Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution
Authors: Sihan Wang
Wenjie Du
Qing Zhu
Yang Wang
Venue: NeurIPS 2025 poster ()
Keywords: Artificial intelligence in chemical research
gain-based core subset selection
asymmetric molecular interaction graph neural networks
Primary area: machine_learning_for_sciences

Abstract: Artificial intelligence (AI) has played a transformative role in chemical research, greatly facilitating the prediction of small molecule properties, simulation of catalytic processes, and material design. These advances are driven by increases in computing power, open source machine learning frameworks, and extensive chemical datasets. However, a persistent challenge is the limited amount of high-quality real-world data, while models calculated based on large amounts of theoretical data are often costly and difficult to deploy, which hinders the applicability of AI models in real-world scenarios. In this study, we enhance the prediction of solute-solvent properties by proposing a novel sample selection method: the iterative core subset extraction (CSIE) framework. CSIE iteratively updates the core sample subset based on information gain to remove redundant features in theoretical data and optimize the performance of the model on real chemical datasets. Furthermore, we introduce an asymmetric molecular interaction graph neural network (AMGNN) that combines positional information and bidirectional edge connections to simulate real-world chemical reaction scenarios to better capture solute-solvent interactions. Experimental results show that our method can accurately extract the core subset and improve the prediction accuracy.

Bridging the Gap Between Cross-Domain Theory and Practical Application: A Case Study on Molecular Dissolution
Sihan Wang Qing Zhu Wenjie D2,4,, Yang Wang 1.2.4 University of Science and Technology of China, China Suzhoo Institute for Advanced Research, USTC, China Hefei National Research Center for Physical Sciences at the Microscale State Key Laboratory of Precision and Intelligent Chemistry, USTC
s sihanwangdmall. wusttngmmmll.ut.......mmmm edu. {duwejjie, qingzhu, angyan)Qustc. edu.
Abstract
Artificial intelligence (AI) has played transformative role in chemical research, greatly facilitating the prediction of small molecule properties, simulation of cat- alytic processes, and material design. These advances are driven by increases computing power, open source machine learning frameworks, and extensive chemi- cal datasets. However, a persistent challenge is the limited amount of high-quality real-world data, while models calculated based on large amounts of theoretical data are often costly and difficult to deploy, which hinders the applicability Al models in practical scenarios. In this study, enhance the prediction of solute- solvent properties by proposing a novel sample selection method: Core Subset Iterative Extraction (CSIE). CSIE iteratively updates the sample subset based on information gain to remove redundant samples theoretical data and optimize the performance of the model real chemical datasets. Furthermore, intro- duce an asymmetric molecular interaction graph neural network (AMGNN) that combines positional information and bidirectional edge connections simulate real-world chemical reaction scenarios to better capture solute-solvent interactions. Experimental results show that our method accurately extract the core subset and improve the prediction accuracy. Code is available at: hutps:/CISE-AMGNN
Introduction
Artificial intelligence (AI) has emerged pivotal tool in advancing chemical research 22 13 51
In recent years, Al has demonstrated transformative potential across various domains, including prediction of small molecule properties [65, g the simulation of catalytic processes [18
68 and the
design and property prediction of materials such as metal-organic frameworks (MOFs)
(3) This
progress has been largely fueled by significant advancements in computational power, the proliferation of open-soe open-source machine learning frameworks, and the expansion of chemical datasets [8 [6 3)
The impact of Al on these areas is evident in the rapid growth of related studies and publications.
Despite rapid advances in AL-driven chemistry, fundamental bottleneck remains the scarcity of high-quality experimental data. Most progress still relies on computational or simulation based datasets 22 25), which, while invaluable for method development, often fail to capture the full
complexity and variability of practical systems. Large-scale repositories such as GDB-17 (54] and FDB-17 17 (4 are generated under idealized rules of chemical stability and synthetic feasibility,
corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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neglecting kinetic effects, environmental perturbations, measurement uncertainty. and procedural variation [5]. To mitigate this gap, recent studies have sought improve generalization under unseen conditions by increasing model capacity or expanding data coverage. For example, Goh et al. [23 employed weakly supervised training on large unlabeled chemical databases combined with transfer learning, while Wang et al. [66] infused chemical reaction information into representation learning.
However, these strategies typically demand ever larger model architectures and datasets, incurring prohibitive computational and storage costs that impede deployment in resource-limited environments. To address both the burden of large-scale training and the challenge of uneven data distributions, researchers in computer vision and natural language processing have developed an alternative paradigm: core subset selection. This approach seeks identify a small but highly informative subset of the training data, so as to reduce training time and mitigate class or feature space imbalances By focusing on the most representative or hard examples, core subset selection methods can both accelerate convergence and ensure that under-represented regions of the data manifold receive sufficient attention during learning.
without substantially degrading performance 26 57
58 50
When naively transferred to molecular chemistry, however, existing core subset selection methods exhibit two major shortcomings: OSelection criteria based on loss, uncertainty, or feature embeddings learned earlier by the downstream task models tend to overemphasize rare or difficult samples or conversely overlook subtle but chemically important patterns, thereby skewing the training distri- bution and degrading performance in mnder-sampled regions of chemical space [28] 32
Many algorithms require repeated evaluation of all large fraction of samples after each training epoch to update their selection scores, introducing substantial overhead that scales poorly with dataset size, especially high-throughput molecular screening campaigns (74.. These limitations motivate the development of a chemistry-specits core subset selection framework. Such framework should be capable of balancing the molecular feature distribution while also minimizing computational costs.
To address the practical limitations of AI- driven chemistry, we identify two complemen- tary challenges: (i) theoretical datasets are large in scale but often noisy, redundant, and imbalanced; (ii) experimental datasets, while high in quality and critical for real-world ap- plications, are limited in size and coverage. These challenges jointly motivate two-stage solution, illustrated through the case study of dissolution free energy prediction.
Methed based on Transfer Leurning
based CISE
Cene Saleet
Methed based on Practical Therey
Real Worid
Aud Halld
In the first stage, we develop Core Subset Iter- ative Extraction (CSIE), principled sample selection framework designed to reduce redun- dancy and improve distributional balance in theoretical datasets. Within the submodular optimization framework [21), CSIE quantifies
Figure Comparison of methods to assist in bridg- ing the gap between theoretical and practical envi- ronments.
the value of each sample via information gain, measuring its marginal reduction of uncertainty or entropy (58) To mitigate the noise and bias inherent in one-shot greedy strategies (20 27). CSIE
adopts an EM-inspired iterative refinement process [40), dynamically re-evaluating sample contribu- tions and progressively approaching globally optimal subset. For reliable estimation, solute and solvent molecules are independently encoded with a chemical language model, and their embeddings combined to represent molecular pairs, ensuring efficiency without invoking downstream task models.
Building on the CSIE-selected core subset, we then fine-tune with scarce but reliable experimental data using the Asymmetric Molecular Graph Neural Network (AMGNN). Unlike conventional GNNs, AMGNN explicitly models the directional nature of bidirectional edges and tailored message-passinn schemes. Extensive experiments demonstrate that the integrated CSIE+AMGNN approach significantly improves dissolution free energy prediction and generalizes well to other physical chemistry tasks, such as material band-gap prediction in Appendix E
interactions through distinct
Our contributions can be summarized as follows:

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Core Subset Extraction
AMGNN
GNN Encoder
AG govv
CSIE
Initial Dataset Core Subset
Asymmetric Graph
Molecular Representation
CSIE Framework
Algorithm
GNN Encoder
Concat
Encoder
Initial Dataset
Step
Step 2
Step n
Figure 2: Model framework: For given solute-solvent dataset, the CSIE framework first extracts the core subset of the dataset (warm points with larger information gain belong to the core subset), and then AMGNN is trained on the core subset predict the AG
CSIE Framework: We propose an iterative submodular- -based core subset selection method that maximizes information gain reduce redundancy and deviation in theoretical datasets.
AMGNN Architecture: We introduce an asymmetric molecular graph neural network to better
simulate directional solute-solvent interactions under realistic chemical conditions. Comprehensive Validation: We conduct extensive experiments and visualization analyses to demonstrate the performance and interpretability of our method chemical property prediction.
2
Method
In this section, we introduce the CSIE framework and the AMGNN model: For given solute-solvent molecule dataset, in Section 2. we use the large model to obtain multimodal representation of the sample data, thereby providing basis for CSIE to calculate information gain. In Section 2.2 we describe in detail the process of extracting the core subset of the theoretical dataset using the CSIE framework. Finally, Section 2.3 we explain how AMGNN simulates real-world solute-solvent interactions predict AG of the solubility reaction.
2.1 Construction of Molecular Representation
Given that many solute and solvent molecules in the dataset share similar SMILES representations yet exhibit significant differences in molecular properties, structural similarity alone is insufficient for accurate sample differentiation. For example, the SMILES of acetic acid is CC(=0) while that of ethanol is CCO. Although they differ by only one oxygen atom in their 2D molecular graphs, their behaviors as solvents are markedly distinct.
To address this, we incorporate the multimodal molecular representation model MolCA 34], which integrates topological and chemical semantic information to generate more expressive embeddings. Molecular text descriptions are primarily sourced from PubChem (30), with additional augmentation from GPT-4 Omni (GPT-4o) (.. By capturing subtle distinctions between structurally similar molecules, multimodal embeddings substantially improve the accuracy information gain estimation in the CSIE core subset selection process.
As shown in Figure 2 given an input set composed of the smiles and text description of the solute- solvent pairs D [[sooventt:: (solventl; Isolutel, (solutel), J. it will be encoded by the MolCA: Dsolute, 6 (gsotute.) || di (t
(1)
Isoluent, Φ (goatvent,) || de (tsotvents):
3

---

Here, || represents the concatenation operation of vectors, grotute; is preliminary graph vector repre- sentation obtained from the smiles of solute, and T solute, is preliminary word vector representation encoded from the textual description of solute,, (-(+) and (x(-) are respectively the graph encoder network and text encoder network of MolCA.
Then we perform a concatenation operation to obtain the embedded representation of the solute- solvent pair x; by concat the two vectors in Equation
2.2 CSIE Framework
Samples with high information gain often correspond to examples near the decision boundary, defined as m(x) = y- (w (w)x)) According to Sorscher et al. [58], when the expected information gain (xG(=)] of the retained samples exceeds the minimum density threshold e = inf E[G(z)), the generalization error decreases exponentially, following e(C,) oc e-c,, where C, the core subset size and r is a constant. Furthermore, the resource cost grows only logarithmically with respect to the desired error, i... & in(e/e), enabling efficient error reduction with limited computational overhead.
In CSIE, we estimate the information gain each sample as the similarity measure between its embedding representation and surrounding samples, and use this to determine whether it should be retained in the dataset. To efficiently compute similarities among samples and facilitate gain estimation, we employ the Hierarchical Navigable Small World (HNSW) algorithm [36], which allows for scalable and approximate nearest neighbor search in high-dimensional spaces.
For the hierarchical index graph, we use the number of nodes in the insertion layer as the number of latent categories: C₁ = = + 1. Latent classes can be viewed as classifications of nodes at different levels of topology. Assume that when node z; inserted into the graph, its level L, is determined by a random process. The node insertion probability p; (x,) of each level Lj can be simulated using parameterized Bernoulli process:
8= P(x: E (,)
C2
2z =
e
8u
(2)
Zj
k=
Among them, B, is the attenuation factor related the level Lj. and the normalization factor Z, is determined by the expected value.
Information gain can be modeled from the perspective of information entropy. First, define the prior information entropy H (x;)) of node x; before insertion and the posterior information entropy H (z)[G) after insertion, where G is the graph structure formed during the insertion process. Information gain G(x,)) can be measured by the difference between the two:
G(x²) = H(x) -
= - Σ P(H log P(H.))
(3)
1=
where P(H.g) is the probability distribution of the implicit category of node at level L.g. and H represents the category label of node z, at level Ly. Here, we can use the average cosine distance from z; to its neighboring nodes an explicit information gain measure. The specific proof can be found in the Appendix B
From the Equation (3), it can be inferred that these pairs tend to have similar feature encoding to their surrounding pairs. Thus, these paris are often not worth adding the dataset.
Consider the sample set X' = {x1, T2....... .......,, xN\ C Rd, where each sample x; follows an unknown distribution P(x). Our goal is to select the core subset C, from X' such that its size satisfies:
Cc.| = N7,
8 E (0.1)
The core subset C, contains the samples with the top s% information gain, aiming to maximize the contribution to the overall data structure. Since the randomness of the sample insertion order may introduce noise and bias, we draw the idea of EM algorithm to optimize the selection of the core subset, aiming eliminate the deviation of the insertion order. The two-step iteration steps are as follows:

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In the E-step of the t-th iteration, we select the first k% high information gain samples from the current core subset (1)) the unbiased core subset C
C arg max G(,))
(5)
CCC(! [0]=[ks-N]]
#:EC
In the M-step, we first insert c into the HNSW structure, and then randomly insert the remaining sample set X' \ C and recalculate the information gain of all samples:
(x:)
[N(C+))
Σ
disl(x;, +
(6)
3y (EE
The new core subset is updated by maximizing the information gain:
c(++1) = arg max
Σ G(++1)
CCX x N.
R.EC
The algorithm ensures the monotonically non-decreasing information gain in each iteration:
Σ
G Z Σ (x:)
(8)
2:Ec(
Z:EC!!
The iteration converges when the core subset satisfies the following conditions:
| cc
<
(9)
Among them, 8 is the preset tolerance threshold, and || Ilo represents the change in the number of collection elements. The proof the iteration is shown in the Appendix C The core subset ratio and EM algorithm threshold are given in the Appendix E.1
Through this design, our method exhibits properties similar to submodular function (X C
f(x x) 2 (xr Y)) [21]. Here, f is set function mapping from the power set of set Q to the real number set R. X and Y are sets that belongs f2 and x satisfies x E 52 Y. In this way, proved in Appendix D Our method satisfies the marginal diminishing effect of the submodular function, which means that it can select the sample set with the highest gain. Due to the characteristics the HNSW algorithm, it can screen and update samples with time complexity of (log(n)).
2.3 The AMGNN to Predict AG
After sample selection by CSIE method, we proposed an asymmetric merged graph and message passing graph neural network to predict AG
We noticed that previous methods often learn chemical reaction behaviors based on solute solvent molecular graphs or merge graphs, which makes it difficult to distinguish between the two situations where molecule dissolves in molecule and molecule dissolves in molecule a, because the merge graphs constructed by the two are exactly the same. Therefore, we made a modification: when constructing the merge graph, we chose add bidirectional directed edges with learnable weights between the topological nodes of the solute and solvent. In this way, during the message passing process, the model solvent to solute. So that the situations we mentioned above can be distinguished. Specifically, our model AMGNN is follows:
distinguish between the message passing from solute to solvent and from
We use an asymmetric merged graph G to represent the solvent topology and solute topology connected by directed edges:
g = (R,
(10)
Here, E is the undirected edge of the solute molecule and solvent molecule, V represents all nodes of in the molecule pair, 24 is the global vector representation of the molecule pair, and R the set of directed edges between solute and solvent molecule:
R {(TK, a, by) 2xxN" xxxx*>>''" N
(11)
5

---

where i E f1, N. E (1.2,3...., N and No are the total number of atoms in each of the two molecules. As mentioned above, in order distinguish between solvent and solute molecules in the merged graph, we constructed this new type of edge as directed edges Faj from a; to by and directed edges rj from by to a;-
Next, following the approach of previous research, we will first perform message transmission and feature updates within the molecule:
o(ey)) e = Eij + T|F[(1 + u3) + FC((e))) + FC()), Eig EyEN, d(ey) +e'
(12)
of = + TFC +
Eij) о FC(U,) + FC(u), Σ
JEN,
where e; and Eij denote the node and edge vectors belonging V and E, respectively. -(-) and a()) are activation functions, FC(.) represents a fully connected layer, @ denotes element-wise multiplication, and is fixed constant.
The next step will be the process of intermolecular information transfer. Compared to previous research, when constructing merged graphs, we add large number of connecting edges between molecules, which may make message passing and node updates too complex and result in over smoothing. To address this issue, introduced a mask vector . {mz/mm m. {0, 1 during node updates to randomly mask out some edges between molecules:
N* x
r = 31 T[FC(O(Vaa: (bz)) v')) + (1 - B1) Tij.
ry = B2 r [FFC(O(16)) U/i m/yy] + (1 - B2) Tjj.
w = (1 - B3) v'ae + my * A3 T FO(T';))l,
(13)
JEN,
bb (1 -a) 1/6 + m,j B4
Σ FO(r()))
where 32, $3, 31 are learnable parameters that control the update rate of information, which makes information differentiated avoid information decay or overwrite the original information. Vaa represents the atomic feature vector in the solvent molecule updated by Equation (12) in the merged molecular graph, while v'a represents the atomic feature vector in the solute molecule. used to measures the similarity between atoms.
Finally, we update the global feature vector u.
N
N
7f = F=(C0( "(y),, v' u'=u+ NO
% + w)]
(14)
N
k=
where No and N are the number of nodes and edges respectively.
Repeat the above update process until obtaining the final merged molecular feature representation uy- Then MLP predict predict the AGsoto:
AG MLP(uy).
(15)
And the expression of the loss function is:
== D (AG Y).
(16)
where D denotes the distance function, Y is the label value given by the dataset.
3 Experimental Results
In this section, we conduct extensive experiments answer the following questions:
RQI: Can CSIE and AMGNN make up for the difference between theoretical data and real data?
RQ2: Why can CSIE and AMGNN achieve outstanding results with few samples?
RQ3: Can CSIE also enhance model performance beyond cross-dataset generalization?

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3.1 Experimental Setup
Datasets. The experimental dataset utilized in this study is the CombiSolv-Exp database, which is compiled by Vermeire and Green [62], and combined the experimental data from multiple sources. And the FreeSoly database is published by Mobley and Guthrie [43], the CompSol database from Moine et al. [45]. MNSSo from Marenich al. [37] and remaining dataset is Abraham collected by
the Abraham group 24. The theoretical dataset (CombiSolv-ou) -Molll---MMM comes from Vermeire et al. 62 which is computerd by the commercial software COSMOtherm.
Baseline. To fully validate the effectiveness of our proposed CSIE, we conducted experiments on various models. They are D-MPNN [63]. SolvBert 770,, SMD [41], Explainable GNN [35], GAT (61, GROVER (52). Uni-Mol 76), Gem [19] which use simple combinations of solute or solvent characterizations without intermolecular messaging. CIGIN 47. CGIB (3] and MMGNN 17 take
into account intermolecular messaging and interactions.
Experimental settings. The model is trained using the Adam optimizer B1 (1e - + 0.5) via batch
size 50. We employ mean squared error (MSE) as our evaluation indicators. The training process was halted if the validation MSE stop reducing in 150 epochs, or the maximum training limit of 500 epochs was reached. It is implemented using PyTorch via Tesla A100 80GB.
Evaluation Metrics. Experiment results are presented in following metrics: the mean absolute error (MAE) and root mean squared error (RMSE). We repeated the training eight times. The mean and standard deviation are recorded.
3.2 Generalization Performance From Theoretical Dataset To Experimental Dataset (RQ1)
A top-1% % core subset QM-mini (same size as CombiSolv-Exxp See the Appendix E.5.2 for more experiments) of the theoretical dataset CombiSoly -M is selected through the CSTE framework. Referring to previous studies, we compare the performance differences of AMGNN and other baselines on the same test set two situations: (1) directly train in CombiSolv-Exxp (2) train in QM- mini and then transfer to CombiSolv-Exp. In Table I we show the results of randomly partitioning CombiSolv-Exp Table 2 and Table 3 show the impact of CSIE on the generalization of the model under solvent-split and solute-split. Based these outcomes, can delincate three key observations:
Obs.1: CSIE facilitates the migration of theo- retical data experimental data. As shown in [able the performance of the model pretrained on QM-mini and then transferred to CombiSoly- Exp has been improved to certain extent and the performance
Table 1: Performance comparison on the test set between models trained directly on CombiSolv- Exp and those transferred after training on core subset QM-mini from theoretical CombiSolv-QM.
ComtiSee Exp :oomhiSoly Exp unti
:ConnSSWy Exp
more stable, even exceed- ing the results of previous work [62] using all CombiSolv- QM data for training and migration. Taking the MAE indicator as example, mul- tiple models such as AMGNN have achieved performance improvements of more than 10%. This fully demonstrates the effectiveness of the CSIE framework. Meanwhile, we give the sig- nificance analysis results of Table [ in the Ap- pendix E.
MAR(()
RM)
MAF(I]
RMSF(
D-MPNN
0.450
6.00
0613
Explaisskie GNN
0.221
84444
0.183
0.364
Sobitifkir
0.7
0.341/a11)
(477)
GAT
0.900)ee1)
12200mmm
0883
0,382
[.99]
0.331aam00
SMD
1.23
0.577
0.576
Un--Md
0.375juam
Gem
0,253
0,21aam
CIGIN
0.24]
B4ilaomn)
6.29..mm
Gdllggem
OGill
0.223
0315
MMGNN
0.218
62000amm)
AMGNN
0.314
(.367
6.191
Obs.2: CSIE improves the generalization performance of the model. As shown in Table 2 and Table the performance of the model using CSIE-assisted migration did not show a significant decline in both solvent partitioning and solute partitioning, and was significantly better than the model trained directly CombiSolv-Exp. especially for some molecules with relatively high specific gravity, such as solvent water and Element O.
Obs.3: Our AMGNN shows the best performance in various settings. Taking Random-split as an example, the transfer learning performance of AMGNN improves MAE 4.5% and RMSE 5.6% compared with the sota model MMGNN. Similar improvements were achieved under solvent-split and solute-split. This demonstrates the excellent ability of AMGNN to simulate asymmetric dissolution reactions in the real- world systems.
7

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Table 2: Comparison of the experimental results under solvent-split. The evaluation metric is MAE.
Confeoot tog ConbiSch-llip
UM-mini-
ComfeSol-ttag
Sotvent
GAT
Eaphonelt: CNN
CGIB
MMGNN
AMONN
GAT
Explattshi GNN
OCill
MMGNN
AMGNN
Testsiiee
Nottone
(u305
0.234
6.352
(22
0.24 Eu.
0,205
0.192
Acttontnte
0.592
0.401
(100)
0.39
0.22
0.238
0.4 0241
p.20
638
n 01700om
0.188
45
DMS0
122770054
a997icuno
0.955
G36
16630001
0.700
0855
0.802
0.798
6.50
0:255 eno
0227
0.228
Octond
0482
0.35
44Hom0
0.325
0:391
THF
0.40
0453
04
6413
0433
0.413
0.387
0.361
116
Water
2.584
1.92
1517
1.512
1153
Hocane
0.232xx114)
(498)
0.148
04410gaas))
0123pmm
0.13
Table 3: The experimental results of solute splitting are compared according to the element type. The evaluation metric MAE,
OM-nii--Coiiisd--BB
Element
GAT
Peptainable
eGiB
MMGNN
AMGNN
GAT
Peplaineii GNN
CGB
MMGNK
AMONN
size
Bc
0.987a
0.80
0.790uamm)
(.632))23))
0.85 mer)
0.790
0.601 y
0.514
152
CI
2,04
1696
1.25
1.27
1
1.244
1
63
1058
1:00 sao
103 mm
1007
1056 eso
g mx3)
0.82 cm0
0.812
0.56%
0.419 60.0
0 603
N
1230
1.774
1306 1.512
1.105
1159
0912
1192
0
2.281
2 2.18%
2.118 1890
2.087 mooes
g,35
1.491
1250
1796
1003
(106
0.19)uza)
(.755
1822
0.741
(1699)0000
0,573 exr)
3.3 Evaluation of The CSIE (RQ2)
To further explore the nature of CSIE, we mapped the features of the samples in CombiSolv-Exp and used the UMAP (39] dimensionality reduction tool to divide them according to solvent type. In F we intuitively and clearly show the screening effect of CSIE on samples. shows the results of the ablation experiment: For more intuitive and clear presentation, we do not take transfer learning as an example, but directly perform sample selection, training, and verification on the CombiSolv-Exp dataset to demonstrate the effectiveness of the CSIE framework and AMGNN.
Obs.4: CSIE removes redundancy by screening samples by category. As shown in Figure. 3 (a). the samples are distinguished by solvent type, and the dark and light tones indicate whether they are in the core subset screened by the CSIE framework. It can be seen that CSIE has achieved the goal of removing redundant samples while ensuring the quality of the dataset by evenly selecting samples of each category.
Obs.5: CSIE can balance the distribution of the dataset. In Figure (b) and Figure (c), we show the proportion of each type of sample before and after the core subset selection by CSIE. can be clearly seen that through CSIE, the proportion of samples such as 'Aromatics' and 'Halogens', which accounted for a relatively small proportion in the original dataset, has been significantly improved in the core subset, while the proportion of IIntra molecular H bonding', which originally had large number, has decreased by nearly 10%. This further illustrates that the essence of CSIE is to improve the quality of the dataset by balancing the distribution of various samples in the dataset.
(8)
(b)
UMAP-2
Aceeatics
mww.euaraaatt.. bonding
15.1%
7.4%
6
O&N.N comariig
Water
10 7%4
18 7%
8
10
UMAP-1
Figure 3: Sample selection and distribution visualization. (a) UMAP projection of solvent molecular embeddings, with lighter points for the full set and darker points for selected sam- ples. (b) Original sample category distribu- tion. (c) Sample category distribution after selection.
Obs.6: The samples selected by CSIE are more helpful for training, while the asymmetric network can better simulate the dissolution reaction. As shown Table under the w/o CSIE setting, the use of random sampling leads to a huge drop in model performance, especially in the case of few samples. The w/o EM algorithm also degrades the quality of the training set. The model performance also suffers slightly under the w/o Asymmetric network setting using undirected edge modeling.
8

