Reinforced Active Learning for Large-Scale Virtual Screening with Learnable Policy Model
Authors: Yicong Chen
Jiahua Rao
Jiancong Xie
Dahao Xu
Zhen WANG
Yuedong Yang
Venue: NeurIPS 2025 poster ()
Keywords: Virtual Screening
Active Learning
Molecular Activity
Drug Discovery
Primary area: neuroscience_and_cognitive_science
TL;DR: We present GLARE, a reinforced active learning framework that improves virtual screening efficiency by dynamically optimizing molecular selection while balancing chemical diversity, biological relevance, and computational constraints.

Abstract: Virtual Screening (VS) is vital for drug discovery but struggles with low hit rates and high computational costs. While Active Learning (AL) has shown promise in improving the efficiency of VS, traditional methods rely on inflexible and handcrafted heuristics, limiting adaptability in complex chemical spaces, particularly in balancing molecular diversity and selection accuracy. 
To overcome these challenges, we propose GLARE, a reinforced active learning framework that reformulates VS as a Markov Decision Process (MDP). Using Group Relative Policy Optimization (GRPO), GLARE dynamically balances chemical diversity, biological relevance, and computational constraints, eliminating the need for inflexible heuristics.
Experiments show GLARE outperforms state-of-the-art AL methods, with a 64.8% average improvement in Enrichment Factors (EF). Additionally, GLARE enhances the performance of VS foundation models like DrugCLIP, achieving up to an 8-fold improvement in  EF$_{0.5\\%}$ with as few as 15 active molecules. These results highlight the transformative potential of GLARE for adaptive and efficient drug discovery.

Reinforced Active Learning for Large-Scale Virtual Screening with Learnable Policy Model
Yicong Chen '", Jiahua Raol** Jiancong Xie', Dahao Xu', Zhen Wang', Yuedong Yang!
Sun Yat-sen University "Equal Contribution 'Corresponding Author edu.
{chenyc57, xiejc3, @mail2. sysu. edu. cn {raojh7, wangzh665, yangyd25 sysu. edu.cn
Abstract
Virtual Screening (VS) is vital for drug discovery but struggles with low hit rates and high computational costs. While Active Learning (AL) has shown promise in im- proving the efficiency of VS, traditional methods rely inflexible and handerafted heuristics, limiting adaptability in complex chemical spaces, particularly in balanc- ing molecular diversity and selection accuracy. To overcome these challenges, we propose G a reinforced active learning framework that reformulates VS Markov Decision Process (MDP). Using Group Relative Policy Optimization (GRPO), GLARE dynamically balances chemical diversity, biological relevance, and computational constraints, eliminating the need for inflexible heuristies, Ex- periments show GLARE outperforms state-of-the-ee AL methods, with a 64.8% average improvement Enrichment Factors (EF). Additionally, GLARE enhances the performance of VS foundation models like DrugCLIP, achieving up an 8-fold improvement in 5.5%% with few 15 active molecules. These results highlight the transformative potential of GLARE for adaptive and efficient drug discovery.
Introduction
Virtual Screening (VS) plays pivotal role in drug design, facilitating the identification of active molecules that interact with biological targets from vast chemical libraries [30]. By predicting the likelihood of activity for each molecule, VS reduces the need for exhaustive experimental testing 44 62 40 However, despite its importance, VS faces significant challenges, including low
hit rates (the proportion of active molecules identified) [46] and the computational cost of screening
ultra-large libraries (106 10 compounds) [36] [3]. These limitations have driven the search for more efficient and accurate methodologies to improve the scalability and performance of VS (60]
Active Learning (AL) has shown promise virtual screening by iteratively refining models through
the selective acquisition of informative molecular samples [4 (26.. Popular strategies often involve uncertainty-base based, diversity-basee ensure broad exploration by selecting diverse samples, while uncertainty-biit basedd methods focus
and hybrid approaches [48] 64 7 Diversity- based approaches
refining predictions by prioritizing uncertain molecules [5] [55]. However, both of them rely on
hand-crafted selection strategies. They often heavily dependent on the diversity and size of the initial molecular pool, thus lacking the flexibility to dynamically balance exploration and exploitation when new data acquired [5).
Recent advancements AL have sought to improve efficiency by introducing frameworks that dynamically combine or select strategies, reducing the need for manual intervention [35] 19. For
instance, SelectAL [15] adjusts AL strategies based on specific tasks and computational budgets, while AutoAL
[57] employs differentiable framework to identify optimal strategies. Although
Source code available http://gthha..cmm//mmmc--AlAl..
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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these approaches mark progress, they remain focused on optimizing the selection of strategies from predefined set (i.e., "learning to select rules"). This approach does not enable models directly learn how to identify the most promising candidate molecules (i.e., "learning to select molecules"). As result, these methods are not well-suited for drug discovery, where the vast and complex chemical space requires highly adaptive and intelligent exploration to effectively balance structural diversity, biological relevance, and computational efficiency [0].
In this work, we introduce GLARE, GRPO-based Learning framework for Active REinforced screening, designed to overcome the limitations of traditional active learning methods and enhance suge-scale virtual screening. GLARE reformulates the virtual screening process as Markov Decision Process (MDP), enabling Reinforcement Learning (RL) to dynamically optimize molecular selection strategies. By leveraging Group Relative Policy Optimization (GRPO) 49), GLARE eliminates the reliance on manually-designe spaces. By balancing molecular diversity, biological relevance, and computational efficiency, GLARE provides a robust and scalable solution for AL in drug discovery.
heuristies, learning to adaptively screen large-scale chemical
Through extensive experiments, we demonstrate that GLARE significantly outperforms state-of-the- art AL methods, achieving an average 64.8% improvement in Enrichment Factors (EF). Furthermore, it improves the performance of virtual screening foundation models like DrugCLIP, achieving up to an 8-fold increase in E.p.5% with as few as 15 active molecules given. These results highlight the transformative potential of GLARE in enabling adaptive, efficient, and intelligent molecular selection for drug discovery.
In summary, our contributions are threefold:
We reformulate virtual screening as Markov Decision Process (MDP), enabling reinforcement learning to dynamically optimize molecular selection.
By utilizing learnable policy model trained with Group Relative Policy Optimization (GRPO), GLARE eliminates the need for manually-designed heuristics and learns to adaptively screen the complex chemical spaces.
Extensive experiments demonstrate that GLARE significantly outperforms state-of-the-arr active learning methods, enhancing virtual screening efficiency and scalability, while also substantially improving the performance of virtual screening foundation models.
2
Related Works
2.1 Virtual Screening Methods
Virtual screening methods are broadly categorized into docking-based and deep learning-bai based approaches [12]. Docking-based methods [16) [56] use predefined force fields estimate binding scores, predicting the binding energy, optimal orientation, and conformation of small molecule within protein binding site 23 However, their reliance on rigid scoring functions and static assumptions limits their accuracy and scalability in complex chemical spaces (21]
Deep learning- virtual screening approaches typically focus on predicting drug-target interac- tions and binding affinities [34] 61 32 28
39 29]] These methods represent drugs and target proteins using various encoding techniques, such as molecular fingerprints, SMILES strings, graph- based representations [38] 37 for drugs, and sequence-basee
63
or structural embeddings for proteins. These methods leverage large experimental datasets and known interaction information to predict new potential interactions 220 9 (50,. offering a more comprehensive perspective for drug discovery.
However, as the size of chemical and biological data grows, the computational cost and inefficiency of exhaustively labeling data become significant challenges [3, 46). This highlights the need for
active learning to prioritize data points that most informative for model improvement 42 41 enabling more effective exploration of the chemical space.
2.2 Active Learning
Active Learning (AL) has been widely applied in drug discovery and development to iteratively guide the selection of unlabeled data for annotation, maximizing model performance with minimal
data [14]]]]]]] 26 5 55, 8. Common strategies include uncertainty-bast based sampling, diversity-basee

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sampling, and hybrid approaches 48 64 71. For example, Graff et al. [14] explored pool-based AL using uncertainty- based acquisition functions accelerate virtual sereeming. Li and Rangarajan [26] proposed diversity-mxxmizziin strategies for graph neural networks, improving chemical space exploration. Van Tilborg and Grisoni [55] systematically evaluated multiple AL strategies under low-data regimes by combining acquisition functions like uncertainty and diversity measures.
Despite these advancements, most AL methods rely on manually designed heuristics or static strate- gies, limiting their adaptability to dynamic and complex chemical spaces 10]. Recent general AL frameworks such as SelectAL [15] and AutoAL [57] have introduced task-specific optimizations. Se- lectAL adjusts AL strategies based on computational budgets, while AutoAL employs a differentiable framework identify optimal strategies.
Other works have attempted to learn acquisition functions using reinforcement learning approaches, primarily based Q-learning [11 6 While these methods have shown promise in domains like semantic and image segmentation, they face challenges in drug discovery, including sparse rewards and the high dimensionality molecular representations. As result, they still fall short in navigating vast chemical spaces efficiently under low-data conditions.
In contrast, our work leverages Group Relative Policy Optimization (GRPO) [4],, which provides more stable and efficient policy updates compared to Q-learning. GRPO enables dynamic optimization of the acquisition policy, allowing better exploration and exploitation in complex and evolving chemical spaces. By directly addressing the limitations of existing methods, our approach significantly enhances hit discovery efficiency and scalability, particularly in low-data drug discovery scenarios.
3 Methodology
3.1 Problem Definition
Virtual screening aims to identify active molecules that interact with biological target from vast molecule library M. The library M contains a large number of unlabeled molecules, with only small fraction being active. Each molecule m, E MM can be labeled as active (И4 1) or inactive (y: 0) after annotation. The challenge lies in maximizing the identification of active molecules while minimizing annotation costs.
To address this, active learning is employed. The process iteratively proceeds as follows:
Train: Train the surrogate model Φe using small labeled dataset D.
2. Query: Apply selection strategy ş to choose molecules Mselett from the unlabeled library M.
Annotate: Label the selected molecules Msllett using the oracle Q, yielding Malell
4. Expand: Add mabell to the labeled dataset D)
The iteration continues until the annotation budget exhausted, with the final labeled dataset D expected to contain a substantial number of active molecules.
Traditional active learning methods rely predefined selection strategies c, often referred to as acquisition functions (e.g., greedy algorithms or mutual information maximization (88). These fixed strategies unable adapt to the characteristics of different datasets, which limits their generalizability and performance across diverse tasks. In this case, the selection strategy can be expressed as:
myslect arg max M s(m, Φo),
(I)
where <(m, Φe) scores molecules based predefined, hand-crafted acquisition function and de is the surrogate model trained on the labeled dataset D.
To address these limitations, we directly replace the predefined selection strategy with a learnable policy network ne. i.e., 5 = wo, which is capable of dynamically adapting different datasets through training. The selection process in our method is expressed as:
Mssleet ~ अ { D),
where Te (m) D) represents probability distribution the labeled dataset D. Unlike fixed strategies, the policy network To is continuously updated during
the unlabeled molecules M conditioned
the training step in active learning, adapting as me 2, mp. This adaptability allows the selection strategy to dynamically adjust to different datasets and tasks.
3

