Controllable Molecule Generation via Sparse Representation Editing: An Interpretability-Driven Perspective

Zhuoran Li; Xu Sun; Chang Wen Chen; Wanyu Lin

cs.LGNov 6, 2026

Controllable Molecule Generation via Sparse Representation Editing: An Interpretability-Driven Perspective

Authors: Zhuoran Li, Xu Sun, Chang Wen Chen, Wanyu Lin

Organizations: Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR, China

Abstract

Controllable molecule generation is crucial for diverse scientific applications, such as drug discovery and materials design. While large language models (LLMs) show great promise, their dense and entangled representations impede precise control over the generation of molecules with bespoke substructures or properties. To address this, we propose Sparse Representation Editing (SpaRE), an interpretability-driven framework for fine-grained and precise control in LLM-based molecule generation. The crux of SpaRE is to learn an overcomplete sparse feature space that disentangles LLM representations into a compact set of latent features corresponding to chemically meaningful concepts. Within this space, we can directly manipulate these concept-aligned latent features to achieve (1) local control, by generating target atoms and functional groups at specified positions; and (2) global control, by customizing the overall structural and physicochemical properties within defined ranges. In this way, our framework advances interpretability from post-hoc analysis to actionable generative control. Experiments show that SpaRE can generate chemically desirable molecules under complex constraints in real-world scenarios, while offering mechanistic insights for quantitative structure-property analysis. The code and demo are available at https://github.com/WanyuGroup/ICML2026_SpaRE.

Figures & tables

Figure 1: SpaRE learns concept-specific activation patterns that connect dense LLM representations to chemically meaningful concepts in latent space. By modulating concept activation strengths, SpaRE steers the corresponding LLM representations toward molecules with desired substructures and properties (dashed boxes denote skipped computations).

Figure 2: Activation patterns for concepts are derived using two schemes: ( Left ) local substructure control of carbon atom and ( Right ) global property control of solubility. For each concept, we select only latent features that are consistently activated ( i.e., activation value $>0.5$ ) in every sample of that concept and concatenate them to form the concept representation used for its control.

GNN-Based
	Quality ( $\uparrow$ )					Control ( $\uparrow$ )	Synthesis ( $\downarrow$ )	Efficiency ( $\downarrow$ )
Model	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA score	Time
MARS	87.24	82.53	84.62	89.82	98.24	50.37	4.12	384.24
MolEvol	88.73	80.96	84.82	94.17	98.92	37.25	4.26	231.27
Diffusion-Based
LDMol	90.23	80.42	80.32	89.57	98.48	26.36	3.96	290.52

Table 1: Site-specific molecule generation on the ChEBI-20 dataset ( Edwards et al., 2021 ) . Quality and controllability are percentages, while synthesizability and efficiency are reported as numerical values. Best and second-best results are marked in bold and underlined.

Figure 3: Distribution of molecules generated under global control of solubility: ( Left ) amplification, ( Middle ) suppression, and ( Right ) controllable tuning by varying activation strength.

Figure 4: ( Top ) The edited molecules optimize lipophilicity while maintaining high scaffold and pharmacophore similarity. ( Bottom ) After optimizing the editing routes, the generated molecules exhibit improved water solubility and synthetic accessibility.

Method	Success	Valid	Novelty	Diversity
MolEvol	88.9	95.1	81.4	67.9
RetMol	93.8	98.7	87.2	71.6
TGM-DLM	94.5	99.7	78.1	67.4
Atomas	95.2	97.6	85.3	72.2
Llamole	90.3	97.3	90.6	68.2
SpaRE ( Ours )	98.1	100.0	96.2	74.9

Table 2: Generation quality (%) for molecules optimized for QED, SA score, and predicted binding affinities to GSK3

\beta

and JNK3, as estimated by the pretrained models ( Jin et al., 2020 ) .

Figure 5: ( Top ) Distribution of generated molecules for oral drugs, showing that three key constraints are precisely optimized within their defined ranges, with SpaRE outperforming all baselines. ( Bottom ) The optimization objectives are achieved in only eight iterations.

Figure 6: The property constraints employed for GSK3

\beta

and JNK3 inhibitor discovery are precisely optimized within target ranges. SpaRE achieves stronger overall performance than the baselines.

Figure 7: Trajectories of iterative property optimization.