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Table 4: Ablation experiments are performed on core subsets of different sizes in CombiSolv-Exp to explore the effect of the CISE framework on sample extraction.
Methods
20% Trainset
40% Trainset
60% Trainset
80% Trainset
MAR
RMSF ())
MAR()
RMSE (1)
MAR
RMSE(U)
(AF
RMSE (J)
wio CISE
1.466 w..en
1.711 1039
0.921 g. (.039/
(o579)
0.391 (.654
0.446..000)
wlo EM algorithu
1.125 1o.25
1.431
aazyy
0.402
0.571 (o.000
0.418,..ee) (.34)
wlo Asymmetric network
0.545)
0.733.0.02
0,427 g. 0.450)
Our Method (MGNN)
D. (.777)
0.81 1 (0009)
0.511
0.350
0.247
D. 261 (0.030)
0.31 0,313
0.570
0.8
-O- K-Merr
-On Magie
66
(b)
K Mobrr
heel Hartor
(c)
Marg'
Gamer Samplly
Micit
Hanndas
20
(d)
-O- Moan
Negh
0.0
Systewaoo Sampling CIFE
Sycneolo Sompling CRd
-O- Ci9
-O- Gide
044
0.
0.
0.2
0.5
06
12
0.4
0.5
06
1.0
0.3
0.2
0.5
0.
5.
1.5
02
0.8
0.
Obez Vi0e
Car sutset Aec
Obez subr
Cono subodd
Figure 4: Model performance using various sampling methods under different datasets. (a), (b), (c), and (d) represent the experimental results on the Abraham, Compsol, Freesolv, and MNsol datasets.
3.4 Performance of CSIE Within The Experimental Dataset (RQ3)
To further explore the universality of the CSIE framework, we trained and tested on four datasets: FreeSolv, MNSol, Abraham, and CompSol. The datasets were randomly divided, and under the premise of keeping the validation set and test set unchanged, we used the CSIE framework and other dataset pruning methods, including Hard Random, Systematic Sampling (69, K-Means (2). Margin [1]. Glister [56). to select core subsets of different orders of magnitude for actual training. Figure 4 shows the performance of the model trained on train datasets with different data volumes.
Obs.7: At the same order of magnitude, the core subset extracted by CSIE is more effective. From Figure 4 we can see that no matter which dataset or sample size, the model trained with the core subset selected by CSIE outperforms other methods. Our method is far ahead of baseline methods such as random sampling, and only systematic sampling methods are close (this is due to the data arrangement of the dataset itself). This demonstrates the effectiveness of our proposed CSIE in sample selection and its robustness on various datasets.
Obs.8: CSIE achieves similar performance with fewer samples. For Abraham (Figure (a)) and MNSol (Figure 4 (d)), the model can achieve performance close that of using all samples using only 80% of the samples. On the CompSol (Figure (b)) the performance of training with 80% of the samples even exceeds that of training with all the samples. This is because by iterative core subset selection, CSIE removes some redundant samples that are similar in feature space, effectively avoiding overfitting of the model to certain type of samples and improving the generalization ability of the model.
Obs.9: CSIE works well on datasets with more uniform distribution of categories. The Freesolv dataset consists of pairs of molecules with different solutes but water as the solvent. Such samples are naturally close in feature space. As can be seen from Figure ( (c), although the model performance slightly decreases when 80% of the dataset is used, when the training samples are reduced to 60%, the model performance increases instead of decreasing. It indicates that the core subset selected by CSIE effectively reduces the redundant information in the dataset by removing solute molecules with similar properties.
4 Conclusion
In the field of physical chemistry, the relationship between theoretical calculations and the real world has always been a difficult problem. This study proposes core subset iterative extraction (CSIE) framework and an asymmetric molecular interaction graph neural network (AMGNN). CSIE effectively extracts core subsets of theoretical data in a lightweight way, while AMGNN simulates

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real molecular reactions through asymmetric connection relationships. Through core subsets and transfer learning techniques, the model can be easily deployed. while expanding the sample space and thus enhancing the model's prediction ability, while balancing computational efficiency, thereby achieving leap from theoretical data to real experimental results. We verified the effectiveness and interpretability of the proposed method through various experiments.
Acknowledgement
This paper partially supported by the National Natural Science Foundation of China (N2.7227701) The Al-driven experiments, simulations and model training were performed on the robotic Al-Scientist platform of Chinese Academy of Sciences., Anhui Science Foundation for Distinguished Young Scholars (No.1908085124, Natural Science Foundation of China (No.62502491).
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A Related work
A.1 Molecular Relational Learning
Molecular relationship learning is typically achieved using graph neural networks (GNNs), with most of them relying on the atomic features within a molecule. For instance, Behler and Parrinello utilized neural networks to model atomic interactions within molecules 66 7 I0 9. Additionally, recent
work 449 has combined passsag-passing and attention mechanisms with GNNs to model molecular representations for predicting solvation free energy. Another framework [3 leverages the graph information bottleneck theory to predict molecular interactions. More sophisticated methods have introduced atom-pair symmetry message-passinn functions, which aim to enhance the representation of interatomic interactions and improve the accuracy of molecular behavior predictions
42
A.2 Discussion on Graph Neural Network
Graph neural networks (GNNs) 55 have shown strong expressive power in molecular relationship modeling and have been widely used to predict molecular properties, reaction behavior, and solubility free energy. Traditional GNNs model the interactions between atoms and bonds through message passing between nodes to extract the structural and chemical characteristics of molecules. Typical models such as D-MPNN [33] and GAT [6]] use this method to complete the representation learning of molecular graphs. Models such as MMGNN [T7), CGIB [33] and IGIB [75] try to innovate in modeling the interaction between molecular pairs.
However, in actual chemical systems, there are asymmetric and diverse relationships between molecules (such as solutes and solvents). Traditional GNNs are limited in their ability to model such heterogeneous information. To this end, heterogeneous graph neural networks (HetGNNs) 722 provide an effective solution. HetGNN expands the modeling capabilities of GNN, introduces multiple types of nodes (such as molecules of different categories) and edges (such as hydrogen bonds, hydrophobic interactions, etc.), and supports type-based message passing mechanisms. However, in the field of molecular relationship learning such as dissolution reaction prediction or drug interaction prediction, there is no mature solution for heterogeneous graph construction.
A.3 From Theoretical Data to Real World
Several approaches have been proposed to bridge the gap between theoretical data and real-world applications. One approach involves training on large-scale theoretical datasets and then using transfer
learning techniques to fine-tune the model on smaller experimental datasets (3 63 73 Another
strategy focuses on introducing more realistic data processing or modeling methods 7 14 [55.
For instance, Mohapatra al. 44 introduced more complex topological structures to capture the similarities among macromolecules, while Wang et al. [66] incorporated chemical reactions into the learning process. Additionally, Stuyver et al. [59] explored the use of quantum mechanics to enhance representations. However, the former requires training on large amount of theoretical data, which increases the difficulty of model deployment, while the latter increases the complexity of the model and the constraints may not be comprehensive.
B PROOFS of Information Gain
B.1 DEFINITION OF INFORMATION GAIN
Information gain quantifies the change in the system's information content before and after the insertion of a node into the graph structure. In the context of HNSW, the insertion of node x; affects the distances between the node and its neighboring nodes, which in turn impacts the system's 's entropy. Before the insertion of node x,, the distance distribution between each node x, and x; is denoted by d(x,, x)). and the entropy H (X After the insertion, the distances change, and the new entropy is H (Xatte). Therefore, the information gain G(x;) can be defined as:
G(x;) = H ( H ([aaaee))
This equation shows that the information gain corresponds to the change entropy before and after the insertion of the node.
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B.2 GRAPH STRUCTURE CHANGES AFTER NODE INSERTION
In the HNSW algorithm, when node x, inserted, is connected to several neighboring nodes through the multi-level graph structure. These neighboring nodes influence the overall structure of the graph, particularly the distances between the new node and its neighbors. After the insertion, the distances between all nodes in the graph will change, affecting the overall entropy of the system.
Let Let.V((x) (>,)) {y1, Y2....... Ym)) represent the set of neighboring nodes of node X,. The distance between node x; and its j-th neighbor y, is given by y,). The average distance between node x; and its neighbors
m Σ y() m j=1
d(x,)
(17)
where d(x;,)) represents the distance between node x; and its j-th neighbor Yj-
where || denotes the Euclidean norm (i.e., the standard vector length). Alternatively, using cosine distance, the distance between nodes x; and yj is:
X; y,
(x;
(18)
xx y
where represents the dot product, and ||x;|| and ||yا| are the norms of the vectors x; and yj. respectively.
B.3 RELATIONSHIP BETWEEN INFORMATION GAIN AND AVERAGE DISTANCE
The insertion of node influences the average distances between nodes in the graph. Therefore, the information gain is related to the change in the average distance between node and its neighbors before and after the insertion.
Let dixfore (x:) denote the average distance from node x, to its neighbors before insertion, and let dater (X:) denote the average distance after insertion. Then, the information gain G(x,) can be expressed as:
G(x;) = doetcre (x,) (x;)
(19)
Thus, the information gain is directly related to the change in the average distance between node x; and its neighbors.
B.4 GEOMETRIC INTERPRETATION OF INFORMATION GAIN
From geometric perspective. the insertion node x, changes the structure of the graph by modifying the distances between nodes. Information gain quantifies the impact of this change in distance. If, after insertion, the distances between node x; and its neighbors decrease (indicating tighter connectivity). the information gain is likely be higher. Conversely, if the distances increase, the information gain will be smaller.
C PROOFS of the EM algorithm
C.1 Proof based on monotonicity
The goal to find the core subset C, of the dataset that accounts for 5% and maximize the function of information gain:
I(C.) log C.) = log Σ p(Cu.
(20)
C.
Introduce the auxiliary distribution (CC.) and use Jensen's inequality to construct the lower bound:
p(C,. C.)
Σ (CCC log p(C,, C.)
L(C.) = log Σ (CC,)
(21)
((Cu)
((C.)
C,
Define the Q function:
Q(c." = B c. [log c(O)
(22)
The two steps of the EM algorithm are:
16