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One Artion
Input
Labeled Dutaset
mi
my
Step
Train
Group
8x
P
Policy Model Ra
C
Step
Expand
State
Enriched Activa Mol
Nothic Lewening Cydee
Mu. Eiicadar
Step 3:
Annotate
Unlabeled Library M
Action Sczzing
Drvergenzee
Uncurtiintl
ne (nls)
0 GRPO Loss
Ruward
Step 2:
Oulput
mi
Query
Action
all
f
nil
+
Distarc Betwean and
Clp Opassorr wit Scale
Figure 1: The overall framework of GLARE.
3.2 Overview of GLARE Framework
Figure ! presents the framework of the proposed GLARE. Similar to traditional active learning methods, also includes four steps: Train, Query, Annotate, and Expand. However, in the query stage, GLARE introduces a Iearnable policy model my to adaptively select molccules for annotation after formulating Markov Decision Process (MDP), addressing the diverse and complex nature of virtual screening tasks. The policy model Ty consists of a Molecular Encoder, which extracts key features from the molecule library M, and Action Scoring Layer, which selects molecules for annotation based on these features. To optimize the policy model and enhance overall performance, GLARE employs Group Relative Policy Optimization (GRPO), balancing structural diversity, biological relevance, and computational efficiency, ensuring adaptive and efficient molecular selection.
3.3 Reinforcement Learning for the Policy Model
The optimization task of the policy model mg is reformulated as a Markov Decision Process (MDP) within the framework reinforced active learning- based virtual screening. In this setup, candidate molecules divided into series of groups, and during each active learning iteration, the policy model sequentially selects molecules for annotation. The MDP is defined by the following key components:
State: The state represents the embedding x; of the molecule m; in the group that currently being considered for annotation.
Action:
The action a; [p", .p' corresponds to the probabilities of "exclude" (p") and "select" (p") for a molecule m,, The policy model no determines the action for each molecule based on its state 8,, denoted [o(o)ss))
Reward:
The reward evaluates the quality of actions, balancing two critical aspects:
(1) Exploitation: Reflects the immediate utility of the selected molecules, specifically whether they are active.
(2) Exploration: Captures the long-term benefit of selecting molecules, providing insights into the broader library, even for inactive molecules. The reward ri of policy network for a molecule m, defined as:
r =
(3: at,y, Ui.
where ys represents the label of m, provided by the oracle Ω and is discount factor that adjusts the exploration contribution which will be discussed Section 3.4.3
By framing the optimization task in this manner, the algorithms of molecular selection and model training for GLARE are summarized in A Algorithm and Algorithm 2 respectively. After optimization in MDP, the policy model na learns balance structural diversity and biological relevance, enabling adaptive and efficient molecular selection.

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Algorithm Molecular Selection under MDP
Algorithm 2 Policy Model Training with GRPO
Input: Unlabeled library M, Budget B
Input: Labeled dataset D
Output: Labeled dataset
Output: Trained policy model me
I: while [B| * do
1: for episode E 21,2.... ..., N do
2: Divide MM into groups
2: Divide into groups G
3: for j f1, [ do
3: for j E (1, [|] do
embed {m .... m.) Gj
emmbed {m....., E G3
4:
Construct state 8, using Xj Xj-
4: Xj
5:
5: Construct state 8, using Xj
6:
{a1...... ................................ aj 1 + (8;)
6: a; TT(ss))))))))))),){{))
7:
end for
7: Receive reward r after taken a,
8: Sample Maclcct with a
8: Do gradient descent step on GRRO
9: Miatel = 22 setecti B)
9: Update xo with
10: D D + Myelect
10: end for
11:
end while
11: end for
12:
return the latest labeled dataset D
12: return the latest policy model T
3.4 Policy Model for Molecular Selection
The policy model my is responsible for determining the probabilities of actions based on the molecular features extracted by the encoder, allowing GLARE to adaptively select molecules for annotation. Given the molecular embedding 2; as state the policy network predicts the probability of action a,, denoted as (p(a; (s)). The policy model consists of Molecular Encoder (Section 3.4.1 and Action Scoring Layer (Section 3.4.2 optimized with GRPO strategy (Section 3.4.3
3.4.1 Molecular Encoder
To enable the policy model my take actions based on molecules, a molecular encoder fene is utilized to extract features h; from the input molecules. The encoder fene is flexible component that can adapt to various molecular representations. In this study, we consider three types of Senci
Molecular Fingerprint MLP. Each molecule is represented using 1,024-bit Extended Connectiv- ity Fingerprint (ECFP) with radius of 2 Á 44). These fingerprints are processed by MultiLayer Perceptron (MLP) to extract molecular features hy:
h; a (Wn,. Iz b..)
where the activation function, and Wm. and bn, are learnable parameters of the MLP.
Graph Neural Network (GNN). For molecules represented as graph structures, Graph Isomor- phism Network (GIN) (59] is implemented. The GIN updates the features of each atom Fiş the molecule m. and an add pooling operation aggregates the features into a molecular hidden feature hy:
T W, (1+ (1+ Fag
(1-1)
Σ
(L-1) Jitk
h;
Σ
J
(5)
kEN())
JEV(()
where W, is a learnable parameter, is a fixed scaling factor, Nj) denotes the neighbors of atom j and (ii denotes the set of all atoms in molecule m,,
Pretrained GNN. Pretrained molecular representation models, such as GraphMVP 27 and 3DInfomax [51] can also be adopted as molecular encoders. These pretrained models leverage prior knowledge from large molecular datasets to provide robust initializations, thereby improving training efficiency and enhancing the quality of molecular representations.
3.4.2 Action Scoring Layer
Extracted by the encoder, the hidden feature h; is passed through a fully connected layer to compute an action score 2; for each possible action (e.g.. "exclude" or "select"):
W. hú + bas
(6)
where Wa and b, are Iearnale parameters of the action scoring layer.
5

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The scores 2 are then transformed into probabilities using a softmax function to gain action
a; softmax(z:).
This transformation ensures that the output is a valid probability distribution, where the sum of probabilities for all possible actions equals to I. The probability distribution represents the confidence of model in selecting or excluding a specific molecule.
3.4.3 Training Strategy Based on GRPO
The goal of the policy model Te in active learning is to identify the most informative molecules from group of candidate molecules, which naturally aligns with the objective of the widely used Group Relative Policy Optimization (GRPO) strategy 49). Thus, we apply an enhanced GRPO to train the policy model Ty with a carefully designed reward function mentioned in Equation 3 based on the requirements of active learning. We further enhance the reward by incorporating uncertainty, ensuring better exploration during molecular selection.
Objective of GRPO in Reinforced Active Learning During the training phase of active learning, the GRPO strategy is adopted to optimize the policy model To, maximizing the following objective group of candidate molecules G:
(a(())8))) Tota (a:[s)) As, clip
To(as (si) Told (ag (s:)
LGRPO G
min
c, +0 - ( [Tol]
-
(8)
where Wrefs Told, and my are the policy model in previous active learning iteration, previous training epoch, and current epoch, respectively. clip(-) a clip operation for stabilizing training and e the clip ratio [47]. The definition of advantage A; and KL divergence [KL[--] as follows:
A, = Ti - mean(r)
Tiar(ai (s) log Tier (Te((a)(8s)
DeK.. [ool[Tol|Taa [ee]]
1,
(r)
To (a(a)));)
To(a:
where is the reward vector of the group of candidate molecules G.
The GRPO objective ensures that the policy model To focuses on selecting the most informative molecules while maintaining training stability. Additionally, the KL divergence term prevents overfitting, especially when meeting the limited available training data active learning due to the annotation budget.
Exploration Modification Although the sampling process of the policy model To already incorpo- rates randomness (as discussed in Section 3.4.2). explicitly considering the value of exploration the reward function further enhances its ability to avoid aimless uninformative exploration (5). This is achieved by introducing gradient-based uncertainty to estimate the exploration value and designing a discount factor u, modify the reward.
The discount factor u; is defined as:
U;
Vis
if y1 and aisy
(10)
1,
y
+
where v is the normalized 2-norm of the gradient gh which is defined as:
9 12 |1 9 1 = [p] T(y: = 0)) + Ip T(y, = 1))3] 11 (s)))
(11)
As defined in 2). molecules with higher uncertainty yield greater I = -2) = 1) resulting in greater 2-norm values of the gradient gh The detailed derivation is provided in the Appendix[A]
By introducing the discount factor ue the model reduces the negative reward for inactive molecules with high uncertainty. This adjustment ensures such molecules can still contribute valuable informa- tion for model training and exploration despite being inactive.

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Table 1: Enrichment Factor (EF) on ALDHI, PKM2 and VDR.
ALDHI
PKM2
VDR
Method
Model
Strategy
Iter 10
Iter 16
Iter
Iter 16
Iter 10
Iter 16
Random
1.020
0.988
0.868
0.994
1.071
1.036
Similarity
2.438
2.362
1.943
2.113
2.438
2.664
MLP
Uncertainty
0.761
0.791
1.364
1.087
0.833
0.888
Greedy
5.196
5.675
2.604
3.729
3.530
4.055
TesAL
MI
4.794
5.116
3.059
4.070
4.363
4.529
55
Random
1,020
0.988
0.868
1.036
1.071
0.994
Similarity
2.438
2.362
1.943
2.113
2.419
2.664
GNN
Uncertainty
0.907
0.923
0.868
0.777
0.754
0.681
Greedy
3.126
3.750
1.901
2.548
3.014
2.871
MI
3.482
4.077
1.777
2.952
4.085
4.085
MLP
Policy
6.574
6,535
2.067
5.904
3.173
6.512
GLARE
GNN Pre. GNN
Policy
6.179
7.067
2,480
7.146
7.535
7.104
Policy
7.274
7.205
4.547
7.768
7.932
7.992
Experiments
4.1 Experimental Setup
Datasets. We evaluate GLARE on two widely used virtual screening benchmarks: LIT-PCBA [52] and Enamine [ For LIT-PCBA, we conduct experiments on the three subsets (ALDHI, PKM2,
and VDR) with most experimentally validated molecules adhering to the protocols in [55]. Each subset is allocated a total annotation budget of 1% (1,000 molecules) of the library (100,000), with 64 molecules annotated per iteration. Enamine is large-scale screening database with subsets named Enamine50k and EnamineHTS. the total annotation budget of Enamine50k set as 6% (3,000) the library (50,240), with 1 % (500) per active learning iteration. Due to the large scale of EnamineHTSS, the budget reduced to 0.6% (12,600) and 1.2% (25,200) of the library (2, 1,,144,, with 0. % (2,100) and 0.2% (4,200) per iteration, noted as EnamineHTS-Q0 1 and EnamineHTS-ON respectively.
Baselines. We evaluate GLARE against two state-of-the-art active learning methods for virtual screening: TesAL [55], with two model architectures and diverse acquisition functions, and PtAL (5]. which leverages pretrained models like MoLFormer 455 and MolCLR [58]. Additional baselines include D-MPNN [17]] RF 31, and LightGBM (24), with acquisition functions such as greedy,
Mutual Information (MI) 18, Upper Confidence Bound (UCB), uncertainty, similarity, and random.
Metrics. Enrichment Factor (EF) is used to evaluate virtual screening performance, calculated
as EF [Damel
Manteel where Dnetive and Maerive represent active molecules in the selection and M
DD
entire library, respectively. Retrieving Rate (RR,) measures the ability retrieve the highly active molecules, defined as RR,,
[appal where Disp-n number of top n active molecules selected.
4.2 Evaluation of Active Learning for Virtual Screening
4.2.1 Evaluation on General Benchmarks
We evaluate GLARE on general active learning virtual screening benchmarks using three subsets, ALDHI, PKM2, and YDR from LIT-PCBA. following the protocols (55). Table reports the Enrichment Factor (EF) scores at the mid-phase (10th iteration) and final-phase (16th iteration). Results for all iterations are shown in Appendix [B.I GLARE consistently outperforms all baseline methods, achieving significant improvements. Notably, GLARE with a pretrained GNN achieves the highest EF scores of 7. 7.205, 7.768, and 7.992 on ALDHI, PKM2, and VDR, reflecting improvements of 27.0%, 90.9%, and 76.5%, respectively, over the best baseline. Even without a pretrained model, GLARE with MLP and GNN significantly exceeds the best baseline almost scenarios, showcasing its strong performance across various molecular encoders. These findings highlight that learning policy model for adaptive molecular selection strategies, rather than relying on predefined functions, substantially enhances performance.

