V log po (G 1
G
C)
Wa
GG Glasl G C) Pologaa (GB G C),
(16)
G
with normalized weights
(Glyg Gt Gla) po Go G C)
wa G
(17)
Ea q( (Glz"
Ciyy G') po (G'
C C)
Thus, the exact gradient is posterior expectation of V, log GG Cl C)-
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Published as a conference paper at ICLR 2026
Q.4 PRACTICAL APPROXIMATIONS
Exact evaluation of equation IG is intractable due to the exponential number of graphs. Accord- ing to the Single-sample / straight-through (MAP) approximation, with K=1, or taking Gig arg mag (Gigi G C)) and ignoring (or stop-gradient on) normalization, we obtain the widely
used estimator
V9 log (
Gige C)
Ve lolog pe
(Gg
Gige 0)
(18)
which is the approximation used to move from the equation I2 to equation [S.. Direct REINFORCE
on log Pa (Gigi ( ()) has high variance due to (a) sparse rewards (only at t=0), (b) weak correlation
between intermediate noisy states and the terminal reward, and (c) accumulated stochasticity across
transitions. DDPO Black et all (2023) treats the denoising steps as an MDP and applies step-wise surrogates; GDPO Lm (2024h) adapts these ideas to discrete graph diffusion and proposes eager/low- varriance estimators. The single-sample 2o surrogate in equation IE is
practical varianceeccomputaiion trade-off also adopted in these works.
R DATASET STATISTICS
Our dataset is constructed following procedure similar DrugAssist et (2025),, involv- ing three main steps: (I) drug-like molecules are filtered from public databases such as ZINC and ChEMBL based on Lipinskis Rule of Five; (2) Matched Molecular Pairs (MMP) are extracted using BRICS fragmentation to identify structurally similar molecule pairs with local edits; and (3) pairs showing significant property shifts are retained, and corresponding natural language instructions are generated to describe the desired property modifications. To prevent data leakage, we construct the splits at the molecule level using canonical SMILES: duplicate or equivalent structures are removed, and no molecule in the test set appears in the training set in any RL fine-tuning inputs. This ensures that all test-time molecules are entirely unseen by the model. Table I8 reports descriptive statistics drawn directly from the MolEdit dataset. These ranges reflect the empirical distributions of property values in our collected molecule pairs. The physicochemical properties in our dataset carefully selected in accordance with Lipinski's Rule of Five, key of guidelines for drug- like molecules that includes constraints on molecular weight (< 500 Da). LogP (S 5), hydrogen bond donors (S 5), and hydrogen bond acceptors (S 10). These constraints are reflected in the value ranges of our dataset properties. The dataset covers both biological activity properties and physicochemical properties, each playing crucial roles in drug discovery:
R.1 BIOLOGICAL ACTIVITY PROPERTIES
DRD2 (Dopamine D2 receptor): A key target in antipsychotic drug development, with values ranging from to indicating binding probability. Our dataset captures substan- tial changes in DRD2 activity, from minor adjustments (++0.050) major shifts (+:0.951), where positive values indicate decreased binding and negative values indicate increased binding.
GSK333 (Glycogen synthase kinase-3 beta): An important target in treating neurological disorders, with values from 0 to representing inhibition probability. The dataset includes modifications ranging from +.0.50 to +00.500.
JNK3 (c-Jun N-terminal kinase 3): A target for neurodegenerative diseases, with values from 0 to I indicating inhibition probability. Property changes range from subtle (=0.030) to significant (+.06900).
R.2 PHYSICOCHEMICAL PROPERTIES
QED (Quantitative Estimate of likgnlikeness): Ranges from 0 1, where higher values indicate better diug-likeness. Our dataset covers modifications from +0.380 to #0.794.
SA (Synthetic Accessibility): Ranges from to 10, where lower values indicate easier synthesis. The dataset includes substantial changes from +0.700 +6.563.
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Table 18: Descriptive statistics of property changes in the MolEdit dataset.
Property
Direction
Pairs
A Range
Source Range
Target Range
80,627
[-0.951, -0.050]
[0.000, 0.944
[0.050, 1.000]
DRD2
+ 80,627
[0.050, 0.951]
[0.050, 1.000]
[0.000, 0.944]
t 98,310
[-0.750, -0.050]
[0.000, 0.940]
[0.052, 0.990]
GSK388
+
98,310
[0.050, 0.750]
[0.052, 0.990]
[0.000, 0.940]
f 94,131
[-0.690, -0.030]
[0.000, 0,880]
[0.040, 0.990]
JNK3
+
94,131
[0.030, 0.690]
[0.040, 0.990]
[0.000, 0.880]
f 97,750
[-0.794, -0.380]
[0.041, 0.564]
[0.438, 0.948]
QED
98,249
[0.380, 0.794]
[0.438, 0.948]
[0.050, 0.565]
f 90,192
16.563,-0.700
[1.059, 7.268]
[2.189, 7.999]
SA
4
87,453
[0.700, 6.104)
[2.277, 7.996]
[1.397, 7.268)
f
89,088
[-6.132, -6.122,-2.625)
[-17.073, 2.369]
[-13.745, 5.000]
LogP
4
90,489
[2.625, 6.132)
[-13.745, 5.000]
[-17.073, 2.372]
f 80,647
[-195,744, -99.031]
[218. 106, 399.216)
[336.084, 499.999]
MW
79,712 4
[99.031, 195.744]
[336.073, 499.99 499.994]
1218.094, 400.241]
f
98,562
[-7.000, -2.000]
[0.000, 8.000]
[2.000, 10,000]
HAccept
4
98,562
[2.000, 7.000]
[2.000, 10.000]
[0.000, 8 8.000]
f
104,468
[000,-2.000]
[0.000, 3.000]
[2.000, 5.000]
HDonors
4
104,468
[2.000, 5.000]
[2.000, 5.000]
[0.000, 3.000]
1
66,369
[-9.000, -3.000)
[0.000, 7.000]
[3.000, 10.000] [0.000, 7.000]
RotBonds
65,806
[3.000, 9,000]
[3.000, 10.000]
MW (Molecular Weight): A fundamental property ranging from 218 to 500 Da in our dataset, with modifications spanning 199.031 to 1995.744 Da.
LogP (Octanol-water partition coefficient): Measures lipophilicity, ranging from - 117 to in dataset, with changes from +2.625 16.13
HDONORS (Hydrogen Bond Donors): Ranges from 0 to 5, with modifications of #2 to
+5 donors.
HACCEPT (Hydrogen Bond Acceptors): Ranges from 0 to 10, with changes of +22 + acceptors.
ROTBONDS (Rotatable Bonds): Ranges from 0 to 10, with modifications of +3 to 19 bonds, affecting molecular flexibility.
For each property, table shows the number of molecular pairs, range of property changes (A Range), and the value distributions in both source and target molecules. The + notation indicates that changes occur both directions positive values for property reduction and negative values for property increase, representing the observed range of property modifications across all molecule pairs in the dataset.