Feature ID	Category	Concept	Detailed Interpretation
2107 , 5036 , 10626 , 14729	Functional Group	N-oxide	Recognizes the N-oxide feature for tuning the electronics and solubility of heterocycles.
10113 , 14412	Structural Class	Saturated carbocyclic systems	Generates saturated carbocyclic systems ( e.g., cyclohexane), focusing on sp 3 -rich structures.
2839 , 2872 , 13586	Stereochemistry	Helical chirality	Models helical chirality, a stereochemical feature arising from a molecule’s screw-shaped structure.
14381 , 14624	Chemical Interaction	Hydrogen bond donor/acceptor arrays	Specifies a defined pattern of hydrogen bond donors/acceptors to guide intermolecular interactions.
7 , 77 , 12777	Topology	Branched vs linear chain isomerism	Differentiates between branched and linear topologies of an aliphatic chain.
1601 , 8808 , 11765	Conformation	Acyclic conformation: gauche/trans preference	Understands the energetic preference for gauche vs. trans conformations in acyclic chains.

Table 3: LLM-derived interpretations of latent features, summarizing representational commonalities and reporting each feature’s category, concept, and detailed interpretation. More examples are presented in Table 17 .

Figure 8: The Left Panel: Ablation study on the number of features. The Right Panel: Feature importance based on p -values.

Expansion Factor	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA Score
10	100.00	78.37	85.93	89.85	99.41	93.52	3.87
20	100.00	77.25	89.98	87.29	99.39	91.43	3.89
40	100.00	81.60	92.10	97.24	99.66	98.92	3.78
80	100.00	76.95	84.23	87.82	99.25	96.47	3.56
100	100.00	74.67	89.51	91.71	98.92	85.74	3.91

Table 4: Ablation study on expansion factor for site-specific molecule generation on the ChEBI-20 dataset ( Edwards et al., 2021 ) . The first six columns report percentages, and the last column reports numerical values.

Layer Choice	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA Score
0	100.00	79.88	84.31	86.11	98.57	0.92	3.82
4	100.00	75.85	86.02	87.93	99.37	1.46	3.78
8	100.00	78.72	86.74	86.81	98.64	4.02	3.57
12	100.00	73.75	85.38	91.56	99.69	10.52	3.62
16	100.00	79.91	82.47	86.13	99.72	56.61	3.86
20	100.00	80.34	83.04	89.71	99.53	93.59	3.69

Table 5: Ablation study on LLM layer choice for site-specific molecule generation on the ChEBI-20 dataset ( Edwards et al., 2021 ) . The first six columns report percentages, and the last column reports numerical values.

Appendix figures & tables51 assets

Supplementary material from the paper’s appendix.

Appendix

Hyperparameter	Value
Base Model	MolXPT ( Liu et al., 2023b )
Transformer Layers	24
Attention Heads	16
Hidden Dimension	1024
Data Format	Group SELFIES
Max Sequence Length	1024

Appendix

Table 6: Hyperparameters for Fine-Tuning

Hyperparameter	Value
Input Dimension	1024
Expansion Factor	40 $\times$
Hidden (Sparse) Dimension	40,960
Encoder Activation	ReLU + $L_{2}$ norm
Reconstruction Loss	MSE
Sparsity Penalty	$L_{1}$

Appendix

Table 7: Hyperparameters for SAE Training

Figure 9: Distribution of molecules generated under solubility control with LLM layer

4

: ( Left ) amplification, ( Middle ) suppression, and ( Right ) controllable tuning by varying activation strength.

Figure 10: Distribution of molecules generated under solubility control with LLM layer

16

: ( Left ) amplification, ( Middle ) suppression, and ( Right ) controllable tuning by varying activation strength.

GNN-Based
	Quality ( $\uparrow$ )					Control ( $\uparrow$ )	Synthesis ( $\downarrow$ )	Efficiency ( $\downarrow$ )
Model	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA score	Time
MARS	87.62	81.62	80.47	89.88	97.83	32.86	4.26	371.28
MolEvol	86.24	78.86	82.81	91.47	96.82	38.43	4.24	252.62
Diffusion-Based
LDMol	86.49	83.01	80.09	89.16	95.24	31.74	3.80	346.19

Appendix

Table 8: Local molecule generation on the GEOM-DRUGS dataset ( Axelrod and Gomez-Bombarelli, 2022 ) . Quality and controllability are reported as percentages, while synthesizability and efficiency are reported as numerical values. Best and second-best results are indicated in bold and underlined, respectively.