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Table Retrieving Rate (RR500 and 000,000) on Enamine50k and EnamineHTS. The upper part the comparison among non-pretrained models, while the lower part corresponds to the pretrained.
5namine50k
EnamineHTS-O0.
EnamineHTS-002
Method
Model
Strategy
Iter 4
Iter
Iter 4
Iter
Iter 4
Iter 6
Greedy
0.4316
0.5452
0.4360
0.5474
0.5802
0.6818
RF
UCB
0.2724
0.3708
0.2796
0.3582
0.4260
0.5096
LightGBM
Greedy
0.5392
0.6944
0.5218
0.6778
0.7018
0.8250
24
UCB
0.4459
0.4088
0,4346
0.5614
0.5448
0.6278
D-MPNN
Greedy
0.4736
0.6532
0.5762
0.7276
0.7784
0.8988
17
UCB
0.4863
0.6688
0.5932
0.7462
0.8046
0.8974
GLARE
GNN
Policy 0.4926
0.7424
0.4385
0.7526
0.7024
0.9032
MolCLR
Greedy 0,5000
0.6708
0.5512
0.7278
0.7574
0.8698
PILL
58
UCB
0.4972
0.6796
0.5384
0.7276
0.7624
0.8844
S
MoLFormer
Greedy
0,5812
0.7836
0.6742
0.8158
0.8534
0.9224
45
UCB
0.6054
0.7924
0.6976
0.8412
0.8594
0.9338
GLARE
Pre. GNN
Policy
0.7765
0.8869
0.8637
0.9181
0.9618
0.9732
4.2.2 Evaluation on Large-scale Benchmarks
To further validate the scalability of GLARE. we conducted experiments on larger-scale virtual screening benchmarks. presents the RR, scores of various methods at the mid-phase (4th iteration) and final-phase (6th iteration) of active learning. More details are shown in Appendix B.2
Among all the methods, GLARE with pretrained GNN achieves the best performance, benefiting from faster initialization, which enables the model to effectively identify active molecules early in the active learning process. On Enamine50k. GLARE with a pretrained GNN achieves RR500 scores of 0.7765 and 0,8869 at the mid and final phases, representing improvements of 28.3% and 11.9% over the best baseline (PtAL with MoLFormer and UCB). Furthermore, GLARE demonstrates excellent efficiency on the larger EnamineHTS, achieving RR1,000 scores of 0.9181 (EnamineHTS- 0.1) and 0.9732 (EnamineHTS-0.2), with substantial improvements over the best baseline. Although underperform at the beginning. GLARE with GNN surpasses all non-pretrained methods and PtAL with MoLCLR at final-phase despite MolCLR being pretrained GNN, which further validate the superiority of our adaptive selection strategy. These experiments on large-scale benchmarks clearly demonstrate the scalability and effectiveness of GLARE for large-scale virtual screening tasks.
4.3 Improvement over Foundation Virtual Sereening Methods
To further showcase the practicality of GLARE, we apply it to enhance the performance of virtual screening foundation models with limited number active molecules. The experiment conducted on the entire LIT-PCBA dataset using the pretrained DrugCI.IP 122 with a budget of n active molccules, denoted as GLARE(n)..
Table Summarizes the results of various virtual screening methods on the LIT-PCBA. Impressively, the screening performance improves by 46.7% on EFo.5% even with the addition of just single active molecule during active learning. With 15 additional active molecules, GLARE(15) achieves an 8-fold improvement EFo.5% compared the baseline model DrugCLIP. These results highlight that our method not only enables efficient virtual screening but also significantly enhances the capability of existing foundational virtual screening models with a minimal budget of active molecules.
4.4 Ablation Studies and Visualization
Ablation studies (Figure a) demonstrate the effectiveness of the GRPO- based reinforced active learning in GLARE. GLARE w/o AL discards the active learning process, while GLARE w/o Policy adopts an active learning process but without reinforcement learning. Both of them perform significantly worse, even with larger annotation budget. Experiments on batch size and total annotation budget reveal that medium batch sizes (64 and 128) strike the best balance between efficiency and performance, while excessively small or large batch sizes lead to trade-offs between screening time and effectiveness (Figure b and c). More analysis is shown in Appendix B.4

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Table 3: AUROC, BEDROC and EF, on LIT-PCBA
AUROC(%)
BEDROC(%)
E.0.s%
EF1%
EF5%
Surflex [22]
51.47
2.50
Glide-SP
[6
53.15
4.00
3.17
3.41
2.01
Planet 61
57.31
4.64
3.87
2.43
Gnina 32
60.93
5.40
4.63
DeepDTA 34
56.27
2.53
1.47
BigBind 4
60.80
3.82
DrugCLIP
57.17
6.23
8.56
5.51
2.27
GLARE(1)
57.21
9.65
12.56
7.65
2.69
GLARE(3)
61.76
18.76
23.32
13.61
4.25
GLARE(IO)
68.33
25.17
57.93
32.31
8.21
GLARE(15)
70.17
28.49
77.03
40.64
9.94
d.
RL-87 Pollcy
3.146
7:24
2.54
2,100
UMARE
USARS
6.
af pefferent Modgds sutt
Time Comareyg of Eorex tiidh
ULMP:: 200 IC00 Madgers 2001
U1ARB: 239 MiScsiin 1000 Budgess Dee
175
125
SEL2
Batch 236
Annitatiin Budget
Figure 2: Results of ablation studies and visualization. a. Ablation study of GLARE for active learning and reinforcement learning. b, Enrichment Factor (EF) under different total annotation budgets and batch sizes for GLARE. Time consumption for different batch sizes of GLARE. d, Visualization results the active learning selection process for TesAL (upper) and GLARE (lower).
The UMAP 31 visualization (Figure d) shows GLARE explores broader and more diverse chemical space compared to TesAL, achieving higher hit rates at both the mid-phase (10th iteration) and final-phase (16th iteration) of active learning. This highlights the superior adaptability and effectiveness of GLARE in discovering novel active molecules while maintaining high structural diversity.
5 Discussion
This work presents GLARE, an active learning-based virtual screening framework that uses a GRPO- based strategy to efficiently explore chemical space with limited annotation budget. Experiments show the superior performance of GLARE, significantly improving EFo.5% and hit rates on datasets like LIT-PCBA and ALDHI, while visualizations highlight its ability identify diverse and novel active molecules. GLARE enhances molecule discovery efficiency and reduces costs, making it valuable tool for drug discovery. However, GLARE faces challenges when scaling to ultra-large chemical spaces (e.g., 10 molecules) due to increased time costs, and its performance tends to decline as the annotation budget increases. Future work could focus on improving scalability and mitigating the diminishing returns associated with larger annotation budgets.

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Acknowledgments and Disclosure of Funding
This study has been supported by the National Natural Science Foundation of China [T2394502]. the Guangdong S&T Program [2024B11] 11 111111100000 1400011] the National Natural Science Foundation of China [62302537], the China Postdoctoral Science Foundation [2025M771540, GZB20250391J, the Guangzhou Basic and Applied Basic Research Foundation [2024A0444449), and the Lingang Laboratory [LGL-8888).
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A Proof for Uncertainty Estimation Based on Gradient
The policy model can be simply modeled as mo(ai)ss) W wo(ss) b, then cross entropy loss between a, = [ P!. and yi is:
+ 1(Us 1) In !
(ce(a yi) [1y 0) In1( D9.
Pf In (o. + whrw(ss)) Wn: w(ss).
(12)
We define xh JW. Ce 1k) as the gradients corresponding to yk=
a
g (Wow + ww(ss Wx-w(s)) w(s:) 1(y = wr(ss)
1
(13)
la.y 1lys = k:] w(s)).
Replacing with ys = arg max (.)) then the 2-norm of whole gradient g = aW (c De) is:
l 2 11 of 111 = - T(ū: = 0)) + ip T(ú: 1)] ( 1
(14)
Evidently, high-uncertainty samples yield greater [Ig - ( = 0)]² + |p - ( = 1)} since V/ = arg max(a;) (a;). i.e., the length of goh is long. Therefore, samples with high uncertainty tend to have great 2-norm of the gradient.
B
Additional Results
B.1
Detailed Results of General Benchmarks
Table 4
presents the complete results of GLARE three LIT-PCBA subsets: ALDHI, PKM2, and VDR, covering total of 16 iterations, with an annotation budget of 64 allocated for each iteration.
Table 4: Enrichment Factor (EF) on ALDHI, PKM2 and VDR of each Iteration.
ALDHI
РKM2
VDR
Iter
Pre. GNN
Pre. GNN
Pre.
MLP
GNN
MLP
GNN
MLP
GNN
GNN
0.483
0.483
0.483
1.754
1.754
1.754
0.869
0.869
0.869
2 1.910
1.500
1.364
1.559
1.559
1.559
0.768
0.768
2.303
3
3.802
3.516
2.566
1.404
1.404
1.404
0.687
1.374
2.062
4
4.303
3.573
3.792
1.277
1.915
1.277
1.866
3.110
3.110
5
4.496
4.260
4.674
1.170
1.756
1.170
1.705
3.978
3.978
6
4.877
4.330
5.873
1.081
1.621
1.081
2.092
5.753
5.230
7
5.540
4.510
6.614
1.505
1.505
2.007
2.422
6.781
6.297
8
5.855
5,024
6.875
1.405
1.405
1.873
2.255
7.218
6.315
9
6.135
5.764
7.045
2.196
2.196
2.635
2.533
7.598
6,754
10 6.574
6.179
7.274
2.067
2.480
4.547
3.173
7.535
7.932
11
6.797
6.631
7.352
1.952
3.904
5.076
4.114
7.854
8.602
12
6.754
6.780
7.289
2.219
4.809
6.288
5.662
8.138
8.846
13
6.765
6,812
7.212
2.811
5.974
7.029
5.372
8.058
8.394
14 6.818
6.840
7.168
3.013
7.029
7.364
6.069
7.666
7,985
15
6,638
7.028
7.335
4.154
7.030
7.349
6.701
7.310
7.919
6.535
7.067
7.205
5.904
7.146
7.768
6.512
7.104
7.992
B.2 Detailed Results of Large-scale Benchmarks
Table preseens the complete results of GLARE on Enamine50k and EnamineHTS, covering a total of 6 iterations. For Enamine50k, the annotation budget is 1% (500) of the library per active learning iteration. For EnamineHTS, the annotation budget 0.6% (12,600) and 1.2% (25,200) of the library per active learning iteration, noted as EnamineHTS-O0 and EnamineHTS- 0.22, respectively.
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Table 5: Retrieving Rate (RR500 and 1,0,000) on Enamine50k and EnamineHTS of each Iteration.
Enamine50k
EnamineHTS-O.
EnamineHTS-0.2
Iter
GNN
Pre. GNN
GNN
Pre. GNN
GNN
Pre. GNN
0.0162
0.0167
0.0010
0.0010
0.0010
0.0010
2
0.1325
0.2340
0.0194
0.7150
0.0473
0.7011
0.3463
0.5829
0.2595
0.8197
0.3955
0.9435
0.4926
0.7765
0.4385
0.8637
0.7024
0.9618
5
0.6388
0.8387
0.6359
0.8987
0.8314
0,9664
6
0.7424
0.8869
0.7526
0.9181
0,9032
0.9732
B.3 Detailed Results with Standard Deviations
To further validate the stability and robustness of the proposed method, Table presents the detailed experimental results of GLARE on ALDHI, PKM2, and VDR, including the mean performance metrics and their standard deviations (Mean STD). All results were obtained from multiple independent runs to ensure the reliability and statistical significance of the evaluation.
Table 6: Enrichment Factor (EF) and of GLARE on ALDHI at final- (16th iteration).
ALDHI
PKM2
VDR
MLP
6.535 + 0.182
5.904 + 1.312
6.512 + 0.674
GNN
7.067 0.384
7.146 = 1.105
7.104 0.358
Pre. GNN
7.205 + 0.124
7.768 + 0.926
7.992 + 0.425
B.4 Supplement of Ablation Studies
To investigate the effectiveness of active learning, we train two variant models, denoted as GLARE w/o AL and GLARE w/o Policy. Considering maintaining the same size of training data, we design GLARE w/o AL with 1000 labeled samples to start and an additional 1000 budget for screening. GLARE w/o Policy adopts an active learning process but without reinforcement learning. As Table shows, although using twice annotation budget, GLAAR w/o AL. performs the worst, which can be attributed to the absence of active learning, leading to substantial waste of annotation resources. In addition, GLARE w/o Policy also underperforms compared to GLARE due to the lack of adaptive molecular selection strategy. These results demonstrate the advantage of active learning-based virtual screening over conventional one-shot screening.
Table EF of ablation study for active learning and reinforcement learning.
Table 8: EF ablation study under different an- notation budgets and batch sizes on ALDHI.
ALDHI
PKM2
VDR
32
64
128
256
REAL w/o AL REAL w/o Policy REAL
2.106
2.753
2.948
500
7.781
6.459
4.495
1.624
4.127
3.807
4.285
1000
7.114
7.067
5.923
3.385
7.067
7.146
7.104
2000
6.792
6.896
5.911
4.949
We investigate the effects annotation budget per iteration (i.e., batch size) and total budget in active learning, using batch sizes 32/ 64 128 256 and budgets of 500 1,000 2,000. As shown in Table 8 EFs for batch sizes 64 and 128 initially increase but slightly decline as the budget grows. The initial increasement can be attributed the reason that larger budget facilitates better model training and allocates more resources for screening. But the subsequent slight decline occurs because the easily identifiable active molecules are quickly selected the earlier iterations, leaving behind more challenging ones. Due to the large batch size of 256, only a few active learning iterations can be operated, resulting in inferior performance compared to others. EF batch size is the greatest when annotation budget comes to 500, because smaller batch size allows the model to update without delay and avoid wasting the screening budget. However, this comes at the cost of doubled screening time (Figure22 c), with minimal EF improvement when the annotation budget exceeds 1000.
15