R.3 NATURAL LANGUAGE PROMPTS
Table Iy presents the natural language prompts designed for our single property editing tasks. For each of the ten molecular properties, we crafted two complementary prompts corresponding to property value increase and decrease. The prompts are purposefully designed to be clear and con- cise while maintaining chemical accuracy and relevance. For biological activity properties (DRD2, GSK33, JNK3), the prompts emphasize binding affinity and inhibitory activity. For physicochem- ical properties, the prompts use specific chemical terminology (e.g., "hydrogen bond acceptors," "rotatable bonds") while remaining accessible. Some prompts, such as those for LogP, include additional context about the property's practical implications (e.g.. "enhance its fat solubility" or "improve its water solubility"). Each prompt contains a [SMILE] placeholder that is replaced with the actual SMILES EES string of the molecule to be modified during the editing process.
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Table 19: Natural language prompts for single property editing tasks.
Property
Direction
Prompt
Optimize this molecule [SMILE] to increase its DRD2 binding affinity.
DRD2
Help me reduce the DRD2 binding activity of molecule [SMIL.F].
Help me optimize this molecule [SMILE] to improve its GSK388 inhibitory activity.
GSK3,8
Reduce the GSK35 3 inhibition potential of this molecule [SMILE).
Enhance the JNK3 binding properties of molecule [SMILE).
JNK3
Make changes lower the JNK3 binding affinity of molecule [SMILE).
Optimize the QED score of molecule [SMILE] to make more drug- like.
QED
Decrease the QED value of this molecule (SMILE).
Make this molecule [SMILE] harder to synthesize. Make this molecule [SMILE] easier to synthesize.
SA
Help me increase LogP value of molecule [SMILE) to enhance its solubility. Help me decrease the LogP value molecule [SMILE] to improve water solubility.
LogP
Help me increase the molecular weight of this molecule (SMILE).
MW
Help me reduce the molecular weight of this molecule [SMILE].
more hydrogen bond acceptors to this molecule (SMILE).
HAccept
Reduce the number of hydrogen bond acceptors in molecule [SMILE].
HDonors
I
Help me increase the number of H-bond donors in [SMILE).
Help me decrease the H-bond donor count in this molecule (SMILE).
Add more rotatable bonds this molecule [SMILE].
RotBonds
Reduce the number of rotatable bonds molecule (SMILE).
S LIMITATIONS AND FUTURE WORK
MolEditRL demonstrates strong and consistent performance in structure-preseeriin editing across wide range of chemical properties, particularly on small medium-sizee molecules. While our current experiments focus on this regime, the underlying framework is designed to generalize and is expected to extend effectively to larger biomolecules, such as proteins or complex natural products, with minor adaptations. The reinforcement learning component leverages property oracles (e.g.. from RDKit and TDC) to guide optimization. These oracles validate MolEditRLs effectiveness on widely studied molecular properties. For ces-cchrrcterrzzed emerging attributes, task-specific pre- dictors can be trained and integrated, enabling flexible extension of the framework to new property domains.
However, in particular, the framework may struggle when a target property is extremely rare, lacks reliable predictive model, or requires prohibitively expensive evaluations. In addition, when user instructions contain logical contradictionssuch as requesting simultaneous improvement of mutually exclusive propertiesthe model is unable produce feasible edits. Looking ahead, plan plore interactive, callogue-basedd molecular editing, enabling users to iteratively refine molecules via multi-turn natural language instructions. This direction could support more intuitive and human- centric workflows for molecular design and lead optimization.
T LLM USAGE STATEMENT
Large language models were employed solely as general-purpose assistance tools during the writing process, specifically for improving clarity and checking grammar. All technical contributions, ex- perimental results, and scientific insights entirely the authors work. No LLMs were used to generate core research ideas, experimental data, or technical implementations. The authors take full responsibility for all content and claims presented in this paper.
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Table 20: Extended results on single-pre propeety molecular editing tasks, Bold indicates best perfor-
mance. Arrows (1. 4) denote desired property increase or decrease.
Modd
Task
Validiy
Ausua (0.65)
Ascuga (0.65)
(0.15)
Auc
Aocegu (0.15)
FCD
Tick
Validity
Ausu (0.65)
Antrali
(0.65)
Aucul
(0.15)
Accolii
(0.15)
FCD
VEINVENT4
0,534
0.1
0.3635
0.4
0.7633
11.0565
0,704
0.10
0.3599
0.442
0.0.7
11 11,2456
MulClen
1,0
0,022
0.022
0.256
0.256
14,6826
1.0
0.004
0.004
0.434
0.044
14.943
BioTS
1,0
0.0
0.0
0,148
0.148
50,3159
1.0
01.0
0.0
0.472
0,722
15.1916
DragAssis
HACCEFTT
0,9439
0.3467
0,3673
0.4429
0.4692
8,7009
HACCEPT
0,9819
0.161
0.1639
0.3421
0,5484
11,9052
Gellmo_ M
0,904
0.064
0.07U8
0.15
0,1659
14,259
0.89
0.298
0,5348
0.524
0,5888
9.21335
Gellmo
0.59
0.57
0,0757
0.162
0.182
12.7839
0.914
0.178
0.1947
0.508
0.5558
833993
MoILdiRL
0.968
0.484
0.5
0,826
0,8533
7,3163
0.914
0.388
0.3984
0.712
0,731
9
REIN VENT4
0.563
0.28
E,718
0338
0,7641
9,356
0L251
0.3541
0.3001
0635
0.836
1.79%
MulCien
1,0
0.028
0.088
0.418
0.18
9.8619
1.0
0.016
0.016
0.432
0,32
14.0007
BioT5
1.0
0.0
0.0
036
0.36
16,5087
1.0
0.0
0.0
0.348
0,348
17.2548
DragAssiss
SAT
0.988
0,216
0.2186
0.29
0,2976
10.34
MWW
0.94
0.5391
0.5501
0.5822
0,592
9.2859
Gellmo. M
0,91
0.12
0,1319
0,388
0,3165
8.319
0.898
0.29
0.3229
0.554
0.0281
6.6778
Gellmo L
0,912
0.104
0.114
022
0,207
8 69652
0,906
0.26
0.287
0.644
0,355
6,6955
MelEddiRL
0.95
0.49
0,5188
6.776
0,8168
7.5351
0,984
0.632
0.6423
0,952
0,9775
6,3938
REISVENT4
0.078
0.28
0,3933
11.739
0.256
0.4085
0.13
0,597
9
MolCien
1,0
0,022
DX22
0,243
0.245
12,4729
1.0
0,042
0,042
0.418
0,418
11.0476
BioT5
1,0
0.0
0.0
0,144
0.144
23.7964
1,0
0.0
0.0
0.272
0.272
16,231
DrugAssiet
IDONORS
0.9319
0.3367
0,3506
0:149
0,6774
8-005
DRD2
0,964
0.524
0,5325
0.57
0,5993
5.9955
Gellmo M
0.898
0.046
B.09
0,002
0,1034
12,5726
0,922
0.136
0.1475
0.24
029922
3382
Gellmo I.
0.806
D:04
0.046
0.1
0,1116
14 157508
0.916
0.132
0.144
0.366
O 3666
8866
MelFaiiRI.