	Quality ( $\uparrow$ )					Control ( $\uparrow$ )	Synthesis ( $\downarrow$ )	Efficiency ( $\downarrow$ )
Dataset	Validity	Uniqueness	Novelty	Atom Stability	Completeness	Success Rate	SA score	Time
ChEBI-20	100.00	99.53	98.38	63.94	82.29	-	7.55	1.82
GEOM-DRUGS	100.00	97.29	96.97	41.51	92.35	-	7.85	1.56

Appendix

Table 9: The results of MCTS-based naive string substitution on two datasets. Quality and controllability are reported as percentages, while synthesizability and efficiency are reported as numerical values.

Model	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success	SA Score
GPT-5.4	88.60	80.83	83.79	92.11	95.41	56.98	3.97
Claude Opus 4.6	91.02	79.60	84.77	89.74	95.46	66.82	4.02
Gemini 3.0 Pro	92.05	77.53	84.07	91.52	96.91	57.27	3.86
SpaRE ( Ours )	100.00	81.60	92.10	97.24	99.66	98.92	3.78

Appendix

Table 10: Evaluation of recent general-purpose LLMs on local control on the ChEBI-20 ( Edwards et al., 2021 ) . All models are prompted with the same task instructions and decoded under the same post-processing and evaluation pipeline.

Figure 11: Distribution of molecules generated under aromaticity amplification. Aromaticity is evaluated by ASE ( Left ), HOMA ( Middle ), and NICS ( Right ): higher ASE and HOMA, and lower NICS, indicate increased aromaticity.

Figure 12: Distribution of molecules generated under aromaticity suppression. Aromaticity is evaluated by ASE ( Left ), HOMA ( Middle ), and NICS ( Right ): lower ASE and HOMA, and higher NICS, indicate reduced aromaticity.

Figure 13: Tuning activation strength enables adjustment of aromaticity, as measured by ASE ( Left ), HOMA ( Middle ), and NICS ( Right ).

Figure 14: Distribution of molecules generated under global control of rings: amplification ( Left ) and suppression ( Middle ) with smooth tunability via changes in activation strength ( Right ).

Figure 15: Distribution of molecules generated under hydrogen bond acceptor amplification. Hydrogen bonding is evaluated by the count of HBA ( Left ) and HBD ( Right ).

Figure 16: Distribution of molecules generated under hydrogen bond acceptor suppression. Hydrogen bonding is evaluated by the count of HBA ( Left ) and HBD ( Right ).

Figure 17: Tuning activation strength enables adjustment of hydrogen bonding, as measured by the count of HBA ( Left ) and HBD ( Right ).

Figure 18: Distribution of molecules generated under ortho-disubstituted position amplification. Ortho-disubstituted positions are evaluated by their count ( Left ) and steric value ( Right ), with larger steric value indicating more ortho-disubstituted positions.

Figure 19: Distribution of molecules generated under ortho-disubstituted position suppression. Ortho-disubstituted positions are evaluated by their count ( Left ) and steric value ( Right ), with lower steric value indicating less ortho-disubstituted positions.

Figure 20: Tuning activation strength enables adjustment of ortho-disubstituted positions, as measured by their count ( Left ) and steric value ( Right ).

Figure 21: The edited molecules optimize ( Top ) water solubility while maintaining ( Bottom ) high scaffold and pharmacophore similarity, showing SpaRE’s precision in the optimization.

Figure 22: Quantitative estimate of drug-likeness for lipophilicity improvement. Two of the four routes achieve favorable drug-like properties ( i.e., QED $\geq$ 0.6).

Figure 23: Quantitative estimate of drug-likeness for water solubility improvement. Three of the four routes achieve favorable drug-like properties ( i.e., QED $\geq$ 0.6).

Figure 24: Visualization of four stepwise bioisosteric editing routes for lipophilicity improvement (Routes 1–4, top to bottom).

Figure 25: Visualization of four stepwise bioisosteric editing routes for water solubility improvement (Routes 1–4, top to bottom).