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B.5 Time Consumption
To provide a comprehensive comparison, we evaluated both training and inference runtimes on the ALDHI dataset, using the same hyperparameters and hardware as TesAL [55].
Active learning typically involves training predictions on a much larger pool of unlabeled data, As result, inference time becomes the dominant computational cost, especially in large-scale virtual screening scenarios. As shown in the Table 9 while GLARE requires extra training time due to the computational cost of gradient-based uncertainty in reward calculation, this overhead is confined the training phase. During inference, reward computation is not required, enabling GLARE to achieve inference speeds comparable to TcsAL.
small number of labeled samples while making
Table The runtime of training and inference on ALDHI.
Training Time (Sec. per Epoch)
Inference Time (Sec.)
Method
Model
MLP
1.5
18.2
TesAL
GNN
4.2
148.6
MLP
4.7
14.5
GLARE
GNN
8.9
152.6
The characteristic particularly important for large- scale dataset. To further demonstrate the scalability of our approach, we tested it on the AmpC dataset (10a compounds) using the same hyper- parameters as (5. As shown in the Table TÔ GLARR achieves inference speeds comparable to the baseline PtAL. across both medium (EnamineHTS-O1. 2 million compounds) and ultra-large (AmpC, 99.5 million compounds) datasets. As dataset size increases, inference time becomes even more significant. Our method maintains inference speed similar to baseline methods while delivering much higher accuracy, making especially well-suited for ultra-large virtual screening tasks.
Table 10: The runtime of training and inference on EnamineHTS and AmpC.
EnamineHTS--0.1 (2m)
AmpC (99.5m)
Method
Model
Train Time
Infer Time (Sec.)
Train Time (Sec.)
Infer Time
(Sec.)
(Sec.)
GNN
41.7
825.5
2274.6
40423.3
PIAL
Pre. GNN
68.3
922.3
3861.5
45374.8
GNN
84.2
913.3
4868.2
49662.4
GLARE
Pre. GNN
155.9
1179.1
8237.6
55127.2
B.6 Detailed Results of Training Loss
Regarding training convergence, we ensured sufficient training within each active learning round. Following the TesAL baseline, we trained each model for 50 epochs per iteration and monitored the training loss. As shown in Table [T] the loss curves for MLP, GNN, and Pre. GNN all reach convergence within each iteration, confirming that our models are well-trained.
Table 11: The loss of model training on ALDHI.
MLP
GNN
Pre. GNN
Epoch
Iter 4
Iter 6
Iter 4
Iter
Iter 4
Iter 6
1
0.4328
0.4204
0.4548
0.555
0.4858
0,5183
2
0.1292
0.1828
0.1717
0.2749
0.2854
0.1897
5
0.0225
0.0181
0.0478
0.0871
0.0351
0.0181
10
0,0016
0.0097
0.0062
0.0158
0.0039
0.0036
20
0.0027
0.0023
0.0112
0.0092
0.0185
0.0256
50
0.0003
0.0009
0.0071
0.0029
0.0006
0.0221
16

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B.7 Comparison for Different Pretrained GNN
To further isolate the effect of the learning strategy, compared GLARE (with MolCLR) and PtAL (also with MoICLR) under same pretrained GINs(i.e. MoICLR), shown in the Table I2 GLARE consistently outperforms PtAL, showing that the performance gain is primarily due to GLARE's superior learning strategy rather than the encoder alone.
Table 12: The comparison for different pretrained GIN encoders.
Enamine50k Iter
EnamincHTS-0.1
EnamineHTS-op
Method
Model
Strategy
Iter 6
Iter 4
Iter 6
Iter
Iter
Greedy UCB
0.5000
0.6708
0,5512
0.7278
0.7574
0.8698
PtLL
MoICLR
0.4972
0.6796
0.5384
0.7276
0.7624
0.8844
GNN MoICLR
0.4926
0.7424
0.4385
0.7526
0.7024
0,9032
GLARE
Policy
0.7695
0.8652
0.8425
0.8814
0.9356
0,9527
Pre. GNN
0.7765
0.8869
0.8637
0.9181
0.9618
0.9732
B.8 Different Optimization Strategies and Virtual Screening Methods
We experimented with Direct Policy Optimization (DPO), another effective RL algorithm, for optimizing the policy network. As shown in the Table [3 GRPO consistently outperforms DPO in our setting. We attribute this to the utilization of group-wise advantage estimation in GRPO, which better captures the relative quality of selected molecules within a batch property that aligns well with the objectives of virtual screening). This provides direct motivation for our choice of GRPO.
Table 13: The result for different reinforcement learning optimization strategies.
ALDHI
PKM2
VDR
Method
Model
Strategy
Iter 10
Iter 16
Iter 10
Iter 16
Iter
Iter 16
Policy (DPO)
5.759
6.012
1.878
5.526
2.397
5.961
MLP
Policy (GRPO)
6.574
6.535
2.067
5.904
3.173
6.512
Policy (DPO)
4.892
6.743
1.927
6,491
3.618
6.635
GLARE
GNN
Policy (GRPO)
6.179
7.067
2.480
7.146
7.535
7.104
Policy (DPO)
6.387
6.834
4.166
7.394
7.293
7.590
Pre. GNN
Policy (GRPO)
7.274
7.205
4.547
7.768
7.932
7.992
Recently TTA has been applied in virtual screening, which typically leverages self-supervised auxiliary tasks during inference to dynamically update the model parameters, enabling the learning of feature representations tailored to individual test instances. This approach requires no prior knowledge of the test data distribution and instead adjusts model parameters dynamically on per-instance basis during inference. Experiments about the comparison with DrugTTA (5) baseline are conducted. The results are summarized is Table 14
Table 14: The comparison for TTA baseline.
Method
Strategy
AUCROC(%)
BEDROC(%)
EF0.5%
EF1% %
EF5%
DrugCLIP
57.17
6.23
8.56
5,51
2.27
GLARE(20)
MI DPO
65.08
29.75
37.85
21.34
6.41
GLARE(20)
70.36
38.03
65.53
37.63
9.61
DrugTTA
71.24
45.08
74.39
42.74
10.61
GLARE(18)
GRPO
75,63
41.55
80.8
44.05
10.32
GLARE(20)
GRPO
79.78
46.39
83.51
47.36
12.08

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C Additional Details
C.1 Overview of the Benchmarks
LIT-PCBA PCBA includes a total of 15 targets, comprising 7,844 active and 407,381 inactive compounds. Following [55], we selected the three LIT-PCBA datasets with the highest numbers of experimentally validated molecules, which correspond to targets of clinical and therapeutic interest: pyruvate kinase M2 (PKM2, agonism), aldehyde dehydrogenase (ALDHI, inhibition), and vitamin D receptor (VDR, antagonism). For each dataset, 100,000 molecules were randomly sampled while maintaining the ratio of active to inactive compounds, which were used to construct screening library. For details, refer to Table 15
Table 15: Summary of the three subsets from LIT-PCBA (PKM2, ALDHI and VDR).
Screening Library Size
Hits Size
Hits Ratio
Test Set Size
Hits Size
Hits Ratio
Dataset
ALDHI PKM2
100,000
4,986
5%
20,000
997
5%
100,000
223
0.2%
20,000
44
0.2%
VDR 100,000
239
0.2%
20,000
48
0.2%
Enamine Enamine is a commercially available database for large-scale screening. The Enamine Discovery Diversity Set (Enamine50k) and Enamine HTS collection (EnamineHITS) consist of 50,240 compounds and 2, 2,141,514 molecules, respectively. The Enamine datasets used for these studies were generated from docking the compounds against thymidylate kinase (PDB ID; 4UNN). Both Enamine50k and EnamineHTS are publicly accessible via the MolPAL code repository [14], undergo molecular docking against thymidylate kinase (PDB ID: 4UNN 33
using AutoDock [33
C.2 Baseline Methods
Baselines We consider two challenging baselines for a thorough evaluation, which contain a range of surrogate models and acquisition functions as traditional active learning methods. Van Tilborg and Grisoni (55) proposes an active learning virtual screening baseline containing two model architectures and some acquisition functions (for convenience, experiments on large-scale datasets and proposed a large-scale active learning virtual screening baseline incorporating pretrained models (for convenience, we name it as PtAL).
name it as TcsAL). Cao et al. [5] conducted
Pretrained Models The pretrained models used in PtAL include MoLFormer 45 and Mol- CLR (25). MoLFormer is a transformer-base model that takes tokenized SMILES strings input and learns molecular representations by capturing intrinsic spatial relationships between atoms. To improve computational efficiency, it employs linear attention and rotary position embedding instead of standard quadratic attention. MoL.Former was pretrained on an ultralarge dataset of 1.1 billion small molecules from ZINC and PubChem using the mask-language modeling technique. MolCLR graph isomorphism network pretrained on 10 million molecules from PubChem (25 using contrastive learning strategy. Other comparative models include D-MPNN [77, GNN variant, as well as RF and LightGBM 24), which are decision tree-based ensemble methods.
language-modeling
Acquisition Functions Acquisition functions used for comparison comprise greedy, mutual infor- mation (MI) [18), upper confidence bound (UCB), uncertainty, similarity, and random.
To estimate the expected value of a molecule m,, the mean prediction across all surrogate models in the ensemble is considered when performing traditional active barning-based virtual screening:
E(y.
Σ pk (u:lm.) k=
(15)
K
Similarly, the prediction uncertainty for a molecule m; is defined as the mean entropy H over the ensemble:
K
H (y.
Pk (ys/m, log (pk (y: (m:))
(16)
K
18