0.942
0,582
8
0,842
0,8838
8.114
0.986
0.656
0.6639
0,72
0,7M12
6,549
VINT4
0.51
0:14
0,189
0.3901
13:999
0,506
0.258
0.5276
0.221
03336
1.5522
MelGinn
1.0
0.064
D.044
0.271
10.5549
1.0
0.11
0.11
0.071
0,114
13.7158
BioTS
1.0
0.
0.0
0,002
11#92
27 1155644
1.0
0,0
0.0
0200
02302
54 7727
LOGP
0,964
0.382
0.3K33
0.442
0-4865
11 1182
L.GPI
0.966
0.548
0,673
0601
00633
1200
Gellmss M
0.89
0.354
D44222
0,724
0.58.5
6.878
0.906
0.004
0.0044
0224
022322
12,9822
Gellmw
0.91
0.268
0.7945
0.50
0.6464
6 6,7566
0.918
0.15
0.1634
01444
7.7802
MolLddRLL
0,964
0,578
0.5996
0.91
0.944
60118
0,972
0,71
0,7305
6.94
0.9671
S.1015
REISVENTT
0.51
0.112
0,1855
0.354
0,2295
11.871
00652
0:15
0.2301
0.312
0:2185
11 1111
MelCCen
1.0
0.084
0.064
0.356
0.355
11 11,4428
1.0
0.024
0.024
0.421
0.411
10,996
RiGTS
I.
0.0
0.306
D.066
16.85
1.6
0.0
0.0
0.74
0.124
15.725
DrsgAssiet
ROTRONDS!
0,9537
0.1469
0.154
0.2716
0.2245
10,7588
QEDL
0,989
0.1444
0,1099
0127
022055
108724
Gellmo. M
0,838
0.072
0,6811
0,16
0,1802
12,100 1089
0.924
0.012
0.015
0.15
0,1623
15. 4161
Gellmo L
0,835
0.088
0.110
0.218
0,3455
10. 10,0584
0,904
0.088
0,0973
0.218
0.2412
10.3736
MolEdiiRI.
0,914
0.392
0.4197
0.764
0,818
7.2832
0.948
0.612
0.6456
0.894
0,943
6.9314
U EXTENDED SINGLE-PROPERTY RESULTS
Table 20 reports extended quantitative results for 10 represeentative single-property molecular editing tasks from the MolEdit-Instruet benchmark. MolEditRL consistently achieves the highest accuracy across both similarity thresholds, while maintaining high chemical validity and the lowest FCD scores across most tasks. This indicates strong structural fidelity and superior alignment with tar- get property distributions. In contrast, baselines such as BioT5 and MolGen often generate valid molecules but fail to satisfy property and similarity constraints. REINVENT4 and DrugAssist per- form moderately well but fall short in structural preservation and distributional realism. These detailed results further confirm the robustness and effectiveness of MolEditRL in single-property editing scenarios.
V EXTENDED MULTI- RESULTS
Table 2 presents detailed evaluation results on multi-property molecular editing tasks from the MolEdit-Instruet benchmark. Each task involves optimizing 2 to 4 chemical properties simulta- neously, reflecting practical constraints encountered in real-world molecular design. MolEditRL consistently achieves strong performance across all multi-property tasks, demonstrating its ability to balance complex property requirements while preserving molecular validity and structural similar- ity. The results confirm its robustness under increasingly constrained and realistic editing scenarios. The property combinations in these tasks are carefully selected to reflect common design goals in medicinal chemistry. For example, tasks like (HACCEPT), HDONORS() aim to reduce molecu- lar polarity, which is essential for improving membrane permeability and bioavailability. (LOGP), ROTBONDSL) targets molecules with lower lipophilicity and rigidity, which improves metabolic stability and reduces off-target binding. On the other hand, combinations such as (MW1, QEDL) simulate etrly-stage exploration of larger, less drug-like molecules, often relevant in hit expansion or macrocycle design. Biologically motivated combinations like (DRD2L. GSK331) reflect efforts to reduce off-target dopamine receptor activity while enhancing GSK33 inhibition, a common chal- lenge in polypharmacology. Furthermore, high-complexity tasks such as (GSK381, HDONORS,,
28

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Published as a conference paper at ICLR 2026
Table 21: Extended results on multi- poopiitrroerty molecular editing tasks. Bold indicates best perfor-
mance. Arrows (1. 4) denote desired property increase or decrease.
Mockl
Tiak
Valility
Aucal (0.65)
Ausuaa (0.65)
(0.15)
Adegi
Ackral (0.15)
FCD
Tisk
Validity
Aucua (0,65)
(0.65)
(0.15)
Accul
Acsyala (0.15)
FCD
BIDT5
1B
0.3
030
0.352
0,352
17.751
1.0
025
0.0
0.19
0.19
19,3292
GALLMTO,N M
GeLLM"O_1
DragAsent
HACCEPTL HDONORS]
0,9819
0.89
0.9
0,2711
0.108
0.146
0.2761
0.1213
0.1622
0.3514
0.264
0.36
0,2966
0,364
0.4
12.0622
14,7575
12.967
JNKI
QED
0.914
1.98
0.9
0.148
0.292
0.098
0.1619
0.1089
0.298
0.336
0.352
0.326
0.1567
0.3911
0,3429
11.1755
10.123
10,866
MolEdRRL
0,972
0.35M
0,373
0,12
0.6497
11,7393
0.976
0,33
0,3581
0.166
0.362
9,6139
BID75
10
0.3
030
02288
0,995
24.7513
1.0
020
035
0115
07383
24.15
GALLMTO,M M
GaLEM"O_L
DragAsent
HACCEPT?
SAT
0.915
0.951
0.964
0.226
0.012
0.026
0
001111
0,2309
0.284
0,03
007
0.2977
0.0531
0,01
23.0712
14.5785
11,5-24
GSK381
DRIZ
0.992
0.942
0.9
0.104
0.136
0.05
0.0356
0,0322
0.1018
0.044
0.126
0,008
0,679
0.127
156116
12.7831
12.3998
MolEdRRL
0,962
0.316
0,3583
0.58
0.6876
11,2492
0.97
0.186
0,1918
0.238
0,351
114435
BIo75
10
0.5
030
0.101
0,104
27.551
1.0
075
035
0.25
125
28,5503
GALLMOO_M
GeLLMTO_L
DragAsent
ROTRONDS]
LOGPL
0,905
0.892
0.88
0.032
0.346
0.09
0
0.635
0.2991
0.384
0.125
0.3
0.3304
0,1435
0
10.9156
16.9039
843341
DRIZ
SAT
0.766
0.924
0.398
0,212
0.102
0.07
0.1136
02172
0.075%
0.33
0.22
0.54
0243
02584
0260
12.0196
11.5436
114432
MolEdRRL
0.7
0.454
0.468
0,686
0,7072
6.2095
0.912
0.23
0,2522
0.398
04364
10,834
BID75
10
0.3
000
0,002
31.853
1.0
050
0.5
0216
0.115
19.1027
GALLMOO_M
GeLLMTO_L
DragAssnt
ROTRONDS1
LOGPL
0,905
0.66
0.66
0.022
0.014
0.09
0.063
0
0.0243
0,034
0.66
0.1
0,1042
0.0662
0,0395
30.3704
16,6925
19.104
QED ROTRONDS
0.994
0.375
0,902
0.014
0.064
0.24
0.0199
02439
0.011
0.216
0.166
0.996
0,2055
03109
0.364
21 21.1825
11 11.7206
11/2221
MolEdRRL
0.954
0.344
0,3891
0,634
0,7172
12,8673
0.943
0.422
0,4742
688
0,336
7.564
Bio75
10
0.3
U2
022
17.3549
1.0
030
0.5
0119
0.39%
20.70
GALLMOO_M
GaLLMOOLL
DragAssit
QEDI
MWT
0:926
0.982
0.88
0.298
0.158
0.072
0
0,1791
0,3041
0.354
0.238
0.316
0.3612
0.35X3
6.157
12.5711
9:5455
7.545
OED.