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E-step: Take the first 4% samples of the core subset C. ((t-1) calculated in the previous M steps as the unbiased core subset c(t)
M-step: First insert " into HNSW, then randomly insert the remaining samples and calculate the information gain to determine the new core subset C.
Due to the maximization of Q function:
Q(c! 2 Q(C (((-))
(23)
This results in monotonicity:
L(C!o) 2 L(C
(24)
Because L(C.) has an upper bound, the EM algorithm converges.
C.2 Proof based on KL divergence
Define the posterior distribution of the unbiased core subset:
p(Cu: C.)
p(C,IC,) p(C.)
(25)
KL divergence is expressed as:
(xa(((Cu)) (I P(GICC)) = ((C))) log ((C,)
(26)
C. C(CulCo)
Decompose the objective function:
L(C,) Q(C,lc(--)) + DKL(g(Cu) |l p(C.).))
(27)
E-step: Choose ((C,) = p(C,)c!(-1) to minimize the KL divergence. M-setp: Maximize
Q(C, Q(C,Ic 1/ thus ensuring:
L(C!") 2 L(Cht-))
(28)
Since the KI. divergence is non-negative, the algorithm converges to a local optimum.
D PROOFS of the Submodular Function
In this section, we provide proof details of how data cleaning methods satisfy the representation and properties of submodular function. In section D.I elaborate the representation and properties of submodular function, and define the functional representation of data cleaning method fde- In
section D.2 D33 and DD.4 we respectively prove that fae satisfies three different properties of the submodular function.
D.1 THE FORM AND PROPERTYS OF THE SUBMODULAR FUNCTION
The form of the submodular function: The submodular function is set function defined power set of a finite set Ω. For each given subset, the submodular function returns a real number:
f 2 + R.
(29)
The submodular function should satisfy the following three properties:
(1) Property 1: For any X,Y C Q, C Y. then for all x E 2 \ Y, should satisfy:
f - (z)
(30)
(2) Property 2: For any S,T _ Ω, it should satisfy:
f(S) + f(T) > f(SUT) ((S
(31)
(3) Property 3: For any X _ Q F1, T2 f2 it should satisfy:
f(XU(z)) + f (22)) > f(XU (X).
(32)
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Next, based on Equation E..2 and the form of the submodular function, we further define the mapping method of fac- Given dataset D, fae maps it the sum of the information gains of all elements in D:
Σ Clzi) IEED.
(33)
where N₂ includes the k-nearest neighbors of in D. is fixed parameter given in advance, and the distance between x and its neighboring nodes is calculated based on the cosine similarity their feature embeddings.
D.2 DERIVATION PROOF OF THE SUBMODULAR FUNCTION'S PROPERTY 1
According to the definition given in Equation
33 can conclude that:
fde U
{x}) fde (Y)
Imoo' Ymol
mean
BENY |
[|8mooz]]
Imol' Pmol
mean(
| -llmmot
PENT
APEX
(34)
E
Tmat Qmal
[motl [l
qENY AgE
mean(
Σ
Fmot Tmol
[moill
[|
TENK
f(X (x)) (X).
here, NV represents the k-nearest neighbors the k-nearest neighbors in set Y and N represents the k-nearest neighbors the k-nearest neighbors in X. In the above derivation, the third step is based on a belief that for element x. if Y gives new nearest neighbors different from X, then it means that the neighbors are elements with a smaller distance from x. And only when NY ny Ø, the third step is to derive an equal sign.
D.3 DERIVATION PROOF OF THE SUBMODULAR FUNCTION'S PROPERTY 2
Before proving, we first define the set V to represent S N. Next, make the following deduction:
Sac(S) + Sao(T)
fa USVVV + fac(V
Sac(V) + Sac(S) V)v (av(V
(35)
> [ac(V)+ + Jac(S) V) + (VUUVV)
fac(V) (
(S + (SUT).
here, we the form fY) represent the sum of information gains that be obtained by adding elements from Y to the set X. That means we can overwrite fa) as fae (Y)). We provide explanation for the third step derivation:
demonstrated in [D.2] adding elements to
a subset of the set always yields greater benefits than adding elements to the set itself. Therefore,
[S V)v 2 (S V)vurv v and only when TV V an empty set, taking an equal sign, which also means T is a subset of S or vice versa.
D.4 DERIVATION PROOF OF THE SUBMODULAR FUNCTION'S PROPERTY 3
Similar to D.2 and D.3 we prove property 3:
((XU()))+/(((3)))x + +f(X)
(36)
f +f(x)
((XU(=))++(XUUpp)))
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Table Impact of different & values (with 5 5%) on information gain and EM convergence.
(8 5%)
0.3
0.5
0.6
0.7
0.8
Final Avg. Information Gain
0.92
0.91
0.91
0.88
0.84
0.80
EM Iteration Rounds
12
3
2
Table 6: Impact of different ò values (with fixed ls 0.5) on information gain and EM convergence.
(k: 0.5)
5%
10%
20%
30%
Final Avg. Information Gain
0.91
0.82
0.76
0.76
EM Iteration Rounds
3
2
1
E SUPPLEMENTARY DETAILS
E.1 HYPERPARAMETERS
This section mainly introduces some hyperparameter settings of the model. For the CISE framework, we set the number of neighbors to 4, the proportion of unbiased core subsets to core subsets k=50%, and the termination condition is 5% of the sum of the information gain of the core subsets in the previous round. For the AMGNN model, we set [WW_layer_mune hideen 1600= 1600 The remaining hyperparameters have been given in the experimental settings in the main text.
FC_ __mm3_
In addition, we supplemented the hyperparameter experiments of the core subset ratio & and the EM algorithm threshold 5.
CSIE is not highly sensitive the choice of l according Table 5 but setting is too low significantly increases the computational cost due to more EM iterations.
We also conducted experiments on the threshold hyperparameter 5 with fixed k 0.5, as shown can be observed that a smaller threshold yields higher information gain, but at the cost of more EM iterations. In contrast, increasing 8 reduces the number of iterations, but may lead to suboptimal convergence. Therefore, a moderate setting (c.g., 5% or 10%) offers a better trade-off between performance and efficiency.
in the Table
E.2 DATASET
In this section, we describe the datasets utilized in this study. The SMILES representations of the molecules are converted into graph structures using the Github code from CIGIN [48]. For datasets associated with solvation free energies, namely MNSol, FreeSolv. CompSol, Abraham, and CombiSolv- we adopt the SMILES- datasets provided by previous work [62]. Only solvation free energies measured temperature of 298K (=2) are considered, and ionic liquids and ionic solutes are excluded [62)
Abraham [24] is dataset compiled by the Abraham research group at University College London. We analyze 6, 091 combinations of 1, 038 solutes and 122 solvents as per the previous study [62].
CompSol 45 is designed to investigate how hydrogen- bonding interactions influence solvation energies. We consider 3, 548 combinations of 442 solutes and 259 solvents from this dataset, following the methodology in [62].
MNNSl (37 consists 3, 037 experimental solvation or transfer free energies of 790 distinct solutes and 92 solvents. For this work, we focus on 2, 275 combinations involving 372 unique solutes and 86 solvents, as done in prior research [62
FreeSolv 43 offers 643 experimental and calculated hydration free energies for small molecules in water. This study includes 560 experimental results based on the dataset from previous work [62].
CombiSolv-Exp 622 includes data from MNSol, FreeSolv, CompSol, and Abraham, amounting 10, 145 combinations of 1, 368 solutes and 291 solvents.
CombiSolv-QM [62] This dataset consists of million randomly selected combinations, in- corporating 284 frequently used solvents and 11,029 solutes. The elements present include
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Table 7: Atoms (nodes), bonds (edges), and global features for molecular representation
Atomic features (V)
Bond teatures (E)
Global teatures (U)
species
Bond type
Total of atoms
No. of bonds
Conjugated status
Total No. of bonds
No. bonded H atoons
Ring size
Molecular weight
Ring starus
Stereo-chemistry
Valence
Aromatic status
Hybridization type
Accepior status
Donor status
Partial charge
Table 8: MAE on the experimental band gap test set (52 GWVD samples) after pretraining core subsets of the theoretical PBE dataset (546 samples) and subsequent fine-tuning. "Direct" denotes models trained and tested on the experimental data.
Method
Core subset size (theoretical PBE)
Direct on experimental (no pretraining)
80%
40%
20%
Hard Random
0.5775(o.042)) 0..00)
0.5759
0.6673
K K-means
0.551 (o.00))
0.5451 (0.0457)
0.6161 (0u330)
0.6457 (0.0577
Glister
(..27))
(o.427)
(o.50)
0.5850
Margin
0.511 (000))
(0.004)))
0.5675 mase)
CSIF
0.4583 (o.008))
0.5002(0.0200)
0.5211 (0.0264)
0.4966
w/o Transfer
0.5784(a.061s)
H, B, C, P,P.P,,, and The molar mass of the solutes varies between 2.02 g/mol and 1776.89 g/mol.
E.3 BASELINE
This section primarily introduces several baseline models involved in the experiment.
D-MPNN 622. This model integrates quantum calculations with experimental data accuracy, utilizing
two databases, CombiSolv-QM and CombiSolv-Exp. to predict solvation free energy transfer learning.
SolvBert 770] It models colute-solvent interactions through combined SMILES representations. Pre-trained on computational solvation free energy datasets, it predicts experimental solvation free energy solubility by fine-tuning on specific datasets.
Explainable GNN 35 This model incorporates chemically intuitive solvation-related parameters, such as semi-empirical partial atomic charges and solvent dielectric constants, alongside standard atomic and bond-level features. It includes interaction layers that facilitate visualization of solubility- enhancing or -reducing interactions.
GAT 61 A novel neural network architecture designed for graph graph-structured data, leveraging masked self-attentn layers to overcome limitations of traditional graph convolution methods. By allowing nodes attend to neighboring features, it assigns varying importance different nodes without expensive matrix operations or prior structural knowledge.
Extracting rich structural information from extensive unlabeled molecular data, this model utilizes self-supervised tasks with a Transformer-style architecture combined with Message Passing Networks, facilitating efficient training on large-scale datasets to address data scarcity and bias.
GROVER [2
SMD Employing quantum charge density of solutes and continuum solvent representation, this approach divides solvation free energy into bulk electrostatic contributions (modeled via IEF-PCM) and short-range interactions within the solvation shell (modeled using atomic surface areas and coometry-depnnden constants).
Featuring two SB(3) Transformer-based pre-trained models, Uni-Mol is trained on 209 million molecular conformations and 3 million protein pocket data. It integrates various fine-tuning strategies for diverse downstream tasks.
Uni-Mol 76
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Table 9: The effect of transfer learning after obtaining the QM-mini dataset using different data pruning methods, the evaluation metric is MAE.
QM-mini- -CombiSall-Faa
Method
Explainable GNN
MMGNN
AMGNN
M-Meuss
(889)p.em)
0.470
0,51
0.482.o.100
0.4
Hard Random
1,433
0.352
0.503
0.477
0.44%
Margin
0.942)0009)
0.27 to 0003
0.410
(o3
(o.00
Glister
0.910)..us)
0.355
0.475
0.221
(..00)..005)
Systematic_
Systematic_Smmpling Sampling
0.951 (0.077)
(.255
(.247
0.26-10.002)
CSIB
(.833
0.19550.00)
0.21 (Q0025)
0.200l
(o.013)))
Table 10: Significance analysis of the effect of the CSIE framework different models, with RMSE as the evaluation metric.
model
GAT
GEM
CGIB
MMGNN
AMGNN
second-best
1.210(o.101)
0.551 (0.023)
0.381 (0.030)
(0.07(0.027)))
0.367 (0.015)
ours
0.960 (0.00))
0.499 (0.03))
0.355 025)
0.317 (0.022)
0.299
p-value
2 x 10
6 x 10
0.034
x 10
2 x 10
Utilizing a geometry-based graph neural network, Gem enhances learning with self- supervised strategies at the geometry level, acquiring comprehensive molecular geometry knowledge for accurate property prediction.
Gem 19].
CIGIN 47 This end-to-end framework consists of three phases: message passing, interaction, and prediction, culminating the final phase estimate solvation free energies.
CGIB B3 Based on graph conditional information bottleneck theory, CGIB extracts conditional subgraphs effectively model molecular interactions.
MMGNN [77. Specializing in atomic interactions such as hydrogen bonds, MMGNN initially forms indiscriminate connections between intermolecular atoms, which are refined using an attention-base aggregation method tailored to specific solute-solvent pairs.
E,4 The Detailed Features For Atoms, Bonds and Molecular Global
Table provides detailed summary of the chosen atom, bond, and global input features. The process begins with transforming the SMILES strings of both solute and solvent into graph structures using the RDKit library. This library is utilized not only for generating graphs but also for calculating atom and bond features for each graph. To reduce the computational overhead associated with quantum mechanical calculations across the entire dataset, the feature selection was limited to those obtainable via RDKit. To ensure uniformity in the lengths of the bond, atom, and global feature vectors, linear transformation is applied to each vector prior to initiating the message-passiing steps.
E.5
SUPPLEMENTARY EXPERIMENTS
E.5.1 EVALUATION OF CSIE ON BANDGAP PREDICTION
Experimental Setup. We evaluate CSIE on two complementary band gap datasets from NREL- MatDB [46]. The first, called the theoretical band gap dataset, comprises 546 PBE-computed values, and the second, the experimental band gap dataset, contains 52 GWVD-computed values.
Table 11: Significance analysis of the effect of the AMGNN and second-best model on different datasets, with RMSE as the evaluation metric.
dataset
QM-mini
FreeSolv
Abraham
MNsol
second-best
0.317 (0.022)
0.926 02.88)
0.402 (0.009)
0.636(o.054)
ours
0.299 (.299(0.010)
0.907 (0.022)
0.391 (0.029)
0.581
p- value
0.032
0.019
x 10
0.013
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Table 12: Result of different methods on FreeSolv, Compsol, Abraham and MNsol datasets.
MAE (4)
RMSE (4)
FreeSolv
CompSol
Abraham
MNsol
FreeSolv
CampSol
Abraham
MNsol
D D-MPNN
0.938 (.499) (011) 0.007)
0.687 0.370 w.276
(.422
Explainahle GNN
0.757 0.757 (0.054)
(0013)
(.515)).045)
0.470
1.353
(..87/
0.801 (0.057)
(c,02)
SolvBERT
(.007)
(.178
0.514
0.806
(..02)
0.683 0.344
1.040
GAT
(o.09)
(0105
(.485
2.076, 061;
(.234)
g..01
0.759
1.782
GROVER
0.656)
0.324(00009)
0.796.o o06:
1.036
0.003)
0.338:o.017)
(..91
0.777 (0.
SMD
0.587 (0.09)
(0015)
0.403
1.328
1.157
1.157:0.010;
(o.02)
0.331
0.544
(6637(22261)
Uni-Mol
(..07)
(o002)
(.343)
0.436x..0w
(.023
0.314
0.629/..00)
0.792jc.070
Gem
0.186 (.605)
0.522
(_172
m.303)).aa)
0.681 (0.00)
1.162
CIGIN
0.576)).0m))
(0017)
0.262
0.492.0.cmm:
0.335 0.928.o.01o)
0.410
0.648 0.638
CGIB
0,563
0.164
0.21
(0.00)
0.434
0.957
0.291 0.291
0.411
MMGNN
0.547 (0.0)))
(.065
(0.00))
0 (.284 (.926)
q.002
(c.36))
AMGNN
0.531 (0022)
0.15%
q..52; 0.188
0 0.290)
0.391 (0.02))
0.581 (.581(c.22))
Table 13: Time complexity of CSIE framework.
FreeSolv
CompSol
Abraham
MNsol
CombiSolv-Exp
CombiSolv-QM
TIME
0.1min
0.1min
0.
0.05min
0,4min
26min
Each material formula is featurized with matminer [67 to extract key descriptors- atomic number, electroncgativity, atomic radius, ionization energy, atomic packing efficiency, and valence/conduction band centers-whie are then concatenated with MolCA embeddings [4]. After removing entries with missing or invalid values, the theoretical dataset is split 80% for training and validation (seed=42), and performance measured by MAE.
Training and Fine-Tunee Tuning Protocol. We apply CSIE to select core subsets of varying sizes from the theoretical dataset, train MLP (two hidden layers of 100 and 50 ReLU units, 500 epochs) on each subset, and identify the best-performing configuration via validation MAE. This pre-trained model is then fine-tuned on the smaller experimental band gap dataset and finally tested to assess generalization. This two-stage procedure-ae core set selection on large theoretical data followed by fine-tuning on real-world demondemmssssattes CSIE's ability to reduce training cost while preserving predictive accuracy.
Training Configuration, We use the Adam optimizer 31 with learning rate of 1 x 10-3 and weight decay of 5 x 50 The batch size is set to 32, and the model is trained for 100 epochs with early stopping based on test MAE (patience 20). All experiments are conducted on NVIDIA A 100 GPU. For statistical reliability, each setting is repeated across five random seeds, and we report the mean and standard deviation.
Baseline Methods We compare CSIE against four representative core subset selection methods: Hard Random, K-Means (2)). Margin [12 and Glister [56].
Results and Analysis. The core set selection method effectively improves model performance in transfer learning and makes up for the lack of real data. Table 8 reports the MAE on the experimental GWVD dataset after pretraining on various subset sizes of the theoretical PBE dataset and subsequent fine-tuning. Across all subset ratios, CSIE outperforms the direct-training baseline by a substantial margin. In particular, pretraining on 80% of the theoretical data yields MAE 0.4553 0260). an improvement of O. 101 eVV over direct training. Even when using only 20% of the thcoretical samples for core subset selection, CSIE achieves MAE = 0.4969 (0.0164). still 0.0815 eV better than the direct-training model. The core set selection method represented by CSIE expands the sample space and improves the performance of the model on actual data by effectively screening a large number of theoretical data.
Results and Analysis.2: The CSIE framework performs best among all core set selection methods. When compared to other core-subset selection strategies (Hard Random, K-means, Glister, Margin), CSIE consistently delivers lower MAE after transfer across all subset sizes (Table For example, at 40% of the theoretical data, CSIEachieves MAE = 0.5211 1 (0.026-1) whereas the next best method Margin yields MAE= = 0.5675 0356)- Moreover, CSIE's standard deviations remain uniformly low, indicating robust performance even under extreme subsampling. These results confirm
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Table 14: Training time, memory consumption, and MAE metrics of AMGNN under different settings on the CombiSolv-ENy dataset.
Metric
AMGNN-100
AMGNN-80
AMGNN-60
AMGNN-40
AMGNN-20
TIME
11.8h
8.5h
5.7h
4.0h
3.4h
Memory
1.6G
1.2G
1.0G
0.9G
0.7G
MAE
0.214
0.216
0.220
0.234
0.266
that the information gain-driven selection of CSIE alleviates the problem of uneven distribution of theoretical data, thereby assisting the model to make more accurate and stable predictions under resource-constrained conditions.
E.5.2 CORE SUBSET RATIO AND TRANSFER IMPACT
The selection of the core subset ratio of 1% of the original data in the main text is mainly due to two (1asons: To ensure consistency with the experimental data set scale, while expanding the sample space. the complexity overhead is minimized CombiSolv-QMi is relatively large, and a too large core subset will introduce more noise.
the greatest extent; (2) The data scale of
In this section, taking AMGNN is supplemented. From the results of Table T5 and Figure 4 of the main text, it can be seen that although expanding the core subset ratio increases the number of samples, may not necessarily improve the performance (it may introduce noise or more samples, resulting in an unbalanced training dataset).
example, the influence of the selection rate transfer learning
Table 15: Transfer performance and training time cost of AMGNN under different core subset selection ratios of CombiSolv-QM.
8%
1%
3%
5%
10%
30%
MAE
0.191 (0.011)
0. 0.194 008)
0. 0.186 (0.00))
0.194 62.5h
0.200 005)
TIME
11.6h
22.6h
40.5h
174.5h
E.5.3 SIGNIFICANCE ANALYSIS OF CSIE
The Table shows that the CSIE framework significantly reduces RMSE in all five models, showing consistent performance improvements. Compared with the second-best method, the improvements of CSIE on GAT, GEM, MMGNN, and AMGNN are highly statistically significant (p < 0.001), and the improvement CGIB is also significant (p 0.034). This shows that the CSIE framework has stable and effective enhancement effect on multiple baseline models.
E.5.4 SIGNIFICANCE ANALYSIS OF AMGNN
As can be from the Table 11 AMGNN outperforms the second-best model all four datasets, and the RMSE difference is statistically significant. particular, on the Abraham dataset, the p value low 10-1 indicating that the improvement is extremely significant; FreeSoly, MNsol, and QM-mini, the values are also less than 0.05, indicating that the performance improvement is statistically reliable. This further verifies the robustness and advantages of AMGNN variety of molecular property prediction tasks.
E.5.5 EVALUATION OF DIFFERENT CORESET SELECTION METHODS ON TRANSFER LEARNING
As can be seen from Table 9 different data screening methods have significant impact on the transfer learning effect. The CSIE method has achieved the lowest MAE on all models, showing the best transfer performance. Compared with traditional methods (such as K-Means and Hard Random), CSIE can significantly reduce the error. For example, on GAT, the MAE of CSIE is 0.853, while K-Means is as high 1.893, decrease of more than 50%. In addition, representative models such
23