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Following these definition, the acquisition functions are defined as follows.
Similarity: samples selected the basis of their highest Tanimoto coefficient (computed with ECFPs; with 1,024 bits and a radius of 2) to any previously acquired hit compound.
Greedy: the best predicted samples selected with
M arg max, (lE (y|m))
(17)
Uncertainty: most uncertain samples are selected with
+ arg max m)).
(18)
Mutual Information (Ml) 18 selects samples with low mutual information with
arg max, (H) - Eι [H (y/m,, 0)))
(19)
Upper Confidence Bound (UCB): selects samples with the highest upper confidence hound
V arg max, (E (y/m) 3 V(ylm)),
(20)
where V denotes the standard deviation between the models.
C.3 Evaluation Metrics
BEDROC Boltzmann-enhance discrimination of receiver operating characteristic (BEDROC)54) is designed to assess early recognition performance, giving higher weights to active compounds that are ranked closer to the top. The formal definition is:
Rs sinh(aa22)
BEDROC,
+ ca(1-Ra)
(21)
R. (
cash(ar/) cosh (a/2 aRa)
(2//8_I)
The commonly used variant BEDROC&s, where the top 2% of ranked candidates contribute to 80% of the BEDROC score.
Enrichment Factor (EF.) For evaluating early retrieval, we use EFa defined as:
Ma |M| a
EF
(22)
where Ma is the true active molecules in top a%, M is the whole library.
C.4 Implementation Details
Hyper-parameters We used the Adam optimizer with a learning rate of 3e-4. The training batch size was set to 64 and the inference batch size was set to 512 for quicker inference. The embedding dim molecules 130, and the hidden dim is 1024. The MLP and GIN employed in the molecular encoder have layers. An MLP with 3 layers is also used in the action scoring layer. During the training step active learning, the number of training epochs is set to 50. The c and B in GRPO are set to 7 and 1c-2, respectively.
Hardware The experiments were performed on computational cluster equipped with dual Intel Xeon Gold 6248R CPUs (3,00 GHz, 48 cores) and an NVIDIA RTX 4090 GPU with 24 GB of memory, running Ubuntu 22.04.2 LTS as the operating system.
19