KAT
0.394
0.78
0,006
0.2525
0.086
0.076
0.0622
0.009)
01337
0.3766
0.184
0112
02051
10.3 1535566
10.30 18.3846
11 101122
MolEdRRL
0,944
0.35
0.4477
0.79
0.936
7.0482
0.338
0,592
0,3111
0377
0.336
7,3852
Bio75
10
0.3
000
0/015
0,06
29 99.9934
1.0
050
0.5
XUT
36 1650K5
DrigAssit
DR021
0.95
0.052
0.0653
0,082
0,08C
14,0958
DRDS HACCEPTY
0.956
0.142
0.1455
0192
02008
13.9499
GALLMOO_M
RACCEPT
0.91
0.018
0.0188
0,344
0.074
16.9513
0.984
0,004
0.044
0054
13.5396
GALLMOOLL
MWL
0.915
0.01
0,0109
D02
0,218
34.1756
SAT
D.92
0.016
0.174
02313
18.25 852579
MolEdRRL
0.962
6.1
01104
0,264
0,3744
12,5672
0
0.192
0,1988
0.288
02981
13.761
HAYS
18
0.5
6.0
01886
6366
383991
1.8
0.
6.5
8T16
2570012
G.LLMOO.M
DrigAseit
HACCEPT
DR02
0,972
0.904
0.03
0.09
0
0,3322
0,022
014
0.090
0,344
13.117
15.8564
DRDE
INK3
0.99
0.91
0.062
0.114
0.081
0.1152
0..58
0.13
01455
15.618
13.438
GaLLMOOLL
JNK3
0.11
0.036
0,0256
0,011
18.5388
QED.
0,112
0.134
0,333
(3889
18.1148
MolEJRRL
0.9
0.23
0.334
0,24
0,3138
13.1973
0,938
0.258
0,2751
0,438
0,467
9,2013
GALLMOO.M
GALLMPOLL
MoIEJRRL
DrigAseit
HAYS
HDONORST
GSK3B1
OEDI
SA
0.945
0.902
0,914
0,954
18
0.032
0.012
0.266
0.056
0.3
0
0,0591
0,119
0,0022
6.0
0,044
0,466
0,022
0,036
0.0041
0,054
0.0394
8,4361
6366
21.3656
18.4036
14.599
HDONORS!
GSK381
LOGPL
DRD2.
0.544
0.884
0
0.962
1.0
0.002
0.174
0
0.0
0.0
0/0022
0,1909
0.021
6.0
6.0
0002
D.008
0.006
0034
0.232
0.089
00063
0,2412
0002
454139
114478
19.1179
19,9507
60.738
GLLLMOO_M
GeLLM"O
MolEJRRL
DragAscid
HACCEPT,
GSK3B
DRD2
SA.
0:988
0:915
0.965
0.91
18
0.042
0.082
0.042
0.122
0.3
0.0858
0.1237
0,062
0083
6.0
0/84
0,922
0.094
0.21
0,051
0,1053
0,1307
6.68
0,313
20:2584
18.1187
21.3259
14.735
HDONORS,
GSK3R
LOGPT
MW.
0.992
0
0.966
0.91
188
0.014
0.146
0.05
0.06
6.5
0.0344
0.907
0.0522
0,1511
0.3
0.108
0.098
0.212
6.1
0.1192
0.0983
0.2195
011
20.1217
24.7748
276363
18/0029
18,0908
QEDJ, SA1) require optimizing target activity while managing solubility, drug-likeness, and syn-
thetic complexitymiroring real trade-off's in lead optimization pipelines. These results collectively showcase MolEditRLs effectiveness not only in individual property edits but also in realistic, multi- objective optimization scenarios critical for practical drug development.
W ETHICS STATEMENT
This work focuses on computational molecular editing for drug discovery applications. We acknowl- edge several ethical considerations: (1) Dataset Release: We release the MolEdit-Instruct dataset publicly to benefit the research community, following established practices for molecular datasets. All molecular data is derived from publicly available databases (ZINC, ChEMBL) and contains no proprietary or sensitive information. (2) Intended Applications: Our method is designed to support legitimate drug discovery research, and the dataset is intended for beneficial applications in medicine and chemistry. (3) Reproducibility: We provide comprehensive implementation details, hyperparam- eters, and dataset construction procedures to ensure reproducible research. (4) No human subjects were involved in this study, and all experiments were conducted on computational datasets.
X REPRODUCIBILITY STATEMENT
To ensure reproducibility of our results, we provide comprehensive details across multiple sections: (I) Model Architecture: Complete architectural specifications are provided in Appendix, including all hyperparameters, network dimensions, and training configurations. (2) Training Setup: Detailed training procedures, optimization settings, and hardware specifications are documented in Appendix. (3) Dataset Construction: The MolEdit- -olEtritInsttuuc dataset construction process is thoroughly described in Appendix, including property definitions, filtering criteria, and prompt generation procedures. The dataset is publicly available on Hugging Face. (4) Experimental Details: All evaluation metrics,
29

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Published as a conference paper at ICLR 2026
baseline implementations, and experimental protocols specified in Section 4. (5) Code Availabil- ity: Upon acceptance, we will release the complete implementation including model code, training scripts, and evaluation pipelines to facilitate reproduction of all reported results.
Y MORE VISUALIZATION OF MOLECULAR EDITING
To further illustrate the editing behavior of different models, we present additional qualitative re- sults in Figure &. Figure gl and Figure M. These figures show visualization of edits across 20 single-property tasks. For each task, subfigure (a) displays the source molecule, and subfigures (be) show successful edits produced by BioT5, DrugAssist, GeLLMO_L, and MolEditRL, respectively. Red-colored substructures indicate regions that have been modified relative to the source molecule. Across all tasks, MolEditRL consistently achieves the highest number of successful edits, well as the best structural fidelityrreserving the core scaffold of the original molecule while precisely introducing the required modifications. Additionally, Figure I2, Figure IRl and Figure [4] high- light side-by-side visual comparisons of different models editing the same molecular structure for a single target property. These visualizations confirm that only MolEditRL can reliably perform property-aligned edits while preserving molecular similarity. Competing models often over-modify or disrupt key structural elements, leading to reduced similarity or invalid transformations.