ID	Original	Step 1	Step 2	Step 3	Step 4	Step 5	Step 6	Step 7	Step 8	Avg.
1_1	2.18	1.55	2.36	3.40	1.70	–	–	–	–	2.24
1_2	2.05	1.60	2.21	3.35	3.80	1.96	–	–	–	2.50
1_3	1.66	1.29	1.85	3.07	1.53	–	–	–	–	1.88
2_1	2.02	2.20	2.17	2.86	3.61	3.42	–	–	–	2.71
2_2	2.02	2.20	2.17	2.86	3.61	3.74	3.55	3.55	3.60	3.03
2_3	2.02	2.20	2.17	2.86	3.61	3.72	3.90	4.25	3.66	3.15

Appendix

Table 11: SA score for 15 representative molecular editing routes. The ID is formatted as “GroupID_RouteID” ( i.e., route #1 from group 1 is denoted as 1_1).

ID	Original	Step 1	Step 2	Step 3	Step 4	Step 5	Step 6	Step 7	Step 8	Avg.
1_1	0.99	0.99	0.96	0.54	0.98	–	–	–	–	0.89
1_2	0.99	0.99	0.91	0.76	0.45	0.99	–	–	–	0.85
1_3	0.99	0.99	0.98	0.78	0.97	–	–	–	–	0.94
2_1	0.99	0.99	0.99	0.95	0.92	0.60	–	–	–	0.91
2_2	0.99	0.99	0.99	0.95	0.92	0.88	0.40	0.40	0.37	0.76
2_3	0.99	0.99	0.99	0.95	0.92	0.71	0.28	0.05	0.26	0.68

Appendix

Table 12: RA score for 15 representative molecular editing routes. The ID is formatted as “GroupID_RouteID” ( i.e., route #1 from group 1 is denoted as 1_1).

Figure 26: The distribution of generated molecules following editing route optimization to increase the number of rotatable bonds.

Figure 27: The distribution of generated molecules following editing route optimization to increase the number of

\text{sp}^{3}

carbon atoms.

Figure 28: Visualization of molecules with favorable synthetic accessibility and improved water solubility. Introducing carboxylate anion, nitro group, and primary amide at specific sites leads to improved water solubility.

Figure 29: Visualization of molecules with favorable synthetic accessibility and increased rotatable bonds. Introducing benzyl, methyl, or ethyl groups at specific sites increases the number of rotatable bonds.

Figure 30: Visualization of molecules with favorable synthetic accessibility and increased $\text{sp}^{3}$ carbon count. Introducing a cyclohexyl group, an allyl-cyclohexyl group, or disulfide linkages at specific sites increases the number of $\text{sp}^{3}$ carbon atoms.

Figure 31: Distribution of generated molecules for Perindopril. The hard constraints on target molecular weight and LogP are precisely satisfied, while the soft constraints, QED and SA score, are optimized more effectively than baseline models.

Figure 32: Iterative convergence of molecular properties during the optimization process for Perindopril.

Figure 33: Distribution of generated molecules for Aripiprazole. The hard constraints on target molecular weight and LogP are precisely satisfied, while the soft constraints, QED and SA score, are optimized more effectively than baseline models.

Figure 34: Iterative convergence of molecular properties during the optimization process for Aripiprazole.

Figure 35: Distribution of generated molecules for oral drug optimization, showing that five property constraints (with the other three illustrated in Figure 5 ) are precisely optimized within their defined ranges, with SpaRE surpassing all baselines.

Figure 36: Distribution of generated molecules under Lipinski’s Rule of Five, showing that six property constraints are precisely optimized within their defined ranges, with SpaRE surpassing all baselines.

Figure 37: Iterative convergence of molecular properties during the optimization process under Lipinski’s Rule of Five.

Figure 38: Distribution of generated molecules for sublingual drug optimization, showing that eight property constraints are steered toward their target ranges, with SpaRE outperforming all baselines.

Figure 39: Iterative convergence of molecular properties during the optimization process for sublingual drug design.

	Constraint Satisfaction Rate					Generation Quality
Method	GSK3 $\beta\geq 0.5$	JNK3 $\geq 0.5$	QED $>0.6$	SA $<4$	All	Success	Validity	Novelty	Diversity
TGM-DLM	72.4	69.2	90.6	98.4	44.7	96.1	98.4	77.2	66.9
MolEvol	57.1	61.3	65.6	73.9	17.0	90.1	93.5	82.4	67.3
RetMol	74.1	86.6	92.3	93.3	55.3	95.9	97.2	87.6	72.6
Atomas	72.2	80.8	90.7	93.4	49.5	94.1	97.7	85.2	70.9
Llamole	70.3	73.7	89.0	93.5	43.1	88.2	98.3	91.1	67.1

Appendix

Table 13: Additional multi-target drug discovery results on constraint satisfaction rate (%,

\uparrow

) and generation quality (%,

\uparrow

). Best and second-best results are marked in bold and underlined, respectively.