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26


Reviews:
[{"review_id": "9iUCPkhcxu", "reviewer": "Reviewer_4qh4", "summary": "This paper introduces the GLARE framework (a GRPO-based Learning framework for Active REinforced screening) for virtual screening in drug discovery. It casts virtual screening as a Markov decision process and integrates it into a policy-based GRPO reinforcement learning framework. GLARE achieves competitive performance across multiple benchmark datasets.", "questions": "- What is the computational time required to train these models?\n- Are four and six iterations sufficient for benchmarking the enrichment factor in Table 2? Can the authors show that training has converged?\n- Could the authors elaborate on the specific strategies applied in the experiments summarized in Table 2?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths: \n- Models virtual screening as a Markov decision process and leverages GRPO for policy optimization.\n- Attains higher enrichment factors and retrieval rates compared to classical active‐learning strategies and state‐of‐the‐art pretrained backbones.\n\nWeaknesses:\n- Represents an incremental modification of existing reinforcement learning frameworks.\n- Does not report training or inference runtime.", "was_revised": true, "final_justification": "This work applies GRPO reinforcement learning to virtual screening. The authors address my concern regarding training convergence and report the computational costs. One minor point is the incremental use of an existing GRPO method, as noted by another reviewer as well. Therefore, we maintain our current positive score.", "rebuttal": "We appreciate the reviewer's careful review of our paper, positive feedback, and recognition of our work, particularly the effectiveness of our proposed method. Below, we address the concerns point by point:\n> W1: The Incremental Modification of Existing Reinforcement Learning Frameworks\n\nR: Thank you for your valuable comments. We would like to clarify that our work is not a simple incremental modification of existing RL frameworks. Our study specifically addresses active learning in virtual screening, which poses unique challenges compared to standard RL tasks. \n\nTo our knowledge, **we are the first to integrate RL (GRPO) with active learning and apply it to virtual screening**. We innovatively model the virtual screening scenario as a Markov Decision Process (MDP) and combine it with active learning principles. This enables our method, GLARE, *to learn how to select the most informative molecules adaptively*. By coupling the RL agent's selection strategy with docking feedback in an active learning loop, our method, GLARE, efficiently prioritizes the most informative docking computations, offering a efficient and practical solution for real-world virtual screening workflows.\n\nIn addition, based on GRPO, we have substantially redesigned the framework. Specifically, we introduced a new reward function based on gradient uncertainty (Exploration Modification), specifically crafted to balance exploration and exploitation in this context, an aspect not addressed by existing methods.\nWe hope this clarifies the novelty and significance of our contribution.\n\n> W2 & Q1: The Runtime of Training or Inference\n\nR: Thank you for raising this important question. To provide a comprehensive comparison, we evaluated both training and inference runtimes on the ALDH1 dataset, using the same hyperparameters and hardware as TcsAL (see Appendix C.4). \n\nActive learning typically involves training on a small number of labeled samples while making predictions on a much larger pool of unlabeled data. As a result, inference time becomes the dominant computational cost, especially in large-scale virtual screening scenarios. As shown in the table below, while our method requires additional training time due to the computational cost of gradient-based uncertainty in reward calculation, this overhead is confined to the training phase. During inference, reward computation is not required, enabling our method to achieve inference speeds comparable to TcsAL.\n\n| Method | Model | Train Time (Sec. per Epoch) | Infer. Time (Sec.) |\n|:---:|:---:|:---:|:---:|\n| TcsAL | MLP | 1.5 | 18.2 |\n|   | GNN | 4.2 | 148.6 |\n| GLARE | MLP | 4.7 | 14.5 |\n|   | GNN | 8.9 | 152.6 |\n\nThe characteristic is particularly important for large-scale dataset. To further demonstrate the scalability of our approach, we tested it on the AmpC dataset (~$10^8$ compounds) using the same hyperparameters as PtAL. As dataset size increases, inference time becomes even more significant. Our method maintains inference speed similar to baseline methods while delivering much higher accuracy, making it especially well-suited for ultra-large virtual screening tasks.\n\n| Method | Model | EnamineHTS-0.1 (2m) |   | AmpC (99.5m) |   |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   | Train Time (Sec.) | Infer. Time (Sec.) | Train Time (Sec.) | Infer. Time (Sec.) |\n| PtAL | GNN | 71.4 | 825.5 | 4,112.6 | 40,423.3 |\n|   | Pre. GNN | 129.1 | 922.3 | 6,749.7 | 45,374.8 |\n| GLARE | GNN | 84.2 | 913.3 | 4,868.2 | 49,662.4 |\n|   | Pre. GNN | 155.9 | 1,179.1 | 8,237.6 | 55,127.2 |\n\n> Q2. The Sufficiency of Iterations and Training Convergence in Table 2\n\nR: Thank you for pointing out this question. \n\nWe closely follow the setting of the baseline PtAL, which adopts four and six active learning iterations with a fixed total annotation budget. Specifically, the *total budget = (number of iterations) × (budget per iteration)*, and this total budget is kept the same across all methods for fair comparison. This practice is standard in the active learning literature and is widely considered sufficient for benchmarking enrichment factors. In our experiments, we observed that the model performance stabilizes after several iterations, and additional rounds yield diminishing returns. Thus, using four and six iterations is both representative and adequate for this benchmark.\n\nRegarding training convergence, we ensured sufficient training within each active learning round. Following the TcsAL baseline, we trained each model for 50 epochs per iteration and monitored the training loss. As shown in the table below, the loss curves for MLP, GNN, and Pre. GNN all reach convergence within each iteration, confirming that our models are well-trained.\n\n| Epoch | MLP |  | GNN |  | Pre. GNN |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   | Iter 4 | Iter 6 | Iter 4 | Iter 6 | Iter 4 | Iter 6 |\n| 1 | 0.4328  | 0.4204  | 0.4548 | 0.555 | 0.4858 | 0.5183 |\n| 2 | 0.1292  | 0.1828  | 0.1717 | 0.2749 | 0.2854 | 0.1897 |\n| 5 | 0.0225  | 0.0181  | 0.0478 | 0.0871 | 0.0351 | 0.0181 |\n| 10 | 0.0016  | 0.0097  | 0.0062 | 0.0158 | 0.0039 | 0.0036 |\n| 20 | 0.0027  | 0.0023  | 0.0112 | 0.0092 | 0.0185 | 0.0256 |\n| 50 | 0.0003  | 0.0009  | 0.0071 | 0.0029 | 0.0006 | 0.0221 |\n\n> Q3: Active Learning Strategies in Table 2\n\nR: Thank you for your question. \n\nIn the experiments summarized in Table 2, we adopted several active learning acquisition strategies, as detailed in Appendix C.2. Specifically, we used Greedy and Upper Confidence Bound (UCB) methods. The corresponding acquisition functions are as follows:\n\n* **Greedy**: Selects samples with the highest predicted value: $$\\psi=\\arg\\mathrm{max}_{n}\\left(\\mathbb{E}\\left(y|m\\right)\\right)$$\n\n* **Uncertainty**: Selects the most uncertain samples:\n$$\\psi=\\arg\\mathrm{max}_{n}(\\mathbb{H}(y|m))$$\n\n* **Mutual Information (MI)**: Selects samples with the highest mutual information:\n$$\\psi=\\arg\\mathrm{max}_{n}\\left(\\mathbb{H}(y|m)-\\mathbb{E}_M\\left[\\mathbb{H}\\left(y|m,\\theta\\right)\\right]\\right)$$\n\n* **Upper Confidence Bound (UCB)**: Selects samples with the highest upper confidence bound:\n$$\\psi=\\arg\\mathrm{max}_{n}\\left(\\mathbb{E}\\left(y|m\\right)+\\beta\\cdot\\mathbb{V}(y|m)\\right)$$\n\nThese strategies are standard in active learning and allow for a fair and comprehensive benchmark as presented in Table 2."}, {"review_id": "aS9HoqLEvq", "reviewer": "Reviewer_7Vgx", "summary": "This work proposes GLARE, a reinforcement learning-based active learning (AL) framework designed for large-scale virtual screening under limited annotation budgets. GLARE reformulates molecular selection as a Markov Decision Process (MDP) and replaces traditional hand-crafted acquisition strategies with a learnable policy network trained via Group Relative Policy Optimization (GRPO). The method dynamically balances structural diversity, biological relevance, and computational cost during molecule selection. The framework is compatible with various molecular encoders, including MLPs, GNNs, and pretrained models. Experiments on the LIT-PCBA and Enamine benchmarks shown substantial improvements in enrichment factor (EF) and retrieving rate (RR) over competitive baselines, particularly in low-data regimes. GLARE also shows strong synergy with foundation models like DrugCLIP, achieving multi-fold gains with minimal additional annotations. Overall, the authors contribute an adaptive approach for efficient hit identification in early-stage drug discovery.", "questions": "1. Can the authors provide a quantitative analysis of GLARE’s computational cost and scalability?\n2. How sensitive is GLARE’s policy performance to the reward formulation and exploration heuristics?", "limitations": "No\nThe paper presents a well-structured and comprehensive framework, but its discussion of limitations could be made more explicit. The authors briefly mention scalability issues (Section 5, Discussion), but they do not provide any quantitative analysis of the computational cost of policy training and inference, especially in ultra large chemical libraries. Next, the reward function includes heuristic components such as uncertainty-weighted discount factors, yet the potential sensitivity of these heuristics across diverse targets is not explored. On top of that, the work does not consider the biological interpretability and off-target risks of the retrieved compounds, which could limit the application of the framework in drug discovery practical scenarios. At this point, a more transparent discussion of these aspects would strengthen the paper’s reliability and completeness.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strength:\n1. The use of a learnable policy model optimized through GRPO distinguishes this work from previous solutions that rely on static and handcrafted heuristics. The methodology is solid as the paper provides a thorough breakdown of the components, including the molecular encoder variants, reward function design, and policy optimization.\n2. The experimental section is extensive, with evaluations on both standard (LIT-PCBA) and large-scale (Enamine) benchmarks. The framework performs well even in extremely low-data regimes, and it is also able to improve the enrichment factor of foundation models like DrugCLIP with limited additional labels. These promising results (Section 4, Experiments) show a great potential of GLARE in early-stage drug-discovery practice.\n3. The inclusion of ablation studies, UMAP visualizations, and comparisons across multiple molecular encoders add clarity and depth to the evaluation.\n\nWeakness:\n1. Although the GRPO-based optimization is well-motivated, the computational cost associated with reinforcement learning in high-dimensional chemical spaces is not clearly quantified (Section 5, Discussion), particularly as the size of the screening library increases to 10⁸–10⁹ compounds.\n2. The reward function in equation 3 and equation 10 uses uᵢ, a manually constructed discount factor for exploration. This heuristic rule proves effective on specific datasets, but it may not generalize well to other tasks (e.g., different biological targets or hit rate distributions), as it is fundamentally driven by empirical design rather than data.\n3. The contributions of the pretrained encoder versus the learned policy in GLARE are not clearly distinguished. In Table 1 and Table 2, although the combination of GLARE with pretrained GNN encoders performs best, the experiments do not clarify whether the observed performance gains arise primarily from the pretrained molecular representations or from the optimization of the policy network itself.", "was_revised": true, "final_justification": "The author has addressed my concerns. Although this work introduces one new way of improving the activing learning virtual screening. I view it as an incremental work rather than a transformative for the field. Since the score to 6 requires groundbreaking impact as stated in the rating selection, and therefore I maintain my original score of 5.", "rebuttal": "We sincerely thank the reviewer for their positive comments, especially recognizing the novelty of our work and the extensiveness of our experimental section. We also appreciate the constructive suggestions, which have helped us to further improve the quality of our manuscript. Below we provide detailed point-by-point responses to each of the reviewer's concerns.\n\n> W1 & Q1: Computational Cost in Large Chemical Space\n\nR: We appreciate your valuable comment on the computational scalability of GRPO-based reinforcement learning, particularly as the screening library size increases to $10^8$–$10^9$ compounds.\n\nTo address this concern, we conducted large-scale experiments on the AmpC dataset (~$10^8$ compounds) using the same hyperparameters as PtAL. As shown in the table below, our method (GLARE) achieves inference speeds comparable to the baseline PtAL across both medium (EnamineHTS-0.1, 2 million compounds) and ultra-large (AmpC, 99.5 million compounds) datasets. Notably, while our method introduces additional computational overhead during training due to gradient-based reward optimization, this overhead does not affect inference. During inference, reward computation is not required, and the runtime is dominated by model forward passes, as in the baselines.\n\n| Method | Model | EnamineHTS-0.1 (2m) |   | AmpC (99.5m) |   |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   | Train Time (Sec.) | Infer Time (Sec.) | Train Time (Sec.) | Infer Time (Sec.) |\n| PtAL | GNN | 71.4 | 825.5 | 4,112.6 | 40,423.3 |\n|   | Pre. GNN | 129.1 | 922.3 | 6,749.7 | 45,374.8 |\n| GLARE | GNN | 84.2 | 913.3 | 4,868.2 | 49,662.4 |\n|   | Pre. GNN | 155.9 | 1,179.1 | 8,237.6 | 55,127.2 |\n\nWe also evaluated both training and inference runtimes on the ALDH1 dataset (PKM2 and VDR have similar scales), using the same hyperparameters and hardware as TcsAL.\n\n| Method | Model | Train Time (Sec. per Epoch) | Infer Time (Sec.) |\n|:---:|:---:|:---:|:---:|\n| TcsAL | MLP | 1.5 | 18.2 |\n|   | GNN | 4.2 | 148.6 |\n| GLARE | MLP | 4.7 | 14.5 |\n|   | GNN | 8.9 | 152.6 |\n\n> W2: The Design of the Exploration Discount Factor\n\nR: Thank you for this insightful comment.\n\nWe would like to clarify that the exploration discount factor $u_i$ in our reward function **is not solely based on heuristic or empirical design, but is fundamentally data-driven**. The motivation for introducing $u_i$ is to ensure that even if a molecule is predicted as inactive, it can still be selected if its uncertainty is high (i.e., it may be informative for model training). Thus, we penalize the reward by a factor that captures uncertainty, rather than using a fixed or manually tuned value.\n\nSpecifically, inspired by [1], we incorporate Exploration Modification in the reward, where uncertainty is quantified by the L2 norm of the model's gradient. This approach makes the discount factor adaptive to the actual characteristics of each dataset, instead of being a static or hand-crafted parameter. As a result, our method can flexibly adapt to different tasks, targets, and hit rate distributions, thereby enhancing both its generalizability and effectiveness.\n\nWe will revise this part in the manuscript to improve its clarity. Thank you again for your valuable feedback.\n\n[1] Jordan T. Ash et al., \"Deep batch active learning by diverse, uncertain gradient lower bounds.\" arXiv preprint arXiv:1906.03671, 2019.\n\n> W3: The Contributions of the Pretrained Encoder and Learned Policy\n\nR: Thank you for highlighting this important point.\n\nTo clarify the respective contributions of the pretrained encoder and the learned policy in GLARE, we provide a condensed analysis of the results from Table 1 and Table 2.\n\nAs shown in the table below, GLARE (with GNN) significantly outperforms TcsAL (with GNN), regardless of the strategy used. Both employ the same non-pretrained GIN architecture. This demonstrates that our Learnable Policy strategy in GLARE provides a substantial advantage over prior approaches. GLARE (with Pre. GNN) further outperforms GLARE (with GNN), indicating that pretraining effectively addresses the cold-start problem and enhances early-phase retrieval performance.\n\n| Method | Model | Strategy | ALDH1 |  | PKM2 |  | VDR |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|  |  |  | Iter 10 | Iter 16 | Iter 10 | Iter 16 | Iter 10 | Iter 16 |\n| TcsAL | GNN | Random | 1.020  | 0.988  | 0.868  | 1.036  | 1.071  | 0.994  |\n|  |  | Similarity | 2.438  | 2.362  | 1.943  | 2.113  | 2.419  | 2.664  |\n|  |  | Uncertainty | 0.907  | 0.923  | 0.868  | 0.777  | 0.754  | 0.681  |\n|  |  | Greedy | 3.126  | 3.750  | 1.901  | 2.548  | 3.014  | 2.871  |\n|  |  | MI | 3.482  | 4.077  | 1.777  | 2.952  | 4.085  | 4.085  |\n| GLARE | GNN | Policy | 6.179  | 7.067  | 2.480  | 7.146  | 7.535  | 7.104  |\n|  | Pre. GNN (GraphMVP) | Policy | **7.274**  | **7.205**  | **4.547**  | **7.768**  | **7.932**  | **7.992** |\n\nFrom the table below, GLARE (with Pre. GNN) also substantially surpasses PtAL (with MolCLR), even though both use pretrained GINs (with different pretraining protocols). GLARE (with GNN, no pretraining) even outperforms PtAL (with MolCLR) at the final phase (Iter 6), confirming the strength of our learned policy.\n\nTo further isolate the effect of the learning strategy, we compared GLARE (with MolCLR) and PtAL (with MolCLR) under same pretrained GINs, also shown in the table below. GLARE consistently outperforms PtAL, showing that the performance gain is primarily due to GLARE's superior learning strategy rather than the encoder alone.\n\n| Method | Model | Strategy | Enamine50k |  | EnamineHTS-0.1 |  | EnamineHTS-0.2 |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   |   | Iter 4 | Iter 6 | Iter 4 | Iter 6 | Iter 4 | Iter 6 |\n| PtAL | MolCLR | Greedy | 0.5000  | 0.6708  | 0.5512  | 0.7278  | 0.7574  | 0.8698  |\n|   | MolCLR | UCB | 0.4972  | 0.6796  | 0.5384  | 0.7276  | 0.7624  | 0.8844  |\n| GLARE | GNN | Policy | 0.4926  | 0.7424  | 0.4385  | 0.7526  | 0.7024  | 0.9032  |\n|   | MolCLR | Policy | 0.7695  | 0.8652  | 0.8425  | 0.8814  | 0.9356  | 0.9527  |\n|   | Pre. GNN (GraphMVP) | Policy | **0.7765**  | **0.8869**  | **0.8637**  | **0.9181**  | **0.9618**  | **0.9732**  |\n\nThese comparisons clearly demonstrate that GLARE's superior performance arises mainly from its learned policy, while the use of a pretrained encoder provides an additional warm-up effect, further enhancing screening efficiency.\n\n> Q2: Sensitivity Analysis of GLARE's Policy Performance\n\nR: To evaluate the sensitivity of GLARE's policy to the reward formulation and exploration heuristics, we conducted a series of ablation studies on the ALDH1, PKM2, and VDR datasets using GLARE (with GNN). The results are summarized in the table below.\n\n| Method | Strategy | ALDH1 | PKM2 | VDR |\n|:---:|:---:|:---:|:---:|:---:|\n| TcsAL | Greedy | 3.750  | 2.548  | 2.871  |\n|   | MI | 4.077  | 2.952  | 4.085  |\n| GLARE | $\\nu_i=0$, i.e., $u_i=1$ | 6.539  | 6.657  | 6.531  |\n|   | $\\nu_i=0.5$ | 6.842  | 6.863  | 6.732  |\n|   | $r_i=2-*$ | 6.873  | 6.902  | 6.965  |\n|   | Policy | **7.067**  | **7.146**  | **7.104**  |\n\n* Removing exploration modification entirely ($\\nu_i=0$, i.e., $u_i=1$) results in the lowest performance among GLARE variants, as the model loses the ability to identify uncertain but informative molecules. \n\n* Using a constant exploration factor ($\\nu_i=0.5$) yields better results than no modification, but is still less effective than dynamically adjusting $u_i$ based on gradient uncertainty. \n\n* When the reward in GLARE is modified as $r_i=2-(y_i-a_{i,y_i})^2 \\cdot u_i$, performance drops slightly compared to the original GLARE, but still remains well above all baselines, indicating that GLARE is not highly sensitive to the specific reward formulation. \n\nOverall, these results highlight the robustness of GLARE and the added value of our adaptive exploration strategy. Importantly, all GLARE variants consistently outperform the existing baselines, regardless of how the reward or exploration modification is set. This demonstrates the robustness of the GLARE framework. \n\n> L1: Discussion\n\nR: We appreciate the reviewer's valuable feedback and acknowledge several limitations of our current framework. We will incorporate these discussions and additional analyses in the revised manuscript. \n\nWhile GLARE demonstrates strong performance and reasonable computational efficiency in our experiments, scaling to ultra-large chemical libraries (e.g., $10^{9}$ compounds) remains challenging. To address this, we have included additional experiments on a $10^8$-scale compound database, which demonstrate the method's scalability in large-scale settings.\n\nAlthough our sensitivity analysis shows that GLARE is robust to changes in reward formulation and exploration heuristics, there may still be cases where performance is affected by unusual or out-of-distribution targets.\n\nFinally, our current framework does not explicitly consider biological interpretability, which is important for real-world drug discovery applications. We recognize these as important directions for future research to further enhance the reliability and practical impact of our approach."