30

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Published as a conference paper at ICLR 2026
BR02
65C38 (+2.010)
HDONDRS (72.0001
LOSR
[QL001]
(-2000)
(-2 22.000
(-001))
(-2574)
HMT 1
DED
54.1
SA
(-29.91)
(-0.000)
ROTBONDS
(-1.00)
(404355
(a) Source
(b) BioT5 (8/20 successful edits)
(c) DrugAssist (9/20 successful edits)
DRD3 (0,001)
GSEIB [-0.40]
NACCEPT (+2.000)
HACCEPT (-20000
HEDDOORS (+2 2000
MOONORR 0000
POCS 60.01.0
LOGP [-2,57]]
CED
63,900
(+109.902)
(-9.9930
(3:39)
(+5,000)
(1.000)
1-3,4701
(4,4355
Goog
(d) GeLLMO L (6/20
successful edits)
MolEditRL (16/20 successful edits)
DAnO (+0,020)
GOESS
RECCEST [-1000]
HDONOES (3.000)
LOGR (441130)
[+0.00]]
OSKERT
HOONOWS
INC3
LOGPT
ROTRORDS 1-6.000
(O.141
(0,957)
(421))
Source
(g) BioT5 (9/20 successful edits)
(h) Assis (9/20 successful edits)
Caona
13
VNCCEFE
POTRONDS
561
1116-000
(i) GeILMO_I. (9/20 successful
its)
(i) MolEditRL (15/20 successful edits)
DRIZ (0.004)
GOICES (10.130)
HIONNORS (.00000]
JNK3 (-0011))
LOGP 1-13
3RD2 (10,002)
HACCEPT (22000)
HOOWORS
LOGP
(2.000)
(+2574)
MNN' (+31,00))
OED (0.7291
ROTBONDS
SA (+1.300
1+23.0001
(-9.9913
(40.129)
(-1,00))
(0.414)
(k) Source
(l) BioT5 (9/20 successful edits)
(m) DrugAssist (8/20 successful edits)
CROX
HACCAPT
HDONORS (910000
NOONORS 1.0000
LOGHT
COE3
GOESE
RACCEPT
DONORS
DONDIS
LOCK
LOCE
(+33.001
(30,05
(+0.533)
13.0001
(-02900
(+1249141
(erar)
(n) GeLLMO_L (10/20 successful edits)
(o) MolEditRL (14/20 successful edits)
Figure 8: More visualization of edits on 20 tasks.
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Published as a conference paper at ICLR 2026
(9090)
(10 200
FE 10400
CHARIE
(12089)
(13009
(1933)
(13003)
CLE3D
LOOP
RCTEONDS
(9222
(a) Source
(b) BioTS (10/20 successful edits)
(c) DrugAssist (12/20 successful edits)
ORI2
COK35
HACCEFT
DRO2
HOONORS OC0)
[+0.075)
(+3.000)
(-2.000)
(1300000
[+0,0201
J931
LOSP
54.1
(+2.987)
(2.772)
[+136,070)
(113.997)
(A.54)
(+300000
G-T (ET000
[-1,37]]
(d) GeLLMO (6/20 suc- cessful edits)
(c) MolEditRL (16/20 successful edits)
1120001
11,000
1-0000
09.151
1476:000
1461220
(f) Source
(g) BioT5 (11/20 successful edits)
(h) DrugAssist (12/20 successful edits)
DROO
HACCEPT
HOONORS
LOOP
HACERPTT
10.001
CH0.2301
3301
FOTH 1615
12,000
M18377
(2000
(i GeLLMO_L (9/20 successful edits)
ii) MolEditRL (14/20 successful edits)
ORCCEPT
MO
LOCP
KOTEORDSA
SAT
(50000)
1650.000
OL 900
(k) Source
(l) BioT5 (12/20 successful edits)
(m) DrugAssist (12/20 successful edits)
(0.001)
10.0401
1.00001
(124034
(H0.004
HACCOFT
HOUCEFT
110.000
1124000
LOUPT
MU
OCD
SA Cecan)
(404
(+0.200)
-OH
(2.044)
1+112214
1+3807
(ASM
1+30000
142200
(n) GeLLMO_L (7/20 successful ed-
its)
(o) MolEdiRL (16/20 successful edits)
Figure 9: More visualization of edits on 20 tasks.
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Published as a conference paper at ICLR 2026
HACCEPT
(+0.030)
(+2,000)
(+1.000)
(+0.011)
(+2.000)
(+0.0500
(.00100)
ROTBONDS (42.000)
ROTBONDS (-1.000)
(-56.040)
(-0.2611
(+1.00)
(42.678)
(-0,889)
P
(a) Source
(b) BioT5 (6/20 successful edits)
(c) DrugAssist (9/20 successful edits)
[-0.208)
(0,004)
(40,000)
(+2.000)
(2,000)
[+2.000]
(40.020)
PAOCUTT
HOONDRSY
192,5331
(QBSS)
(+218.005)
1924411
(4.3511
1+5,0001
2490
(d) GeL.LMO_L L (5/20 successful ed- its)
(e) MolEditRL (14/20 successful edits)
14E3H
12 900
adnlax
DOP
MAT
0401
d
(f) Source
(g) BioT5 (11/20 successful edits)
(h) DrugAssist (12/20 successful edits)
LEEECI
10.0001
112.0001
0000
1+ 1000
LOOKT
NOTRONES
ROTSONES
ROTEONOS
Crew
(i) GeLLMO_L (7/20 successful edits)
(j) MolEditRL (16/20 successful edits)
DIOZ (-25-991)
BACCEPT
HOONGES
HODONORS
JOCP
(+0.018)
(0.00)
(40.000
(+1 DOM
(-A03.2000
(+8,000)
(+0.309
(0.178)
[+2 002
(+8.33))
(k) Source
()) BioT5 (9/20 successful edits)
(m) DrugAssist (9/20 successful edits)
HACZRPE
HOCCOPT BESI
1-2.000
1-0.3001
cerani
(+0.C10)
(*nan)
1-2.0001
LOGA
LOGP
ROTECLES
SAT
(n) GeI.MO_I. (5/20 successful edits)
(0) MolEditRI (13/20 successful edits)
Figure 10: More visualization of edits on 20 tasks.