Method	LogP ([1,3])	MW ( $<500$ )	TPSA ( $\leq 140$ )	HBD ( $\leq 5$ )	HBA ( $\leq 10$ )	Arom. Rings ( $\leq 3$ )	SA ( $<5$ )	QED ( $>0.7$ )	All
Original	57.2	74.8	75.0	63.9	68.1	60.5	60.6	32.8	1.9
MolEvol	65.4	84.1	88.4	91.3	92.1	79.0	62.1	22.6	1.6
Llamole	88.3	98.3	96.9	91.9	91.5	77.8	78.8	56.8	21.7
Atomas	90.3	97.6	95.8	96.3	96.0	99.1	91.5	98.4	67.4
RetMol	95.8	98.9	100.0	89.6	93.7	93.9	96.2	0.5	0.2
TGM-DLM	100.0	96.5	94.2	97.1	96.6	94.8	95.4	97.9	78.5

Appendix

Table 14: Quantitative success rate (%,

\uparrow

) for oral drug design. Best and second-best results are indicated in bold and underlined, respectively.

Model	Oral Drug Design	Sublingual Drug Optimization	Multi-Target Drug Discovery
TGM-DLM	4.43	7.07	17.84
MolEvol	2.40	3.61	8.98
RetMol	10.12	15.99	40.12
Llamole	43.11	64.02	161.77
Atomas	1.92	2.83	7.01
SpaRE ( Ours )	2.18	3.50	6.67

Appendix

Table 15: Runtime for each method (seconds per molecule satisfying the specified constraints,

\downarrow

). Best and second-best results are indicated in bold and underlined, respectively.

Feature ID	Interpretation
1592	Polarity
7329	Hydrophilic Groups
38472	Ionic Nature
29104	Nonpolar
12945	Amphiphilic
23847	Lattice Energy

Appendix

Table 16: Feature interpretations from the fitted linear model indicate that these features are statistically significant for the prediction of solubility.

Figure 40: The activations for input–output patterns (from LLM layer 10), where the X - and Y -axes represent the input and output of the LLM, respectively. We focus on the most frequently generated output token and identify the input tokens that most strongly activate it.

Feature ID	Category	Concept	Interpretation
21 , 2737 , 10881 , 13815	Functional Group	Peroxide (R-O-O-R’) bond	Recognizes the peroxide (R-O-O-R’) bond, a feature implying reactivity or instability.
1883 , 4374 , 10168 , 13616 , 14625	Structural Class	Scaffold: Piperidine ring	Identifies the piperidine ring as a common saturated heterocyclic scaffold.
6643 , 6749 , 7729 , 9916 , 14095	Structural Class	Scaffold: Oxazole/Thiazole ring	Recognizes oxazole or thiazole rings as key five-membered aromatic scaffolds.
4580 , 12785	Structural Class	Scaffold: Thiophene ring	Identifies the thiophene ring as a sulfur-containing, core aromatic scaffold.
327 , 5576 , 11937 , 12190 , 12674	Functional Group	Sulfone (R-S(=O) 2 -R’)	Recognizes the sulfone group (R-S(=O) 2 -R’), a stable and often electron-withdrawing moiety.
1478 , 5676 , 13132	Stereochemistry	Center of symmetry/meso compounds	Understands the concept of meso compounds: molecules with chiral centers that are achiral overall.

Appendix

Table 17: LLM-derived interpretations of latent features, summarizing commonalities and reporting each feature’s category, concept, and detailed interpretation. Feature groups are not necessarily mutually exclusive, and a latent feature may contribute to multiple higher-level concepts.