}, {"review_id": "whHhHUjQgY", "reviewer": "Reviewer_jbx2", "summary": "This paper presents GLARE, a novel active learning framework for virtual screening. The authors reformulate the selection of molecules as a Markov Decision Process (MDP) and apply Group Relative Policy Optimization (GRPO) to train a learnable policy model for molecule selection. The proposed framework achieves state-of-the-art performance over baselines along with better exploration and exploitation.", "questions": "1. Could GLARE be extended to support multi-objective optimization? for example, if users want to identify active molecules with optimized druglikeness, solubility, etc. Could the reward function or policy network be extended accordingly?", "limitations": "Yes", "rating": 4, "confidence": 2, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Stength:**\n1. This paper studies an important scientific problem, i.e., virtual screening, with potential real-world impact\n2. Reformulating virtual screening as an MDP is innovative\n3. The writing is overall clear and easy to follow.\n\n\n**Weakness:**\n1. The RL part relies heavily on Group Relative Policy Optimization, which limits the novelty of the paper\n2. The paper does not report walltime or computational cost relative to TcsAL or PtAL. \n3. The choice of pretrained GNN is not clear. If I understand correctly GNN corresponds to GIN, how about the pretrained GNN? And similarly, for table 2 could author add another experiment so that PtAL and GLARE are using same pretrained GNNs for a more fair comparison?", "was_revised": true, "final_justification": "The authors' responses have addressed most of my concerns and problems, and I am therefore willing to raise my score to 4.", "rebuttal": "We sincerely thank the reviewer for the detailed feedback and constructive suggestions to improve our manuscript. Below, we summarize the reviewer's questions and provide point-by-point responses.\n\n\n> W1: Clarification on Novelty and GRPO\n\nR: Thank you for your valuable comments.\n\nWe respectfully disagree with the view that our work is merely a straightforward application of Group Relative Policy Optimization (GRPO). In fact, our study goes significantly beyond simply adopting GRPO. We specifically address the unique and underexplored challenges of active learning in virtual screening, challenges that are fundamentally different from those in standard RL domains. These include extreme class imbalance, label scarcity, and the need for efficient prioritization of candidates from ultra-large chemical spaces.\n\nTo our knowledge, **this is the first work to integrate GRPO within an active learning framework and apply it to large-scale virtual screening**. We not only reformulate the virtual screening process as a Markov Decision Process (MDP) but also introduce active learning-driven reward structures, enabling GLARE to **adaptively and efficiently select the most informative molecules**. By coupling the RL agent's selection strategy with docking feedback in an active learning loop, our method, GLARE, efficiently prioritizes the most informative docking computations, offering a efficient and practical solution for real-world virtual screening workflows.\n\nIn addition, based on GRPO, we have substantially redesigned the framework. Specifically, we introduced **a new reward formulation that incorporates gradient-based uncertainty, specifically tailored to the active learning context**, an aspect not addressed in prior GRPO works.\n\nWe also compared GRPO with Direct Policy Optimization (DPO), another effective RL algorithm, for optimizing the policy network. As shown in the table below, GRPO consistently outperforms DPO in our setting. We attribute this to GRPO's use of group-wise advantage estimation, which better captures the relative quality of selected molecules within a batch (a property that aligns well with the objectives of virtual screening). This provides a direct motivation for our choice of GRPO.\n\n| Method | Model | Strategy | ALDH1 |  | PKM2 |  | VDR |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   |   | Iter 10 | Iter 16 | Iter 10 | Iter 16 | Iter 10 | Iter 16 |\n| GLARE | MLP | Policy (DPO) | 5.759 | 6.012 | 1.878 | 5.526 | 2.397 | 5.961 |\n|   |   | Policy (GRPO) | 6.574  | 6.535  | 2.067  | 5.904  | 3.173  | 6.512  |\n|   | GNN | Policy (DPO) | 4.892 | 6.743 | 1.927 | 6.491 | 3.618 | 6.635 |\n|   |   | Policy (GRPO) | 6.179  | 7.067  | 2.480  | 7.146  | 7.535  | 7.104  |\n|   | Pre. GNN | Policy (DPO) | 6.387 | 6.834 | 4.166 | 7.394 | 7.293 | 7.590  |\n|   |   | Policy (GRPO) | 7.274  | 7.205  | 4.547  | 7.768  | 7.932  | 7.992  |\n\nWe hope this clarifies the novelty and significance of our contribution.\n\n\n> W2: The Computational Cost\n\nR: Thank you for raising this important question. To provide a comprehensive comparison, we evaluated both training and inference runtimes on the ALDH1 dataset, using the same hyperparameters and hardware as TcsAL (see Appendix C.4). \n\nActive learning typically involves training on a small number of labeled samples while making predictions on a much larger pool of unlabeled data. As a result, **inference time becomes the dominant computational cost, especially in large-scale virtual screening scenarios**. As shown in the table below, while our method requires additional training time due to the computational cost of gradient-based uncertainty in reward calculation, this overhead is confined to the training phase. During inference, reward computation is not required, enabling **our method to achieve inference speeds comparable to TcsAL**.\n\n| Method | Model | Train Time (Sec. per Epoch) | Infer. Time (Sec.) |\n|:---:|:---:|:---:|:---:|\n| TcsAL | MLP | 1.5 | 18.2 |\n|   | GNN | 4.2 | 148.6 |\n| GLARE | MLP | 4.7 | 14.5 |\n|   | GNN | 8.9 | 152.6 |\n\nThe characteristic is particularly important for large-scale dataset. To further demonstrate the scalability of our approach, we tested it on the AmpC dataset (~$10^8$ compounds) using the same hyperparameters as PtAL. **As dataset size increases, inference time becomes even more significant. Our method maintains inference speed similar to baseline methods while delivering much higher accuracy**, making it especially well-suited for ultra-large virtual screening tasks.\n\n| Method | Model | EnamineHTS-0.1 (2m) |   | AmpC (99.5m) |   |\n|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   | Train Time (Sec.) | Infer. Time (Sec.) | Train Time (Sec.) | Infer. Time (Sec.) |\n| PtAL | GNN | 71.4 | 825.5 | 4,112.6 | 40,423.3 |\n|   | Pre. GNN | 129.1 | 922.3 | 6,749.7 | 45,374.8 |\n| GLARE | GNN | 84.2 | 913.3 | 4,868.2 | 49,662.4 |\n|   | Pre. GNN | 155.9 | 1,179.1 | 8,237.6 | 55,127.2 |\n\n> W3: The choice of Pretrained GNN.\n\nR: Thank you for highlighting this important point. \n\nFor GLARE (with GNN), we use a Graph Isomorphism Network (GIN) as the backbone. For GLARE (with Pre. GNN), we also employ a GIN architecture, but it is initialized with weights from the pretrained GraphMVP or MolCLR model.\n\nFrom the table below, GLARE (with Pre. GNN) substantially outperforms PtAL (with MolCLR), even though both use pretrained GINs (with different pretraining protocols). Notably, **GLARE (with GNN, no pretraining) also surpasses PtAL (with MolCLR) in the final phase (Iter 6), confirming the strength of our learned policy**.\n\nTo further isolate the effect of the learning strategy, we additionally compared GLARE (with MolCLR) and PtAL (with MolCLR) using the same pretrained GINs. As shown in the the table below, GLARE consistently outperforms PtAL under the same backbone, demonstrating that the performance gain is primarily attributable to GLARE's superior learning strategy, rather than the encoder itself.\n\n| Method | Model | Strategy | Enamine50k |  | EnamineHTS-0.1 |  | EnamineHTS-0.2 |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   |   | Iter 4 | Iter 6 | Iter 4 | Iter 6 | Iter 4 | Iter 6 |\n| PtAL | MolCLR | Greedy | 0.5000  | 0.6708  | 0.5512  | 0.7278  | 0.7574  | 0.8698  |\n|   | MolCLR | UCB | 0.4972  | 0.6796  | 0.5384  | 0.7276  | 0.7624  | 0.8844  |\n| GLARE | GNN | Policy | 0.4926  | 0.7424  | 0.4385  | 0.7526  | 0.7024  | 0.9032  |\n|   | MolCLR | Policy | 0.7695  | 0.8652  | 0.8425  | 0.8814  | 0.9356  | 0.9527  |\n|   | Pre. GNN (GraphMVP) | Policy | **0.7765**  | **0.8869**  | **0.8637**  | **0.9181**  | **0.9618**  | **0.9732**  |\n\n> Q1: Extension to Multi-Objective Optimization.\n\nR: Thank you for this excellent question. We appreciate the suggestion and will discuss this direction in the Discussion section as future work.\n\nGLARE can indeed be extended to support multi-objective optimization, which is highly relevant for practical drug discovery.\n\n**Reward Extension:**\nA straightforward way is to incorporate additional objectives as regularization terms in the reward function. For example, the reward can be reformulated as\n$$r_i = (1-(y_i-a_{i,y_i})^2 \\cdot u_i) \\cdot c_i,$$\nwhere $c_i$ represents a constraint or score for another property, such as druglikeness or similarity to a reference compound. By tuning $c_i$, the model can learn to favor molecules optimizing multiple objectives.\n\n**Policy Extension:**\nAlternatively, the framework can support multiple policy networks, each focusing on a different objective (such as activity, druglikeness, solubility, etc.). Each policy provides a confidence score for its respective objective, and decisions can be made by aggregating these scores. This can be formulated as a multi-agent optimization framework: \n$$M = \\lbrace \\pi^{c_k}(a_i^{c_k}|s_i) | k \\in 1, \\cdots,n \\rbrace,$$\n$$a_i = a_i^{c_1} \\otimes \\cdots \\otimes a_i^{c_n},$$\nwhere actions from different agents (objectives) are combined to determine the final selection.\n\nThanks to our flexible reward design and reinforcement learning-based policy, GLARE naturally accommodates such extensions. This makes it well-suited for practical drug discovery scenarios that require simultaneous optimization of multiple molecular properties."}, {"review_id": "LpOckfEVzR", "reviewer": "Reviewer_XNVU", "summary": "This paper proposes GLARE, a novel active learning framework for drug virtual screening. Unlike traditional methods that use hand-crafted selection heuristics, GLARE learns a reinforcement learning-based policy to directly select molecules for labeling. By formulating the task as a Markov Decision Process and optimizing a policy using Group Relative Policy Optimization (GRPO), GLARE dynamically balances exploration (diversity) and exploitation (biological relevance). Extensive experiments show that GLARE significantly outperforms existing active learning methods, achieving up to 64.8% higher enrichment factors and enhancing the performance of foundation models like DrugCLIP by up to 8-fold with minimal labeled data.", "questions": "see weakness", "limitations": "n/a", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n1. The overall framework design is well-aligned with the virtual screening task. Reformulating the process as a Markov Decision Process and learning a molecule selection policy is a reasonable and principled approach.\n2. The proposed method shows consistent improvement over baseline models across several datasets and evaluation metrics. \n3. The paper is clearly written and easy to follow. The figures, including framework diagrams and UMAP visualizations, are clear, professional。\n\n## Weakness\n1. While the approach is effective, the overall idea is relatively straightforward. It applies existing reinforcement learning techniques (specifically GRPO) within an active learning setting. The novelty mainly lies in combining known components. It would be beneficial to evaluate other reinforcement learning algorithms beyond GRPO, or provide stronger justification for its selection.\n2. The policy model only considers molecular features when making selection decisions. In structure-based virtual screening, incorporating protein or pocket-level features might improve performance.\n3. The method appears to require training a separate policy model for each target, which limits scalability in applications involving large sets of proteins or binding pockets. It does not support efficient many-to-many matching.\n4. The baselines in the experiments are somewhat limited. Only TcsAL is used for the LIT-PCBA benchmark, and only PtAL for the Enamine datasets. It would strengthen the empirical evaluation to include a wider range of baseline methods.", "was_revised": true, "final_justification": "This paper uses the RL objective to replace conventional acquisition functions in an active learning based virtual screening setting. I acknowledge that applying an RL objective in an active learning setting is an interesting exploration, and its success is not surprising. However, I still have some reservations about the contributions, as I feel the method is highly dependent on the specific setting. In particular, if RL for active learning is applied only to virtual screening, the chosen baselines (simple acquisition functions) are relatively weak, and it is expected that RL would outperform them. This makes the contribution appear somewhat setting-dependent. That said, from another perspective, introducing a method to a new task where it has not been applied before does offer a degree of novelty. Overall, after careful consideration and reading other reviewers’ comments, I still think the weaknesses outweigh the strengths and will maintain my rating at borderline reject.", "rebuttal": "We sincerely thank the reviewer for the detailed feedback and constructive suggestions. Below, we summarize the reviewer's questions and provide point-by-point responses.\n\n> W1: The Contribution of the Proposed Methodology\n\nR: Thank you for this insightful question. We would like to clarify that, while our approach leverages existing reinforcement learning (RL) techniques, **our contribution goes well beyond simply combining known components**. Our study specifically **addresses the unique challenges of active learning in virtual screening**, an area that differs substantially from standard RL tasks. \n\nTo our knowledge, this is the first work to integrate GRPO within an active learning framework and apply it to the context of virtual screening. We innovatively **formulate the virtual screening process as a Markov Decision Process (MDP) and embed active learning principles**, allowing GLARE to adaptively select the most informative molecules for labeling. By coupling the RL agent's selection strategy with docking feedback in an active learning loop, our method, GLARE, efficiently prioritizes the most informative docking computations, offering a efficient and practical solution for real-world virtual screening workflows.\n\nIn addition, based on GRPO, we have substantially redesigned the framework. Specifically, we introduced **a new reward formulation specifically tailored for virtual screening, which incorporates an exploration enhancement based on gradient uncertainty**, a feature absent from prior methods.\n\nWe also experimented with Direct Policy Optimization (DPO), another effective RL algorithm, for optimizing the policy network. As shown in the table below, GRPO consistently outperforms DPO in our setting. We attribute this to GRPO's use of group-wise advantage estimation, which better captures the relative quality of selected molecules within a batch (a property that aligns well with the objectives of virtual screening). This provides a direct motivation for our choice of GRPO.\n\n| Method | Model | Strategy | ALDH1 |  | PKM2 |  | VDR |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   |   | Iter 10 | Iter 16 | Iter 10 | Iter 16 | Iter 10 | Iter 16 |\n| GLARE | MLP | Policy (DPO) | 5.759 | 6.012 | 1.878 | 5.526 | 2.397 | 5.961 |\n|   |   | Policy (GRPO) | 6.574  | 6.535  | 2.067  | 5.904  | 3.173  | 6.512  |\n|   | GNN | Policy (DPO) | 4.892 | 6.743 | 1.927 | 6.491 | 3.618 | 6.635 |\n|   |   | Policy (GRPO) | 6.179  | 7.067  | 2.480  | 7.146  | 7.535  | 7.104  |\n|   | Pre. GNN | Policy (DPO) | 6.387 | 6.834 | 4.166 | 7.394 | 7.293 | 7.590  |\n|   |   | Policy (GRPO) | **7.274**  | **7.205**  | **4.547**  | **7.768**  | **7.932**  | **7.992**  |\n\nWe hope this clarifies the novelty and significance of our contribution.\n\n> W2: Incorporating Protein or Pocket Features in the Policy Model\n\nR: Thank you for this valuable suggestion.\n\nWe would like to clarify that our previous experiments have utilized protein features by incorporating DrugCLIP pretrained representations. However, the policy model did not include a protein encoder to fully exploit these protein or pocket-level features during the selection process. To address this, we have conducted additional experiments by explicitly integrating protein or pocket-level features into the policy model.\n\n| Method | Mol. Model | Protein Model | Strategy | ALDH1 |  | PKM2 |  | VDR |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   |   |   | Iter 10 | Iter 16 | Iter 10 | Iter 16 | Iter 10 | Iter 16 |\n| GLARE | MLP | × | Policy | 6.574 | 6.535 | 2.067 | 5.904 | 3.173 | 6.512 |\n|   | MLP | Pre. GNN | Policy | 6.732 | 6.607 | 4.159 | 6.575 | 4.124 | 6.716 |\n|   | GNN | × | Policy | 6.179 | 7.067 | 2.480 | 7.146 | 7.535 | 7.104 |\n|   | GNN | Pre. GNN | Policy | 6.501 | 7.175 | 4.027 | 7.318 | 6.827 | 7.224 |\n|   | Pre. GNN | × | Policy | 7.274 | 7.205 | 4.547 | 7.768 | 7.932 | 7.992 |\n|   | Pre. GNN | Pre. GNN | Policy | 7.027 | 7.269 | 4.672 | 7.795 | 7.897 | 8.021 |\n\nThese results also highlight both the strong extensibility and superior performance of our approach in virtual screening scenarios. We plan to further explore and design more effective protein feature fusion modules in future work.\n\n> W3: Scalability for Applications\n\nR: Thank you for raising this important point. \n\nWe would like to clarify that our method does not require training a separate policy model for each target. In our experiments, we utilized DrugCLIP-pretrained representations to encode protein or pocket features, enabling the model to generalize across multiple targets. For example, on the LIT-PCBA benchmark, our model simultaneously handles 15 protein targets and their associated binding pockets within a single framework, demonstrating our superior performance across all targets (Table3 in manuscript).\n\nTo better exploit protein or pocket-level information, we also have conducted additional experiments incorporating these features directly into the policy model via a protein encoder. During training, the policy model takes both the molecular features and the corresponding protein or pocket features as input. This design enables efficient many-to-many matching between molecules and multiple targets or pockets within a single model, significantly improving scalability and practical applicability in large-scale virtual screening scenarios.\n\n| Method | Mol. Model | Protein Model | AUROC(%) | BEDROC(%) | EF0.5% | EF1% | EF5% |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n| DrugCLIP | - | - | 57.17 | 6.23 | 8.56 | 5.51 | 2.27 |\n| GLARE(1) | Pre. GNN | × | 57.21 | 9.65 | 12.56 | 7.65 | 2.69 |\n|   | Pre. GNN | Pre. GNN | 57.96 | 12.08 | 16.17 | 9.11 | 3.10 |\n| GLARE(3) | Pre. GNN | × | 61.76 | 18.76 | 23.32 | 13.61 | 4.25 |\n|   | Pre. GNN | Pre. GNN | 62.51 | 21.36 | 26.96 | 15.57 | 4.62 |\n| GLARE(10) | Pre. GNN | × | 68.33 | 25.17 | 57.93 | 32.31 | 8.21 |\n|   | Pre. GNN | Pre. GNN | 69.79 | 27.25 | 61.49 | 34.49 | 8.90 |\n| GLARE(15) | Pre. GNN | × | 70.17 | 28.49 | 77.03 | 40.64 | 9.94 |\n|   | Pre. GNN | Pre. GNN | 71.72 | 33.72 | 81.69 | 43.80 | 10.66 |\n\n\n> W4: Baseline Methods in Experiments\n\nR: Thank you for your comments.\n\nIn active learning research, comparisons are typically organized around both acquisition functions (strategies) and surrogate models. Accordingly, TcsAL and PtAL each represent a series of baselines in our experiments. TcsAL includes baselines with various strategies and has been evaluated with both MLP and GNN surrogate models. PtAL, on the other hand, emphasizes comparisons using different surrogate models, including pretrained models such as MolCLR and MoLFormer, while employing two widely validated strategies (Greedy and UCB).\n\nTo further compare active learning methods with traditional virtual screening baselines, and to demonstrate the added value of GLARE, Table 3 presents results of GLARE and several virtual screening baselines (including DrugCLIP) across all 15 LIT-PCBA subsets.\n\nAdditionally, we have included comparisons with a classic baseline (ε-Greedy strategy) and a recently proposed baseline (Rank strategy)[1], as shown in the table below. While the Rank strategy performs better than the methods reported in Table 1, it still underperforms compared to GLARE. This is primarily because Rank remains reliant on manually defined selection strategies.\n\n| Model | Strategy | ALDH1 |  | PKM2 |  | VDR |  |\n|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n|   |   | Iter 10 | Iter 16 | Iter 10 | Iter 16 | Iter 10 | Iter 16 |\n| MLP | ε-Greedy | 5.395 | 5.794 | 2.019 | 4.102 | 3.814 | 4.370  |\n|   | Rank | 5.762 | 6.045 | 2.304 | 4.905 | 4.265 | 4.983 |\n|   | GLARE | **6.574**  | 6.535  | 2.067  | 5.904  | 3.173  |6.512  |\n| GNN | ε-Greedy | 3.861 | 4.267 | 2.135 | 2.914 | 2.911 | 3.327 |\n|   | Rank | 4.397 | 5.637 | 2.278 | 4.662 | 3.767 | 5.492 |\n|   | GLARE | 6.179  | **7.067**  | **2.480**  | **7.146**  | **7.535**  | **7.104**  |\n\nWe hope these extensive comparisons address the reviewer's concerns and further validate the robustness and effectiveness of GLARE.\n\n[1] Deng, Xun, et al. Improving the Hit Rates of Virtual Screening by Active Learning from Bioactivity Feedback. Journal of Chemical Theory and Computation, 4640-4651, 2025."}]