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Published as a conference paper at ICLR 2026
Source #168 GSK3B 0.040
BioT5: 0.030 (.0,000)
DrugAssist: 0.010 (-0.030)
GeLLMO: 0.030 (-3.020)
MolEdRIRL: 0 000 [-0.440]
Source 4182 GSK38 4:10.22
BioTS: 0.110 ( (.0.010)
DrugAssit: 0.090 (.0.030)
GALLMO: 0.110 (-3.010)
MoIEdRRL: 090 (-0.030)
tO
DrugAssist:
#283 GSK38 0.030
BioT5: (-0.010)
DrugAssist: 0. 0.000 (-0.030)
GeLLMO: 0,010 (-0 (-0.020)
MoIEdBRR: 0000 [-0.0301
Source #416 GSK38 0.070
BioT5: 0 0.020 (-0.050)
Drughssiat: 0. 0.040 (-0.030)
GeLLMO: 0,010 (-0.060)
MolEdRiRL: O 010 [-0.060)
Source 0.170
BioT5: (:0.050)
DrugAssist: (-0.090)
GeLLMO: 0.150 (-0.020)
MoIEdRRL: 0.060 [-0.110)
BioTs: (+0.010)
DrugAssist:
MoIEdIRLL (40)
GSK3B 0.000
(+0.010)
(+0.010)
HD
Source #186 GSK38 0:0000 0.020
BioTs: 0.057 (+0.037)
DrugAszist: D. 1,770 (+0.0501
GeLLMO: 0,040 (+0.020)
MoIEdIER:: O. 0.0BD (+0.060)
Source #240 GSK38 0.040
BioT5: 0.110 [+0.070)
DrugAssist: 0.100 (+0.060)
GeLLMO: 0,050 (+0.010)
MoIEdiRL: 0. 11110 (+0.070)
Druglasist: (+0.010)
G5K38 0.000
(+0.010)
Figure 11: Qualitative comparison of molecular editing methods.
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Published as a conference paper at ICLR 2026
Source #66 DRD2 4: 0.054
BioT5: 0.026 (.0,028)
DrugAssist: 0.037 (.D.016)
GeLLMO: 0.039 (-3.015)
MolEdIIL: 6 019 (.0.335]
Source #74 DRD2 4: 0.065
BioTS: 0.019 ( (0.066)
DrugAssist 0.063 (.0.022)
GALLMO: 0.038 (-3.047)
MoIEdRRL: D. 019 (-0.066)
DrugAssist:
(-0.014)
Source #221 DRO2 0.100
BioT5: (-0.046)
DrugAssist: 0.037 (-0.066)
GeLLMO: 0,093 (-0.010)
MoIEdIRL: 0.037 (-0.0661
Source #348 DRD2 L: 0.100
BioT5: O 0000 (-0.098)
DrugAssiat: O. 0.0 (-0.040)
GeLLMO: 0.039 [-0.061)
MolEdRRR: O 008 [-0.092]
Source 0.030
BioT5: (-0.001)
DrugAssist: (-0.016)
GeLLMO: 0.016 (-0.013)
MoIEdRIL: [-0.021)
BioTs: 0,008 (+0.005)
DrugAssist:
GeLLMO: (+0.007)
MoiEdIRL: (+0.034)
DRD2 0.003
(+0.005)
Source #21 DRD2 t: 0.002
BioTS: 0.003 (+0.001)
DrugAszist: L.016 (+0.014)
GeLLMO: 0.004 (+0.002
MolEdBERL: C. 02029 (+0.027)
Source #231 DRD2 0.000
BioT5: 0.002 [+0.0011
DrugAssist: 0.005 (+0.004)
GeLLMO: 0,002 (+0.001)
MoIEdiRL: 0 011 (+0.011)
Drughasist: (000)
DRD2 0.000
(+0.001)
Figure 12: Qualitative comparison of molecular editing methods.
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Published as a conference paper at ICLR 2026
Source 46 HDONORS 3,000
BioT5: 2,000 (1.000)
DrugAssist: 2,000 (-1.000)
GeLLMO: 2,000 (-1.000)
MoIEdIRL: 1 0000 [:2,000]
Source #43 HDONORS 4: 3.000
BioTs: 2,000 (-1.000)
DrugAssit: 1. 0000 (:2.000)
GALLMO: 1,000 (-2.000)
MoIEdRRL: 1. 0.0 (:2,000)
DrugAssist:
(-1.000)
(-2.000)
(=2.000)
Source #83 HDONORS 4: 4:0.000
BioT5: (-1.000)
DrugAssist: 0.000 (-2.000)
GeLLMO: 0,000 [-2 2,0000
MolEdiRRL: 0.000 [-2 0001
Source #396 HACCEPT 4.00000
BioT5: 3.000 (-1.000)
Drughssist: J. 3.000 (-1.000)
GeLLMO: 3,000 (-1.000)
MolEdiRRL 00000 [-2.000)
Source 4352
BiGT5: 3.000 (-1.000)
DrugAssist: (-1.000)
GeLLMG: 4.000 (-2.000)
MoIEdRIL: 3.000 (-3.000)
BioTs: (+1.000)
DrugAssist. 4.000 (+2.000)
GaLLMO: 4,000 (+nan)
MoIEdIRL: (+2.000)
HDONDRS 1: 2.000
Source #263 HDONDRS T: 3,000
BioT5: 5,000 (+2.000)
DrugAszist: & 0000 (+ (+300001
GeLLMD: 4,000 (+1.000)
MolEdIERL: 5. 0.0 (+2.000)
Spurce #447
BioT5: 28 000 (+24.000)
DrugAssist: 5. 00000 (+1.000)
GeLLMO: 000 (+1.000
MolEditR:: 000 (+2.000)
HACCEPT 4,000
Drughasist: (+1.000)
HDONORS 2.000
(+1.000)
Figure 13: Qualitative comparison of molecular editing methods.
36

---

Published as a conference paper at ICLR 2026
Source 43 SA 4: 5.219
BioT5: 5.132 (..006)
DrugAssist: 4.681 (...338)
GeLLMO: 4.469 (-0.750)
MoIEdIRL: 4 219 (-1.000]
Source #A3 SA 4: 4.004
BioTS: 4.068 ( (0.016)
DrugAssist: 3,649 (:0.435)
GaLLMO: 3.718 (-0.366)
MoIEdRRL: 3,439 (-0.643)
DrugAssist:
(-0.322)
BicT5: 4,214 (-0.187)
DrugAssist: 4. 044 (-0.357)
GeLLMO: 4,323 (-0.078)
MoIEdiRL: 3.986 [-0-435)
SA L: 4,400
Source #380 SA 4.31.
BioT5: 3.954 (-0.365)
DrugAssiat: 3.538 (-0.780)
GeLLMO: 3.770 (-0.548)
MolEdRiR: 3.4 3340 [-0.889)
4122
BioTS: 4.276 [+0.118)
DrugAssist: (+0.408)
GeLLMO: 4.204 (+0.046)
MolEdRIL: (+0.501)
BioTs: (+0.017)
DrugAscist: (+0.468)
3.687 (+0.038)
MoIEdIRL: (+0 540)
SA 3.659
Source #266 SA T: 3.369
BioT5: 4.417 (+1.048)
DrugAssist: 3.813 (+0.443)
GeLLMO: 3.893 (+0.524)
MolEditRL: 4.529 (+1.159)
Source #373 SA 4.870
BioT5: 5.148 (+0.278)
DrugAssist: 4.889 (+0.018)
GeLLMO: 5,313 (+0.443)
MolEditRL: 6 045 (+1.175)
Drughasist: (+0.332)
(+0.752)
(+0.257)
Figure 14: Qualitative comparison of molecular editing methods.