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as AMGNN, CSIE is also better than other advanced methods such as Margin and Glister, indicating that this strategy has a stronger ability to retain high-quality information. Overall, CSIE significantly improves the transfer learning effect from QM-mini to CombiSolv-Exp. verifying its robustness and generalization ability in cross-dataset scenarios.
E.5.6 SUPPLEMENTARY EXPERIMENTS OF AMGNN AND BASELINES ON REAL DATASETS
We run our model and other baseline models on four datasets: FreeSolv, Compsol, Abraham and MNsol the same experimental setting and training environment as the main text. As can be seen from Table 12] AMGNN outperforms other comparison models the four datasets, showing the strongest generalization ability and prediction accuracy. Specifically, under the MAE indicator, AMGNN achieved 0.531, 0.159, 0.188 and 0.352 on FreeSolv, CompSol, Abraham and MNsol, respectively, all of which are the best or tied for the best value. Under the RMSE indicator, AMGNN also achieved the best results on Abraham and MNsol, and the other indicators were also very close to the optimal values. This shows that AMGNN can stably adapt to different data distributions and maintain excellent performance in multiple molecular property prediction tasks, verifying its modeling ability and versatility.
E.5.7 TIME AND SPACE COMPLEXITY AND EFFICIENCY TRADE-OFFS BETWEEN CSIE FRAMEWORK AND AMGNN
In Table 13 show the time complexity of the CSIE framework. Due to the randomness of the insertion order, We take one iteration of core subset selection as an example, rather than the total time of the final core subset selection. For the efficiency-complexne supplementary hyperparameter experiments and their analysis in the Appendix E.
trade-off of CSIE, see the
In addition, we CombiSolv- Exp trade-off results when reducing the scale of AMGNN in Table [4] To study the trade-off between computational efficiency and expressive capacity, evaluate several variants of AMGNN using different inter-molecular edge retention ratios, denoted as AMGNN-20, -40, -60, -80, and -100, corresponding 20%, 40%, 60%, 80%, and 100% of possible cross-molecular edges being retained. As shown [44 AMGNN-60 achieves a favorable balance, significantly reducing computational cost while preserving prediction accuracy.
example to supplement the computational efficiency
E.5.8 VALIDATION OF AMGNN DESIGN
To validate that AMGNN's improvement stems from its ability to model interaction directionality rather than added model complexity, we compared it against three interpretable baselines. Flagged GNN: Adds solute/solvent flags atom features in standard GNN. Heterogeneous GNN: Uses node types to distinguish solute and solvent atoms. Embedding Concatenation: Separately encodes solute and solvent, then concatenates the embeddings for prediction.
Furthermore, we constructed a benchmark subset, Opposite-Exp. which includes all 240 solute- solvent pairs in CombiSolv-Esx where both directions exist (i.e., A-BB and B B-+)). This subset directly evaluates model sensitivity to role reversal.
Table 16: Performance comparison of AMGNN and baseline models across multiple datasets.
Model
FreeSolv
CompSol
Abraham
MNsol
CombiSolv-e -xxp
Opposite-ENy
Flagged GNN
0.732
0.241
0.503
0.921
0.437
0.562
Heterogeneous GNN
0.581
0.183
0.258
0.439
0.254
0.294
Embedding Concatenation
0.785
0.197
0.462
0.453
0.223
0.367
AMGNN
0,531
0.159
0.188
0.352
0.214
0.191
As shown in Table 16 across datasets, AMGNN consistently achieves the lowest error, with the
most significant improvements observed on the Opposite-Exp subset. This confirms that AMGNN effectively captures the asymmetric nature of solute solvent interactions, a capability not realized by symmetric flpe-flagged baselines.
24