Model	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA Score
MolEvol	92.96	79.61	84.82	81.04	97.89	31.53	4.79
LDMol	95.02	82.27	91.14	94.96	98.62	21.62	3.91
TGM-DLM	97.30	82.78	84.26	93.52	96.39	24.63	4.19
RetMol	93.58	78.03	73.84	82.75	98.23	28.34	4.92
Llamole	94.17	78.59	75.71	89.25	94.71	19.49	4.39
SpaRE ( Ours )	100.00	78.87	90.16	89.85	99.21	96.77	3.98

Appendix

Table 18: Local molecule generation on the PubChemSTM dataset ( Liu et al., 2023a ) . Quality and controllability are reported as percentages, while synthesizability and efficiency are reported as numerical values. Best and second-best results are indicated in bold and underlined, respectively.

Model	Valid	Uniqueness	Novelty	Atom Sta	Completeness	Success Rate	SA Score
MolEvol	91.19	72.48	93.62	89.59	92.92	42.57	4.78
LDMol	96.48	70.48	88.52	86.38	99.64	33.49	3.79
TGM-DLM	95.15	75.91	94.92	84.92	97.44	36.84	4.08
RetMol	96.31	73.65	82.42	87.78	94.50	22.30	4.22
Llamole	96.87	69.70	90.76	91.13	91.58	43.76	4.49
SpaRE ( Ours )	100.00	77.25	96.70	93.98	99.85	91.43	3.85

Appendix

Table 19: Local molecule generation on the MolTextNet dataset ( Zhu et al., 2025 ) . Quality and controllability are reported as percentages, while synthesizability and efficiency are reported as numerical values. Best and second-best results are indicated in bold and underlined, respectively.

Figure 41: Distribution of molecules generated under solubility control on the PubChemSTM dataset ( Liu et al., 2023a ) : ( Left ) amplification, ( Middle ) suppression, and ( Right ) controllable tuning by varying activation strength.

Figure 42: Distribution of molecules generated under solubility control on the MolTextNet dataset ( Zhu et al., 2025 ) : ( Left ) amplification, ( Middle ) suppression, and ( Right ) controllable tuning by varying activation strength.

Method	Accuracy (%, $\uparrow$ )	AUC (%, $\uparrow$ )
Random 1	53	47
Random 2	54	48
Random 3	50	52
Random 4	52	49
Random 5	48	55
Ours	88	92

Appendix

Table 20: Solubility prediction using ten randomly selected latent features, matched to the dimensionality of the linear probe in Figure 8 ).

Setting	# Removed	Accuracy (%, $\uparrow$ )	AUC (%, $\uparrow$ )
w/o x1	1	85	88
w/o x1-x2	2	78	83
w/o x1-x3	3	76	79
w/o x1-x4	4	72	75
w/o x1-x5	5	70	72
w/o x1-x6	6	67	64

Appendix

Table 21: Ablation study on solubility prediction features via leave-one-out removal.

Direction	$\Delta$ LogP ( $\downarrow$ )	$\Delta$ Ring Count ( $\uparrow$ )	LogP Success Rate (%, $\uparrow$ )	Ring Success Rate (%, $\uparrow$ )
Random 1	- 0.13	+ 0.03	44.81	47.16
Random 2	+ 0.10	- 0.05	38.97	47.70
Random 3	- 0.02	- 0.19	39.14	39.97
Random 4	- 0.07	- 0.18	47.29	42.54
Random 5	+ 0.07	- 0.20	44.01	41.56
Ours	- 0.99	+ 1.12	92.43	91.60

Appendix

Table 22: Random directions matched in norm for increasing solubility ( i.e., measured by LogP) and the number of rings.

\Delta

indicates the change in value.

Molecular Generation

Molecular generation develops computational methods to create chemical structures and conformations for discovery, optimization, and structure elucidation.

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Molecular Optimization LLM Interpretability Large Language Model-Guided Optimization

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Large language models possess strong chemical reasoning capabilities, making them effective molecular editors. However, property-relevant information is implicitly entangled across their dense hidden states, providing no explicit handle for property control: a substantial fraction of edits fail to improve or even degrade target properties. To address these issues, we propose SLIM (Sparse Latent Interpretable Molecular editing), a plug-and-play framework that decomposes the editor's hidden states into sparse, property-aligned features via a Sparse Autoencoder with learnable importance gates. Steering in this sparse feature space precisely activates property-relevant dimensions, improving editing success rate without modifying model parameters. The same sparse basis further supports interpretable analysis of editing behavior. Experiments on the MolEditRL benchmark across four model architectures and eight molecular properties show consistent gains over baselines, with improvements of up to 42.4 points.

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