37


Reviews:
[{"review_id": "aMqEXXnIzI", "reviewer": "Reviewer_niJm", "summary": "This paper presents MolEditRL, a framework for structure-preserving molecular editing that integrates discrete graph diffusion with reinforcement learning. The model first performs discrete diffusion pretraining conditioned on the source molecule and natural-language instruction, then applies RL fine-tuning with property-based rewards and KL regularization to balance optimization and structural fidelity. The authors also introduce MolEdit-Instruct, a large-scale dataset for instruction-based molecular editing. Experiments demonstrate substantial gains over existing approaches in both property optimization and structural similarity metrics with fewer parameters.", "questions": "1. Hyperparameter sensitivity in RL fine-tuning. Hyperparameter sensitivity in RL fine-tuning. Did the authors conduct ablations on key hyperparameters such as $\\beta$, policy-update stride, top-k during sampling, and the 0.2 partial-success reward? How did the authors determine the final values?\n2. How are train/test splits constructed to avoid data leakage?\n3. I am curious about the inference efficiency of the diffusion model. Could the authors compare the generation efficiency of the proposed method with that of other models?\n4. The equation 5 appears to mis-type a $q$.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Clear writing and strong presentation. The paper is well-structured with clearly outlined motivation, method, and evaluation.\n- Technical soundness. The integration of discrete diffusion with RL fine-tuning is implemented carefully and supported by strong experimental performance.\n- Comprehensive evaluation. Extensive experiments across single and multi-property editing tasks, with multiple structure-based similarity metrics and FCD, provide convincing evidence.\n- Strong empirical results. The method consistently surpasses large language model baselines in both accuracy and structural fidelity while being significantly more parameter-efficient.", "weaknesses": "- Missing efficiency analysis. The paper highlights parameter efficiency but does not report oracle-query efficiency compared to baselines. Quantitative analysis of efficiency would strengthen practical claims.\n- Limited novelty in the diffusion–RL combination. Similar hybrid ideas have appeared in related areas. The primary contribution lies in adapting within the molecular editing context rather than fundamentally extending these techniques, making the innovation more technical than conceptual.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "SoDxunowhk", "reviewer": "Reviewer_JXjh", "summary": "The paper introduces MolEditRL, a framework for structure-preserving molecular editing. The approach is designed to modify a source molecule based on a natural language instruction to optimize for desired chemical properties while maintaining high structural similarity. MolEditRL consists of a two-stage process: first, a discrete graph diffusion model is pretrained to reconstruct target molecules conditioned on both source structures and text prompts. Second, this model is fine-tuned using reinforcement learning (RL) with a KL-regularized objective to explicitly maximize property-related rewards. To facilitate this research, the authors constructed a new large-scale dataset, MolEdit-Instruct. The experimental results show that MolEditRL outperforms several baselines, including large language models, in both editing accuracy and structural fidelity.", "questions": "1. Oracle Hacking. Could you please explicitly discuss the problem of \"oracle hacking\" and elaborate on how the KL-regularization in your RL objective functions maintains chemical realism and prevents the model from generating non-synthesizable molecules? \n2. Justifying Natural Language. Can you provide a more compelling justification for using natural language instructions over structured inputs for the current set of tasks? Better yet, could you demonstrate the model's capabilities on more complex editing tasks where the flexibility of natural language is genuinely advantageous?\n3. Evaluating Pre-training. Could you provide standalone evaluation metrics for the discrete diffusion model after the pre-training stage is complete (but before RL)? Metrics like validity, reconstruction accuracy, and FCD against the ground-truth distribution would help disentangle the contributions of the two training stages.\n4. Instruction Encoding Details. Could you please clarify the precise mechanism for embedding the instruction tokens and integrating them with the graph representations in the model's input?", "limitations": "", "rating": 2, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The problem of controlled, structure-aware molecular editing is highly relevant to drug discovery. The paper’s core approach of combining a discrete graph diffusion model with a full-trajectory RL fine-tuning process is compelling. By operating on graph representations, it sidesteps the syntactic instability and representational ambiguity issues inherent in string-based methods, representing a step towards more reliable molecular design.\n2. The construction of the MolEdit-Instruct dataset is a valuable contribution to the community. The experimental evaluation is extensive, comparing the proposed method against a strong and relevant set of recent baselines on a diverse range of single- and multi-property tasks.\n3. The authors show clear motivation of the design choices, particularly the shift towards graph-based representations and the integration of RL for precise optimization.", "weaknesses": "1. The method for encoding the natural language instruction is not sufficiently detailed in the main text. Section 3.1 states that instruction tokens, source atoms, and target atoms are embedded and concatenated into a unified sequence. However, it is unclear how these instruction tokens are specifically embedded and integrated.\n2. A well-known failure mode for generative models fine-tuned with RL for molecular optimization is \"oracle hacking,\" where the model learns to generate molecules that achieve high scores from the property predictor but are chemically invalid, unstable, or non-synthesizable. While the KL-regularization term is likely intended to mitigate this by constraining the policy to stay close to the pretrained distribution, the paper does not explicitly discuss this critical issue or demonstrate how its method avoids it. Furthermore, while the paper includes REINVENT4 as a baseline, it misses a broader comparison with other established RL-based molecular optimization frameworks (e.g., MolDQN, GCPN) that could provide a more direct assessment of its RL component.\n3. The experiments do not showcase scenarios that leverage the true power and flexibility of natural language, such as describing complex, localized edits or conveying chemical intuition. Without such examples, the language-conditioning aspect feels underexploited, and its contribution to the model's performance is unclear. The current property editing description is too simple.\n4. The performance of language-conditioned models is often highly sensitive to the specific phrasing of the input prompt. The paper presents a single, fixed prompt template for each task (Table 6) but provides no analysis or ablation study on how variations in these prompts might affect editing outcomes. This is an omission, as it leaves the robustness of the instruction-following capability unevaluated.\n5. The framework's success hinges on the quality of the distribution learned during the first pre-training stage. A high-quality pretrained model should provide a strong prior for the RL fine-tuning, ensuring that the search for high-reward molecules begins from a space of valid and realistic structures. However, the paper does not provide any direct, quantitative evaluation of the stage-one diffusion model (e.g., reconstruction accuracy, validity, and FCD on a held-out set before any RL fine-tuning). This makes it difficult to assess whether the strong final performance is due to a superior pretrained base model or solely the effectiveness of the RL stage.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "44e29cn9ps", "reviewer": "Reviewer_rzAV", "summary": "This work proposes a molecular editing framework based on discrete diffusion and reinforcement learning. Specifically, authors design a structure-preserving attention mechanism with a bias term encoding adjacency information, to preserve the structural integrity of the template molecule during editing. The molecular generation process is implemented as a pre-trained discrete diffusion model, which is then fine-tuned with editing success and validity rewards on pair molecular edit data. The framework is then tested on several molecular design tasks involving multiple properties and outperforms the baselines on all metrics.", "questions": "- Eq3: in the early denoising steps, as $E_{tgt}$ is inaccurate and noisy, how to prevent it from misinforming the denoising of the atoms?\n\n- Since the language used for the editing instruction is well-structured and only the property type (and potential numbers) matter, how much does the language model backbone really contribute?