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E.5.9 EFFECTS OF ENCODER ON CSIE
We further investigated how different pre-trained encoders and subset sizes influence the performance of CSIE. Using AMGNN on CombiSolv-Bii as an example, we report results in terms MAE.
Table 17: Impact of different encoders and subset sizes on CSIE performance on CombiSolv-Exp
Method
20% Trainset
40% Trainset
60% Trainset
80% Trainset
w/o CSIE (Random)
1.468
0.839
0.579
0.391
MolCA + GROVER (2D)
1.032
0.667
0.457
0.353
MolCA UniMol (3D)
0.851
0.588
0,431
0.356
MolCA + MolTC (LLM)
0.789
0.321
0.365
0.324
Standard CSIE
0.777
0.311
0.247
0.320
The MAE results of Table 17 indicate that the choice of encoder substantially affects CSIE's effec- tiveness. In particular, the LLM-based encoder (MolTC) outperforms graph-based encoders, and Standard CSIE achieves the best overall performance, especially with limited training subsets.
F
LIMITATIONS
F1 DEALING WITH DATA BIAS AND NOISE
The CSIE framework utilizes the hierarchical navigation small world (HNSW) algorithm compute information gain and select core samples efficiently. However, enhancing its robustness to noisy data remains a key direction. Future improvements could integrate advanced noise detection and removal mechanisms, such as multi-scale clustering or adaptive neighborhood selection, to refine the selection process and enhance training quality.
F22 MORE ACCURATE IDENTIFICATION OF OUTLIERS
In the method proposed in this paper, a sample with high information gain means that it far away from the surrounding samples in space has low feature similarity. Then, high-gain sample may be a sample that is added first in a cluster (i.e., a representative sample), but may also be an outlier. In the future, more accurate evaluation model should be designed to distinguish the two and further improve the quality of the dataset.
F33 EFFICIENT INFORMATION TRANSFER AMGNN
The AMGNN model introduces large number of graph connections to capture solute-solvent interac- tions, but managing information flow efficiently is an important consideration. Future enhancements could involve adaptive attention mechanisms selectively prioritize key connections and multi- scale information transfer strategies to balance local and global interactions. These improvements could help mitigate over-smoothing and ensure more effective information propagation, ultimately enhancing solubility prediction accuracy.
G Broader Impact
The proposed CSIE-AMGNN framework advances the application of Al chemistry by improving the efficiency and reliability of molecular property prediction in real-world settings. By introducing scalable core subset selection method and modeling asymmetric solute-solvent interactions, this work offers pathway to reduce computational costs while maintaining high predictive accuracy, which is particularly valuable for experimental chemists and material scientists operating under limited data conditions. Potential positive impacts include accelerating the discovery of new solvents, optimizing reaction conditions, and aiding the design environmentally friendly materials and pharmaceuticals.
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NeurIPS Paper Checklist
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Question:
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Question: For each theoretical result, does the paper provide the full of assumptions and complete (and correct) proof?
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Answer: [Yes]
Justification: We provide proof' on information gain B the EM algorithm C and submodular function D in the appendix.
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Reviews:
[{"review_id": "3GChfNGWA4", "reviewer": "Reviewer_24Q7", "summary": "This paper proposes a new sample selection framework, Core Subset Iterative Extraction (CSIE), for improving the generalization of molecular property prediction models from theoretical datasets to real-world chemical data. CSIE aims to maximize information gain and reduce redundancy via an iterative EM-like approach using molecular representations augmented with both graph-based and textual features. The work also introduces the AMGNN, an asymmetric molecular interaction graph neural network designed to better capture solute-solvent interactions through directed edge modeling.", "questions": "Refer to the weakness.", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "## Strength\n1. The paper grounds the CSIE approach in submodular function theory and utilizes information-theoretic and geometric measures for informative sample selection, moving beyond simple uncertainty or loss-based criteria. \n2. The data-centric approaches hold certain reference value in the field of AI for Science, given that this field typically faces the problem of data confounding.\n3. The results show that CSIE enables strong performance even when training on a fraction of the real-world data, and AMGNN outperforms or matches other strong GNN baselines.\n\n\n## Weakness\n1. The use of information gain as defined through average cosine distance in the embedding/HNSW graph is only described in a high-level and is not deeply tied to chemical relevance—there is insufficient empirical analysis or visualization on whether the CSIE-selected core samples are chemically diverse, rare, or cover meaningful classes.\n2. I think that if the CSIEFramework is also applied to demonstrate performance improvements across different network models, it would significantly aid in verifying the method's effectiveness.\n3. Can the authors clarify the attribution of AMGNN's gains over MMGNN or CGIB? Is it driven solely by the asymmetric message passing or also by the use of multimodal (MolCA) representations?\n4. The domain gap issue is important, but the demonstrated improvements are incremental and not conclusively shown to generalize across molecular chemistries or other tasks. I hope to provide some discussion on the generalizability of the method.", "was_revised": true, "final_justification": "I am inclined to accept this article. On the one hand, the authors have demonstrated the effectiveness of their method in specific domains (molecular solubility prediction and band gap prediction). On the other hand, data quality is a prevalent issue across all AI4Science fields, making data-centric approaches increasingly important. This article serves as a typical example in this regard. Although it does not explore extensions to other domains, I believe it holds certain reference value.", "rebuttal": "# Response to Reviewer 24Q7:\n\nThank you for your thoughtful and constructive feedback. We sincerely appreciate the time and effort you dedicated to reviewing our manuscript. We are encouraged by your recognition of the theoretical grounding of the CSIE framework, its relevance to data-centric AI for Science, and the empirical performance of both CSIE and AMGNN. At the same time, we acknowledge the need for deeper clarification regarding the chemical relevance of information gain, the contributions of different components (e.g., MolCA vs. asymmetric message passing), and the broader generalizability of our approach. Below, we provide a detailed, point-by-point response to each of your insightful comments.\n\n---\n\n> **W1.** Visual Analytic\n\nThank you for pointing out the issue. We explain as follows:\n\n- First, in terms of theoretical foundations, the connection between the information gain metric and chemical properties is primarily established through the use of a more effective encoder. In this study, we employ both molecular graph features and semantic features for encoding. Additionally, we utilize **MolCA**, a general-purpose chemical foundation model pre-trained on **PubChem 324k**, to fully extract sample features and ensure the completeness of chemical semantics as much as possible.\n\n- Second, we divide the dataset based on solvent types and evaluate the **sample category distribution before and after CSIE filtering**. It is clearly observable that CSIE effectively alleviates the data redundancy issue, resulting in a **more balanced dataset in terms of class distribution**. Detailed results can be found in **Section 3.3, Figure 3**, along with the corresponding analysis.\n\n\n> **W2.** Application of CISE in different network models\n\n- **CSIE consistently improves model performance across different network architectures**: In **Table 1 on page 7**, the column *CombiSolv-Exp → CombiSolv-Exp* shows the performance of each model trained directly on real experimental data, while *QM-mini → CombiSolv-Exp* represents models pretrained on the CSIE-filtered QM-mini dataset and fine-tuned on the real data. **A comparison of these two columns reveals that most models exhibit significant performance improvements, demonstrating the effectiveness of CSIE**.\n\n- Similarly, **Tables 2 and 3 on page 8** present the performance under different solute/solvent type splits, illustrating **CSIE's enhancement of model generalization ability**. In **Table 9 on page 20 (Appendix)**, we compare the performance gains of CSIE with other core-set selection frameworks across different network models. **The results clearly show that CSIE outperforms the alternatives**.\n\n> **W3.**  Reasons for the performance improvement of AMGNN\n\n- The performance improvement of **AMGNN** over **MMGNN** and **CGIB** is indeed attributed to its **asymmetric message-passing mechanism**. Under both experimental settings—*CombiSolv-Exp → CombiSolv-Exp* and *QM-mini → CombiSolv-Exp*—AMGNN achieves the best performance.\n\n- In the former setting, the model is trained directly on the target dataset without any influence from CSIE, let alone the *MolCA* module within CSIE. In the latter transfer learning scenario, *MolCA* only plays a role during the data filtering process; the filtered *QM-mini* dataset is then fairly used across all models.\n\n- **In summary**, the performance gains of AMGNN are independent of *MolCA*.\n\n\n> **W4.**  Discussion on the generalizability of CSIE and AMGNN\n\nWe appreciate the reviewer’s concern regarding the generalizability of our proposed method. While the **dissolution free energy prediction task serves as a focused case study**, both CSIE and AMGNN are designed as **general-purpose tools** for broader applications in molecular machine learning.\n\n**(1) Generalizability of CSIE.**  \nCSIE is a **model-agnostic, data-centric framework** aimed at improving the quality and efficiency of theoretical datasets. While it draws inspiration from active learning, it **specifically addresses limitations in uncertainty-based sampling**, which is often unreliable in noisy chemical datasets. To demonstrate its generality, we applied CSIE to a **material band gap prediction task** (see Appendix E.5.1), which is entirely distinct from dissolution. The performance gains observed there confirm that **CSIE is not limited to solubility tasks**, but rather serves as a **universal dataset screening strategy** for scientific machine learning domains where data redundancy and imbalance are common.\n\n**(2) Generalizability of AMGNN.**  \n\nAMGNN proposes an **asymmetric molecular graph construction paradigm** that directly encodes the directional interactions between two molecules. Unlike traditional methods that treat molecular pairs **symmetrically**—either through feature concatenation or attention-based fusion (e.g., Uni-Mol, Explainable GNN, CIGIN)—AMGNN captures the fact that **molecular interactions are not order-invariant**. \n\nFor example, as stated in line 173, the solvation energy of **ethanol in water** differs significantly from that of **water in ethanol**. This asymmetry is also critical in other molecular relation learning tasks—such as **drug–drug interaction**\n\nBy explicitly capturing these asymmetries through **heterogeneous graph modeling**, AMGNN can be **readily applied to other pairwise molecular tasks** such as reaction prediction and toxicity analysis. As discussed in **W2 & W3**, we believe this paradigm opens a promising direction for more **chemically realistic and interpretable molecular interaction learning**.\n\nIn summary, while solubility serves as a representative task, the methods we propose—**CSIE for data selection** and **AMGNN for asymmetric modeling**—are both **task-agnostic and widely applicable**, offering general value to the molecular machine learning community.\n\n---\n\nWe greatly appreciate your insightful and constructive comments, which have helped us clarify key aspects of our work and strengthen the manuscript. We hope that our point-by-point responses have addressed your concerns, especially regarding the chemical relevance of information gain, the independent contributions of CSIE and AMGNN, and the generalizability of our approach. If our clarifications and additional analysis have resolved the ambiguities you raised, we would be sincerely grateful if you would consider revising your evaluation. Of course, if you have any further questions or suggestions, we would be more than happy to continue the discussion. Thank you once again for your thoughtful review and valuable feedback—it has been instrumental in improving our work."}, {"review_id": "L6tUGx0wAD", "reviewer": "Reviewer_2Vf7", "summary": "Addressing the mismatch between large simulated datasets and scarce experimental data, the authors introduce a two-part framework. Core Subset Iterative Extraction iteratively selects a compact, information-rich subset of theoretical solute–solvent data using submodular information-gain criteria computed from pretrained chemical language-model embeddings. The selected subset is then used to train an Asymmetric Molecular GNN that models directional solute→solvent and solvent→solute message passing. Together, CSIE and AMGNN improve experimental-data accuracy and reduce training cost, and the selection strategy generalizes to a second case study on band-gap prediction.", "questions": "1 How sensitive is the CSIE to the choice of the pretrained chemical encoder and the size of subset selected? Also, did the authors experiment with different core subset sizes and how does that affect the final model accuracy? \n\n2 Can the authors clarify the individual contributions of CSIE and AMGNN to the performance gain? Providing an ablation where (a) no CSIE + AMGNN, (b) CSIE + standard GNN, and (c) CSIE + AMGNN are compared on the real data would isolate how much each part helps. \n\n3  The method assumes theoretical data has useful information but includes redundancy. What if the theoretical calculations systematically deviate from experimental reality (e.g., consistently overestimate dissolution free energies or fail for certain classes of compounds)? Would the model still be biased? \n\n4.  The approach was tested on dissolution and briefly on band gap. Could the authors provide more detail on the latter or other domains – for example, did CSIE need any modification for selecting theoretical band-gap data? And does AMGNN’s asymmetry concept apply there (or was only CSIE used)?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. The CSIE method is grounded in submodular optimization – it iteratively adds samples that maximize information gain, which is a principled way to reduce redundancy in the simulation data. The second component, AMGNN, is well-motivated: real solute–solvent interactions are asymmetric. \n\n2. The paper’s experiments show that combining CSIE + AMGNN yields improved prediction accuracy on real-world dissolution data, indicating the approach successfully bridges the gap as intended. \n\nWeaknesses:\n1. The novelty is split across two techniques, making each part feel somewhat thin in isolation. CSIE draws from known concepts in active learning, and AMGNN is essentially a tailored GNN architecture. The question is if either component by itself is a significant advance. \n\n2. Another weakness is the potential dependence on the quality of the pre-trained chemical encoder used for computing sample diversity – the paper uses a “chemical LLM” to embed molecules for CSIE. If that encoder has biases or limitations (e.g., it might not encode solvation-specific features), CSIE might pick suboptimal subsets. Also, CSIE is evaluated by final model performance, but it would be better to see an intrinsic evaluation: e.g., does the core subset indeed have less redundancy or better cover important solute–solvent feature space than random sampling? \n\n3 The focus is on dissolution; even though they argue it’s a general approach, the paper reads a bit like a case study rather than a broad benchmark.", "was_revised": true, "final_justification": "Thanks authors for informative and careful rebuttal. Most of my concerns has been addressed, I raise my score to 4.", "rebuttal": "Thanks for your thoughtful and constructive feedback. We sincerely appreciate the time and effort you invested in reviewing our manuscript. We are encouraged by your recognition of the principled basis of our subset selection strategy and its empirical effectiveness in bridging theoretical and experimental domains. At the same time, we acknowledge that some aspects require further clarification. Below we provide a detailed, point-by-point response to your comments and suggestions.