Author rebuttal:

Meta-review: This paper proposes GLARE, a reinforced active learning framework for virtual screening that formulates the problem as an Markov decision problem and leverages Group Relative Policy Optimization (GRPO). Reviewers appreciated the clear motivation, strong experimental results, and practical significance of the approach, particularly the demonstrated improvements over state-of-the-art AL methods and the ability to enhance VS foundation models such as DrugCLIP. The framework’s design, which integrates uncertainty-aware rewards and operates effectively within limited iterations, was considered well-suited for real-world drug discovery scenarios.

In the reviews and during the AC-Reviewer discussion, concerns were raised about the limited evaluation metrics (focus on enrichment factors without considering optimization of best binders). Still, the authors’ rebuttal was judged thorough and addressed runtime and methodological concerns convincingly. AC recommends that all rebuttal contributions are incorporated in the final version and that additionally the paper contribution is discussed in the context of the existing literature of RL for molecule design, which was overlooked in the initial submission.

The contribution is significant in bringing reinforcement learning into active learning for virtual screening, a domain where adaptive strategies are highly needed. Overall, while not transformative at the algorithmic level, the work is technically sound, well-executed, and fills an important niche in molecular AI. Given the empirical strength and practical impact, the paper is recommended for acceptance.
Decision: Accept (poster)