\n\n- Is the structure-preserving behavior more achieved by the structure-aware attention, or the explicit KL divergence objective during RL? I.e. what are the raw similarity values in the ablation study results?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 3, "strengths": "- The proposed structure-aware attention mechanism offers a potential solution to the complex problem of conditioning the molecular generation with structural constraints.\n\n- The joint encoding of both instruction text and molecular structure achieves good alignment between text description and the molecular space.\n\n- The performance of the proposed framework significantly outperforms all baselines by several tasks and metrics. Also, detailed visualizations and interpretations of the generative results as well as ablation studies are provided.\n\n- The authors also provide a comprehensive dataset for molecular edit paired covering a wide range of properties.", "weaknesses": "- The RL setting relies on ready-to-use and fast property calculations, so generalizability to rare or unseen properties is limited (especially for protein binding).\n\n- Some specific questions on methods and clarity. See Questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Cky52r47QN", "reviewer": "Reviewer_zGL2", "summary": "This paper proposes **MolEditRL**, a framework combining discrete graph-diffusion and reinforcement learning (RL) for structure-aware molecular editing. It first uses a discrete diffusion model to learn to reconstruct molecules given editing instructions and source molecules, and then fine-tunes via an RL objective with property-based rewards and a KL-regularizer to enforce structural fidelity.\nExperiments cover a new editing dataset (“MolEdit-Instruct”), single- and multi-property editing, and comparisons to state-of-the-art baselines, showing substantial improvements in editing success, scaffold preservation, and parameter efficiency.", "questions": "Q1. In section 3.4, equation (8), how can the function $r(.)$ be implemented? Is this an oracle by the RDKit tool?\n\nQ2. In section 4.5, the effect of different RL training schemes is discussed. What does this statement mean: \"while GDPO leverages **x0-parameterization** to optimize ***only the final output*** \" and \"MolEditRL introduces KL-regularized optimization ***over the entire diffusion process*** \"? \n\nDoes this mean that GDPO is also having the reward for the final step? Then how is GDPO different from MolEditRL in this case, given that both methods apply the same x0-parameterization techniques described in Equations (12-14)?\n\nOther questions: please check my weakness section above regarding running time and the missing ablation study on the structure-aware module inside the transformer module.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The reviewer acknowledges the following contributions of the paper:\n\n **Novelty and Technical Contribution** \n\n- The integration of discrete diffusion and reinforcement learning for molecular editing (not just generation) is an original and well-motivated application.\n\n- The KL-regularized RL fine-tuning introduces a principled mechanism to balance property optimization with structural preservation, addressing the classic trade-off between exploration and validity.\n\n- A particularly notable technical insight is how the authors address the high variance problem of rewards that appear only at the final step of the diffusion trajectory. By rewriting the reverse transition using x_{0}-parameterization and approximating gradients through a reward-weighted cross-entropy loss, the algorithm shows a more stable and computationally efficient estimator.\n\n**Dataset Contribution**\n\nThe newly released MolEdit-Instruct dataset fills a key gap for large-scale, instruction-based molecular editing tasks and is a valuable contribution to the research community.\n\n**Breadth of Experiments**\n\n- The authors conduct comprehensive experiments covering single-property, multi-property, and cross-dataset generalization scenarios (both with the proposed dataset and C-MuMOInstruct (appendix)). The methods are compared against a variety of baselines, e.g., Drug-assist.\n\n- The obtained results show gains in both editing success rate and structural similarity to the source molecule.", "weaknesses": "**Incremental Nature of Some Components**:\n\n- While the integration of discrete diffusion and RL is novel, both components themselves (diffusion for graph generation and RL for property optimization) have been individually explored before in molecular design. The novelty thus lies more in **their combination and adaptation** rather than in a fundamentally new paradigm.\n\n- In the meantime, the Reviewer found another paper [1] shares highly similar ideas with this work, i.e., using diffusion to generate a new updated molecule and then regularized by reinforcement by the PPO method. Though it should not count this work as identical [1] because this one was released after the ICLR deadline. Instead, the reviewer suggests that the author include this paper in the related works and discuss it.\n\n**Limited Discussion on Computational Cost and Scalability**\n\n- The paper reports efficiency in terms of parameter count but provides limited analysis of training/inference cost, scaling to larger biomolecules, or real-time editability.\n\n- Reinforcement learning fine-tuning may still be computationally expensive due to oracle calls and multi-step diffusion.\n\n**Ablation Gaps on Graph-Specific Components**\n\n- Although the authors conduct extensive ablations, there is no explicit comparison showing the benefit of the structure-aware attention mechanism versus a standard transformer without graph bias. The structural-aware self-attention is emphasized as one of the selling points in the paper; thus, a deeper analysis of this property is important.\n\n**Clarity and Accessibility**\n\n- The methodology section is dense and mathematically detailed, which could challenge readers from a non-ML background. A higher-level intuition or illustrative example could improve accessibility.\n\n- It would also be beneficial to discuss potential limitations, such as failure cases (e.g., when instructions are ambiguous or contradict structural constraints).\n\n[1] Kaech, Benno, et al. \"Refine Drugs, Don't Complete Them: Uniform-Source Discrete Flows for Fragment-Based Drug Discovery.\" Arxiv 2025", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "j0yovUHfmh", "reviewer": "Reviewer_F1Hf", "summary": "This paper introduces MolEditRL, a model that modifies input molecules to optimized some desired chemical property, which is provided by the user as a text prompt. The MolEditRL framework works by combining a graph diffusion model with a reinforcement learning training strategy, with rewards coming from an oracle (e.g. RDKit) for the chemical property of interest. \nThe paper also proposes a novel dataset, MolEdit, tailored for benchmarking molecular editing models with natural language instructions.\nThe method is benchmarked on MolEdit against baselines from the literature, and shows consistently state of the art performance across several tasks and evaluation metrics.", "questions": "- Please clarify the issues raised on the oracle. \n- Why are you using textual instructions rather than a categorical variable (e.g. one-hot encoding for supported properties) for conditioning the diffusion?", "limitations": "", "rating": 6, "confidence": 2, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The paper is quite well-written, as it clearly states the motivation and the high level idea of the method. Even though I am not familiar with the literature, I could easily understand most of the paper. \n- The empirical results shown in the paper are very impressive, as consistently MolEditRL shows state-of-the-art performance across several tasks and evaluation metrics. \n- The paper shows results not only on their new MolEdit dataset, but also on the C-MuMOInstruct dataset from the literature. Even here, MolEditRL is most of the time the leading model.", "weaknesses": "- Some details about the use of an oracle as a reward are unclear to me. It is unclear whether the model would work for chemical properties for which no oracle is available, or if the oracle can be noisy (e.g. a ML model for function/property prediction)\n- Related to the point above, it is unclear to me how the oracle for the reward is chosen. It seems like the oracle should depend on the textual instruction S, but there are no details on how it is selected. \n- A few chemical properties are reported. However, it is unclear how these properties were selected for benchmarking, and if the method shows the same performance on other kinds of properties. \n\nMinor points:\n- In Table 1 some negative vspace seem to have been used too aggressively. \n- In Figure 4a, the choice of using a line plot is questionable, since the x axis is models. A bar plot like  FIg. 5b would have been more appropriate\n- The authors should use the extra page to present some results on the generalization to unseen properties and on datasets other than their own, as I think it would strengthen the paper.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)