\n\n___\n\n> **W1.&W3.** Novelty and Generality of CSIE and AMGNN\n\nThank you for the valuable comments. While **CSIE** and **AMGNN** are two distinct components, they are **proposed in tandem to address a core challenge in AI chemistry**: the gap between large, imbalanced theoretical datasets and limited, high-quality experimental data. CSIE focuses on **data quality and efficiency**, while AMGNN targets **accurate modeling of molecular interactions**. Their integration offers a coherent solution for effectively leveraging both theoretical and experimental data.\n\nWe emphasize that **both methods have standalone value**:\n\n- **CSIE** is not a typical active learning strategy. It **avoids uncertainty-based sampling**, which is unreliable in noisy chemical domains, and instead adopts a **structure-aware, distribution-optimized selection** mechanism. Its **generality is validated on a separate task**—material band gap prediction (Appendix E.5.1)—demonstrating that it is **not limited to dissolution**.\n\n- **AMGNN** introduces a **novel asymmetric graph structure** to model directional interactions between solute and solvent, addressing a key limitation in prior work that **assumed symmetric or simplistic combinations**. This is **crucial in chemistry**, where interaction order significantly impacts properties (e.g., solvation, drug interactions).\n\nAlthough **dissolution prediction** is the main benchmark in this paper, it was selected due to the **availability of both theoretical and experimental data**, making it suitable for evaluating both CSIE and AMGNN. However, the **methods are general** and can be **readily extended to other molecular property prediction tasks**, as demonstrated by our additional experiments.\n\nIn summary, **CSIE and AMGNN are unified by a shared objective**, each solving a different but complementary part of the same problem. **Their combination is synergistic, but each also contributes meaningful innovation individually**.\n\n\n> **W2.** Sampling quality of CSIE\n\nThank you for pointing out the problem about CSIE sampling quality.\n\n- First of all, it should be noted that the encoder does affect CSIE's evaluation of samples to a certain extent, which is also a problem we hope to alleviate. For this reason, this paper uses both molecular graph features and semantic features for encoding, and uses the general chemical model MolCA pre-trained on PubChem to alleviate the problem of domain bias.\n\n- Secondly, **in Section 3.3 Fig 3**, we also visualized the screening effect of CSIE itself on the dataset: we evaluated the sample category distribution of the dataset before and after CSIE screening according to the solvent type. It can be clearly seen that CSIE can indeed **alleviate the redundancy problem of the dataset and obtain a more balanced dataset.**\n\n___\n\n> **Q1.** Effects of encoder and subset size on CSIE\n\n- The following table uses AMGNN and CombiSolv-Exp as examples to supplement the impact of pre-trained encoders on CSIE (indicator is MAE). It can be seen that the encoder will change the effect of the subset selected by CSIE to a certain extent, and LLM is significantly better than GNN encoder.\n\n|          Method          | 20% Trainset | 40% Trainset | 60% Trainset | 80% Trainset |\n| :----------------------: | :----------: | :----------: | :----------: | :----------: |\n|    w/o CSIE (Random)     |    1.468     |    0.839     |    0.579     |    0.391     |\n| MolCA->GROVER(2D graph)  |    1.032     |    0.667     |    0.457     |    0.353     |\n| MolCA->UniMol (3D graph) |    0.851     |    0.588     |    0.431     |    0.356     |\n|    MolCA->MolTC (LLM)    |    0.789     |    0.321     |    0.365     |    0.324     |\n|      Standard CSIE       |    0.777     |    0.311     |    0.247     |    0.320     |\n- Regarding the sizes of different core subsets, we provide the experimental results and corresponding analysis on different data sets and different core subset sizes in Figure 4 in Section 3.4 of the main text and Table 15 in Section E.5.2 of the Appendix. We can draw the following conclusion: The larger the core subset, the better the effect. On multiple data sets, smaller core subsets have achieved better results, such as Compsol (80%), FreeSolv (60%), and CombiSolv-QM (5%).\n\n> **Q2.** Ablation experiment\n\nWe provide the corresponding ablation experiments in **Table 4 on page 9** of our paper:\n\n1. **w/o CSIE** versus **no CSIE+AMGNN**: Instead of using CSIE and performing multiple experiments, we use a randomly selected dataset and an AMGNN network.\n\n2. **Asymmetric network** versus **CSIE+standard GNN**: We eliminate all interaction edges and use only the basic MPNN mechanism to extract individual molecular information, then obtain the overall bimolecular information through concatenation.\n\n3.  **Our Method (CSIE+AMGNN)** versus **CSIE+AMGNN**\n\nThe comparative results in Table 4 fully demonstrate the respective roles of CSIE and AMGNN. If you would like to add more ablation experiments on other datasets or models to further resolve your confusion, we are also happy to discuss with you and conduct more experimental verification to further improve our paper. Thank you very much for your help!\n\n> **Q3.** The impact of theoretical calculation deviation\n\nThank you for your concern about this. We will explain from two perspectives that our model is not easily affected by theoretical calculation bias. First, the CSIE framework evaluates information and redundancy based solely on sample characteristics, without considering the impact of theoretical dataset labels. Second, the pre-training task focuses on encoder pre-training, while the task prediction network is carefully fine-tuned on real data.\n\nTo verify this, we conducted additional experiments: **We randomly varied the calculated results (label values) on the theoretical CombiSolv-QM dataset from 0 to 10%, 20%, 30%, 40%, 50% and presented the transfer learning results on AMGNN.**\n\n|     | original dataset | ±10%  |   ±20%    | ±30%  | ±40%  | ±50%  |\n| :-: | :--------------: | :---: | :-------: | :---: | :---: | ----- |\n| MAE |      0.191       | 0.193 | **0.192** | 0.201 | 0.196 | 0.213 |\n\nGenerally speaking, for the same sample, the error between theoretical calculation and actual experiment is **within ±25%,** and the CSIE framework still maintains stable effectiveness. Of course, as random errors are amplified, the encoder will also be affected to a certain extent, resulting in deviations.\n\n> **Q4.** Explanation of some details\n\nBand gap prediction is a single-molecule task. For data screening, CSIE only needs to modify the final bimolecular feature concatenation step and use only single-molecule features for information gain calculation. Regarding the model architecture, since this is a single-molecule task, we only use a simple GCN+MLP for prediction.\n___\n\nThanks again for your time and effort in reviewing our manuscript. Your feedback is important to our efforts to improve our work. If our response adequately addresses your concerns, we sincerely hope that you will consider revising your rating. We are also happy to provide further clarification or continue the discussion."}, {"review_id": "iGTHvMC3cS", "reviewer": "Reviewer_Lzfx", "summary": "This paper introduces two novel machine learning approaches for modeling solute–solvent interactions: Core Subset Iterative Extraction (CSIE) and the Asymmetric Molecular Graph Neural Network (AMGNN). The authors develop an iterative optimization strategy for selecting a core subset of data points, named CSIE. This method aims to maximize information gain while minimizing redundancy and theoretical bias in the dataset, leading to more representative and efficient training data. To more accurately capture the directional and asymmetric nature of solute–solvent interactions in realistic chemical environments, the authors design a graph neural network AMGNN that incorporates molecular asymmetry into its architecture. Then the authors evaluated CSIE+AMGNN on several datasets.", "questions": "1.\tMotivation for Dataset Subsampling in Solvation Free Energy Prediction:\nThe paper introduces a dataset subsampling method (CSIE) for solvation free energy prediction, but it does not sufficiently justify why subsampling is necessary in this domain. Are there any domain-specific challenges—such as noise, redundancy, or systematic bias in theoretical datasets—that make subset selection particularly valuable? If not, why not demonstrate the method’s general utility by applying it to widely-used benchmark datasets like QM9 or MoleculeNet?\n2.\tComparison to Full Dataset Training:\nFigure 4 shows that training on the full datasets consistently outperforms all subsampling methods on the Abraham, Compsol, Freesolv, and MNsol datasets. What about on the CombiSolv-QM? This raises a fundamental question: if subsampling does not lead to performance gains, what is the practical benefit of CSIE in this context? This is critical to understanding whether CSIE offers any meaningful advantage.\n3.\tBaseline Comparison for AMGNN:\nThe effectiveness of the proposed AMGNN architecture is not fully validated. Specifically, the paper should include a comparison to a simple baseline where a standard GNN is used with additional embeddings or flags to mark solute and solvent atoms/molecules or just use heterogeneous graph neural network. Without this comparison, it is unclear whether AMGNN provides any substantial improvement over simpler, more interpretable alternatives.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 1, "originality": 1, "strengths_and_weaknesses": "Strengths:\n\nThe paper presents a novel subset selection method, Core Subset Iterative Extraction (CSIE), which offers a potentially useful strategy for improving data efficiency in molecular modeling tasks.\n\nWeaknesses:\n\n1.\tLack of Cohesion and Justification: The paper lacks a clear and coherent structure, and the connections between its core contributions—CSIE and AMGNN—are not well articulated. It remains unclear why CSIE is appropriate for the solvation free energy prediction task, and how it integrates with or enhances AMGNN. As presented, the work feels like two loosely connected efforts rather than a unified framework.\n\n2.\tUnclear Motivation for AMGNN Design: The rationale behind the proposed Asymmetric Molecular Graph Neural Network (AMGNN) is insufficiently explained. The paper does not convincingly argue why existing approaches, such as heterogeneous GNNs or simpler methods involving solute–solvent embeddings or interaction labels, are inadequate.\n\n3.\tMarginal Performance Gains: The reported improvements over baseline models are not compelling. In Table 1, AMGNN shows only slight performance gains compared to UniMol and Explainable GNN. Similarly, Figure 4 suggests that CSIE offers only marginal benefits over systematic sampling. This weakens the empirical justification for the proposed methods.", "was_revised": true, "final_justification": "After carefully considering the author's rebuttal and subsequent discussion, I have revised my rating from 2 to 3. While my initial concerns focused on the lack of cohesion between CSIE and AMGNN, unclear motivation for the architectural design, and marginal performance gains, the authors have provided substantial clarification and supporting evidence on each of these points. Nevertheless, the cohesion issue still exists.\n\nRegarding AMGNN, the authors clarified that the novelty lies not in architectural complexity but in representing chemically meaningful asymmetry via a directed heterogeneous graph. The added baseline comparisons, especially the Opposite-Exp subset, support the claim that this representation captures interaction directionality better than existing methods.\n\nAlthough the absolute performance improvements remain modest, the authors provided statistical analyses and detailed ablation studies that demonstrate the robustness and significance of their findings. The explanation of how systematic sampling can inadvertently rely on dataset ordering—and how CSIE is more robust under permutation—was insightful and well-supported with additional analysis.\n\nOverall, while some concerns remain about the practical impact of the performance gains, the conceptual contributions and the thoughtful experimental design make this work a meaningful addition to the literature on molecular machine learning. I appreciate the authors' detailed and scientifically grounded responses and have updated my rating accordingly.", "rebuttal": "Thanks for your detailed and constructive comments. We appreciate the time and effort you dedicated to reviewing our manuscript. While we recognize that several aspects required clarification and improvement, we are encouraged by your acknowledgment of the potential value of our proposed subset selection strategy. Below, we provide a point-by-point response to your concerns. \n\n---\n\n> **W1.** The connection between CSIE and AMGNN\n\n- Both **CSIE** and **AMGNN** are designed to address data quality challenges in AI-driven chemistry. Theoretically generated datasets are often large but noisy, with significant class imbalance, while high-quality experimental data are scarce. To better leverage both types of data, we propose two complementary strategies:  \n  **(1)** Use **CSIE** to streamline theoretical datasets by reducing redundancy and improving distribution, thereby lowering pretraining costs.  \n  **(2)** Use **AMGNN** to better simulate real-world solute–solvent interactions and enhance model performance on experimental datasets.\n\n- It is important to emphasize that **CSIE is intended as a general-purpose framework** for theoretical data screening. As stated in the title, dissolution free energy prediction serves merely as a case study. We also note in **line 82** that **Appendix E.5.1** includes additional validation on the **material band gap prediction task**, further demonstrating the versatility of CSIE.  \n  With such a general framework, we are able to select lighter, better-balanced pretraining datasets (Figure 3), which in turn improve the **performance (Table 1)** and **generalization ability (Tables 2 and 3)** of AMGNN.\n\n\n> **W2 & Q3.** Clarification and Validation of AMGNN Design\n\n-  Design Motivation\n\n\tThe core of **AMGNN** is not to propose a new GNN architecture, but to introduce a **more chemically faithful graph representation** by constructing an **asymmetric heterogeneous solute–solvent graph** using DGL.\n\n\tMost existing approaches (e.g., Uni-Mol, Explainable GNN, CIGIN) treat the two molecules **independently**, and then model interactions via **concatenation** or **attention**, which results in a **symmetric treatment** of solute and solvent. \n\n\tHowever, **molecular interactions are inherently directional**. For instance:\n\t  - *Ethanol dissolves in water*: −5.1 kcal/mol  \n\t  - *Water dissolves in ethanol*: −6.3 kcal/mol\n\n\tSuch asymmetry is chemically meaningful but cannot be captured by symmetric designs. Therefore, AMGNN directly builds a **directed heterogeneous graph**, which encodes the interaction direction. This enables the model to learn directional dependencies from both structural and relational cues.\n\n- Baseline Comparison & Validation\n\n\tTo ensure AMGNN’s superiority is not due to model complexity, we implemented and compared against **three strong, interpretable baselines**:\n\n\t1. **Flagged GNN**: Adds solute/solvent flags to atom features in a standard GNN.  \n\t2. **Heterogeneous GNN**: Uses node types to distinguish solute and solvent atoms.  \n\t3. **Embedding Concatenation**: Separately encodes solute/solvent, then concatenates for prediction.\n\n\tTo further evaluate AMGNN’s ability to model directionality, we constructed a new evaluation subset:\n\n\t- **Opposite-Exp**: Contains all **240 solute–solvent pairs in CombiSolv-Exp** where both directions exist (i.e., A→B and B→A).  \n\t- This allows direct testing of the model’s **sensitivity to role reversal**.\n\n|                             | FreeSolv  |  CompSol  |  Abraham  |   MNsol   | CombiSolv-Exp | Opposite-Exp |\n| :-------------------------: | :-------: | :-------: | :-------: | :-------: | :-----------: | :----------: |\n|       **Flagged GNN**       |   0.732   |   0.241   |   0.503   |   0.921   |     0.437     |    0.562     |\n|    **Heterogeneous GNN**    |   0.581   |   0.183   |   0.258   |   0.439   |     0.254     |    0.294     |\n| **Embedding Concatenation** |   0.785   |   0.197   |   0.462   |   0.453   |     0.223     |    0.367     |\n|            AMGNN            | **0.531** | **0.159** | **0.188** | **0.352** |   **0.214**   |  **0.191**   |\n \n As shown in the table, **our AMGNN consistently outperforms (MAE(↓)) all three approaches, suggesting that the proposed asymmetric design more effectively captures solute–solvent interactions.**\n\n\n> **W3.** Performance improvement analysis\n\n- **Performance Analysis of AMGNN**:\n    - In **Table 1**, since *CombiSolv-Exp* contains a relatively large amount of data, the performance differences among models may not appear very pronounced. However, in **Table 11 on page 20 of the Appendix**, we conduct a necessary **significance analysis**, which confirms that the performance improvement of AMGNN is statistically significant.\n\n    - Furthermore, for the comparison between AMGNN and other models, **Table 12 on page 21 of the Appendix** provides additional validation. On smaller datasets, AMGNN demonstrates a more noticeable advantage.\n\n- **Performance Analysis of CSIE**:\n    - In **Table 10 on page 20 of the Appendix**, we also present the **significance analysis for CSIE**.\n    - In **Observation 7 of Section 3.4**, we note that the comparable performance of **CSIE and systematic sampling** stems from the dataset’s inherent solvent-type organization, which naturally balances categories. As shown in **Table 9 of the Appendix**, systematic sampling offers no significant advantage on _CombiSolv-QM_. To confirm this, we shuffled the sample order in Abraham and Compsol datasets and repeated the experiments; results (marked with *) are presented below.\n\n| Dataset  |       Method        | core subset size=20% | core subset size=40% | core subset size=60% | core subset size=80% |\n| :------: | :-----------------: | :------------------: | :------------------: | :------------------: | :------------------: |\n| Abraham  |        CSIE         |        0.4170        |        0.3080        |        0.2185        |        0.1693        |\n|          | Systematic_Sampling |        0.4587        |        0.3388        |        0.2404        |        0.1862        |\n| Abraham* |        CSIE         |        0.4153        |        0.3102        |        0.2019        |        0.1581        |\n|          | Systematic_Sampling |        0.5028        |        0.4210        |        0.2956        |        0.2433        |\n| Compsol  |        CSIE         |        0.4030        |        0.2496        |        0.1939        |        0.1605        |\n|          | Systematic_Sampling |        0.4433        |        0.2745        |        0.2132        |        0.1765        |\n| Compsol* |        CSIE         |        0.4215        |        0.2352        |        0.1877        |        0.1621        |\n|          | Systematic_Sampling |        0.4995        |        0.3142        |        0.3027        |        0.2018        |\n It can be seen that:\n>**the CSIE framework is robust when the original dataset is perturbed, while the performance of the method adopted by the system is significantly degraded.**\n\n---\n\n> **Q1.** Motivation for subset sampling\n\n- In **W1**, we have thoroughly explained the motivation for applying the CSIE framework to dissolution free energy prediction, particularly in collaboration with AMGNN to leverage both theoretical and experimental data. We further demonstrated the generalizability of CSIE across various solute–solvent datasets and in the **Material Band Gap Prediction dataset (Appendix E.5.1)**.\n\n- Moreover, in solute–solvent prediction tasks, the _CombiSolv-QM_ dataset exhibits notable **redundancy and category imbalance**, which can hinder model pretraining and transfer learning. As shown in **Section 3.3 (Figure 3b)**, when samples are grouped by solvent type, **aromatic hydrocarbons make up 29.5%** of the dataset, while **halogen-containing solvents account for only 7.6%**, highlighting the skewed distribution.\n\n> **Q2.** Comparison with full data training\n\nThanks for your careful observation. However, we would like to clarify that for many datasets, the CSIE subset selection framework effectively balances the sample distribution, **achieving better model performance with fewer training samples**:\n\n- **First**, we sincerely apologize for any confusion caused by the unclear design and analysis of Figure 4. In fact, the results shown in Figure 4 indicate that CSIE achieves **better performance than using the full dataset** on *Compsol*, *Freesolv*, and *MNsol*, while using only **80%**, **60%**, and **80%** of the data respectively. We discuss and analyze this phenomenon in **Obs.8 on page 9 of the main text**.\n\n- **Second**, on *CombiSolv-QM*, we tested the effect of different subset ratios on transfer learning performance. A similar trend was observed as described above. The detailed results and analysis can be found in **Appendix Section E.5.2 and Table 15**.\n\n\n---------------\n\nWe greatly appreciate your insightful and helpful comments, as they will undoubtedly help us improve the quality of our article. If our response has successfully addressed your concerns and clarified any ambiguities, we respectfully hope that you consider raising the score. Should you have any further questions or require additional clarification, we would be delighted to engage in further discussion. Once again, we sincerely appreciate your time and effort in reviewing our manuscript. Your feedback has been invaluable in improving our research."}, {"review_id": "5bJ7hEyusX", "reviewer": "Reviewer_vRGh", "summary": "This paper proposes a core subset iterative extraction (CSIE) framework for samDesign of light- and chemically responsive protein assemblies through host-guest interactionsple selection to solve the problem of too large theoretical data sets in transfer learning, and on this basis proposes an asymmetric molecular interaction graph neural network (AMGNN) for predicting solute-solvent properties. Through this method, the article can retain a wider chemical space of theoretical data while reducing the complexity of training. The authors fully verified the effectiveness of the proposed core data set selection method and asymmetric interaction network in two scenarios: solute solvent interaction and band gap prediction.", "questions": "- Is it possible that CSIE systematically prefers a certain type of “high-information but more difficult to predict” samples, thereby causing overfitting to these areas during training?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The article has a clear motivation and proposes a novel chemical core subset selection method and molecular interaction network, balancing multiple requirements such as efficiency and overhead, and has good application prospects.\n\n2. The main text and appendix provide sufficient experiments and relatively complete theoretical proofs, and verify the effectiveness of CSIE and AMGNN in multiple scenarios such as solute-solvent reaction and molecular band gap prediction, achieving good results.\n\n3. The article clearly presents the motivation, methods, and results. The content of each section is well-organized and the reading experience is good. The visualization of the methods and conclusions is impressive (Figure 1,3).\n \n\n**Weaknesses:**\n1. The article proposes an asymmetric molecular interaction network, but the discussion on GNN is somewhat thin.\n\n2. Compared with existing subset selection methods based on information theory such as Glister and InfoBatch, the innovation boundary of CSIE is slightly blurred. It is recommended to further emphasize the essential differences and advantages of the method over existing work.\n\n3. Although the author emphasizes advantages such as \"lightweight\" and \"efficient\", there is a lack of clear data support, such as how much training time is saved under what scale of data? How does the memory usage change?", "was_revised": false, "final_justification": "", "rebuttal": "Thanks for your detailed and constructive review. We sincerely appreciate the time and effort you invested in evaluating our manuscript. We are encouraged by your recognition of the motivation, clarity, and potential impact of our proposed chemical core subset selection strategy and asymmetric molecular interaction network. At the same time, we acknowledge the areas that require further clarification and improvement, particularly in the discussion of GNN-related components, the differentiation from existing information-theoretic methods, and the need for more concrete efficiency metrics. Below, we provide a point-by-point response to your thoughtful comments and suggestions.\n\n---\n\n> **W1.** Discussion on Graph Neural Network\n\nThank you for pointing out this issue. We will add the following discussion on GNN in molecular relationship learning:\n\n- Graph neural networks (GNNs) [1] have shown strong expressive power in molecular relationship modeling and have been widely used to predict molecular properties, reaction behavior, and solubility free energy. Traditional GNNs model the interactions between atoms and bonds through message passing between nodes to extract the structural and chemical characteristics of molecules. Typical models such as D-MPNN [2] and GAT [3] use this method to complete the representation learning of molecular graphs. Models such as MMGNN [4] and CGIB [5] try to innovate in modeling the interaction between molecular pairs.\n\n- However, in actual chemical systems, there are asymmetric and diverse relationships between molecules (such as solutes and solvents). Traditional GNNs are limited in their ability to model such heterogeneous information. To this end, heterogeneous graph neural networks (HetGNNs) [6] provide an effective solution. HetGNN expands the modeling capabilities of GNN, introduces multiple types of nodes (such as molecules of different categories) and edges (such as hydrogen bonds, hydrophobic interactions, etc.), and supports type-based message passing mechanisms. However, in the field of molecular relationship learning such as dissolution reaction prediction or drug interaction prediction, there is no mature solution for heterogeneous graph construction.\n\n[1] The graph neural network model.\n\n[2] Transfer learning for solvation free energies: From quantum chemistry to experiments.\n\n[3] Graph attention networks.\n\n[4] MMgnn: A molecular merged graph neural network for explainable solvation free energy prediction.\n\n[5] Conditional graph information bottleneck for molecular relational learning.\n\n[6] Heterogeneous Graph Neural Network.\n\n> **W2.** The difference between CSIE and existing jobs\n\nAlthough methods such as **Glister** and **InfoBatch** have explored data subset selection strategies based on information gain, gradient diversity or label uncertainty in recent years, these methods still have two fundamental limitations when directly transferred to the field of molecular chemistry, which also constitute the starting point and core innovation of the CSIE framework proposed in this study:\n\n**(1) The weak alignment problem between chemical structure and semantics leads to misleading sample selection by existing methods.** Glister uses the sensitivity estimate of model gradient to generalization error to measure the \"importance\" of samples, while InfoBatch relies on historical gradient trajectories to dynamically screen samples. However, these methods **are heavily dependent on the representation or loss function feedback learned by the model in the early stages of training**. In chemical molecular data, early representations often fail to capture complex and subtle changes in chemical properties, such as resonance structures, stereo effects, and non-covalent interactions. This leads to: **1. Over-attention to rare or difficult samples** (e.g., pairs of molecules with extremely strong or weak polarity), thereby sacrificing the representativeness of the chemical space; **2. Ignoring important samples that are \"fuzzy\" in the representation space**, such as molecules with similar topology but significantly different behaviors. The innovation of CSIE is **introducing multimodal semantic embedding (MolCA) as the basic representation**, which is independent of upstream and downstream tasks, and measuring whether the sample brings structural information gain by **information entropy change**, thereby avoiding overfitting dependence on early task models.\n\n**(2) The update computational cost under large-scale data is too high, making it difficult to use for high-throughput chemical screening.**  InfoBatch needs to frequently calculate indicators such as gradient change and loss difference, and Glister needs to evaluate the proxy objective function after each round of training, **which significantly increases the computational overhead in the molecular space (usually tens of thousands of samples**). Especially in practical scenarios, such as drug discovery or solubility screening, training cost is one of the deployment difficulties. The combined strategy proposed by CSIE has the following three efficiency advantages: the approximate nearest neighbor graph (HNSW) in sparse high-dimensional space can efficiently estimate the relative information density between samples; the submodular function structure ensures that the information gain of each round of subset optimization is monotonically constant and the optimization converges; the EM mechanism is introduced to iteratively correct the insertion bias to avoid the local optimum caused by one-time greedy selection. Through these designs, CSIE controls the time complexity to _O(log(n))_, which has the practical potential to process real-scale chemical data.\n\n> **W3.** Time and memory efficiency issues\n\nRegarding the time efficiency, we explain as follows: During the model training process, the CSIE framework reduces the time and space costs mainly by reducing the size of the data set. For example, in the FreeSolv data set, by filtering samples through the CSIE framework, only 60% of the training data is needed to achieve performance that exceeds all training data. Therefore, for this data set, it can be said that CSIE saves 40% of the training cost and achieves better results. In Table 15 of Appendix E.5.2, the superiority of the CSIE framework in transfer learning is more intuitively demonstrated. It can achieve results that exceed 30% of the data with only 5% of the data, and the training time is more than 130 hours. Similarly, smaller data sets will also occupy less memory, but it should be acknowledged that CSIE is a core subset screening framework and does not affect the memory of the model.\n\n---\n\n> **Q1.** discussion about sample selection of CSIE\n\nIt should be clarified that the amount of information is only related to the chemical semantic features of the sample itself, and has nothing to do with the prediction results. This article uses both molecular graph features and semantic features for encoding, and uses the general chemical model MolCA pre-trained on PubChem to fully extract sample features and ensure the completeness of sample chemical semantics as much as possible. Therefore, the CSIE framework does not prefer difficult samples. It prefers to select samples that **are not yet included in the chemical space of the core subset or are in small numbers**. In addition, in Section 3.3, we also visualized the screening effect of CSIE itself on the dataset: we divided the dataset according to solvent type, and evaluated the sample category distribution of the dataset before and after CSIE screening. It can be clearly seen that CSIE can indeed alleviate the redundancy problem of the dataset, obtain a more balanced dataset, and reduce overfitting.\n\n---\n\nThank you again for your thoughtful and constructive review. Your feedback has been valuable in helping us improve the clarity, rigor, and positioning of our work. We have carefully revised the manuscript to implement your suggestions. We sincerely hope that our detailed response has adequately addressed your concerns. If the answer is yes, we would be grateful if you would consider revising the overall assessment of the manuscript. Of course, we are happy to provide any further clarification or participate in follow-up discussions as needed."}]

Author rebuttal:

Meta-review: This paper proposes Core Subset Iterative Extraction (CSIE), a sample selection framework for improving molecular property prediction, and Asymmetric Molecular Graph Neural Network (AMGNN), a GNN architecture for solute-solvent interactions. The idea is considered theoretically grounded (24Q7, 2Vf7, vRGH), impactful for AI4Science (24Q7, vRGh), and novel (Reviewers 2Vf7, vRGh). Although some weaknesses remain, including limited clarity, modest performance gains (2Vf7, Lzfx), fortunately, the authors have addressed the main issues through a detailed rebuttal providing additional clarification and experiment results.
Decision: Accept (poster)