Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction
Authors: Zhiqiang Zhong
Davide Mottin
Venue: NeurIPS 2025 poster ()
Keywords: Molecular Property Prediction
Auxiliary Task Learning
Automatic Task Selection
Primary area: machine_learning_for_sciences
TL;DR: We introduce a fully automated framework that uses LLMs to seamlessly retrieve auxiliary tasks and employs adaptive weighting to integrate them for improved molecular property prediction.

Abstract: Recent studies in *Machine Learning* (ML) for biological research focus on investigating molecular properties to accelerate drug discovery. However, limited labeled molecular data often hampers the performance of ML models. A common strategy to mitigate data scarcity is leveraging auxiliary learning tasks to provide additional supervision, but selecting effective auxiliary tasks requires substantial domain expertise and manual effort, and their inclusion does not always guarantee performance gains. To overcome these challenges, we introduce *Automatic Auxiliary Task Selection* (AutAuT), a fully automated framework that seamlessly retrieves auxiliary tasks using large language models and adaptively integrates them through a novel *gradient alignment* weighting mechanism. By automatically emphasizing auxiliary tasks aligned with the primary objective, AutAuT significantly enhances predictive accuracy while reducing negative impacts from irrelevant tasks. Extensive evaluations demonstrate that AutAuT outperforms *10* auxiliary task-based approaches and *18* advanced molecular property prediction models.

Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction
Zhiqiang Zhongl:23 Davide Mottin'
'Deparmeent of Computer Science, Aarhus University
Institute for Advanced Studies, University of Luxembourg
3 Faculty of Science Technology and Medicine, University of Luxembourg Contacts: zhiqiang.zhong @uni.lu, davide@cs.au.dt
Abstract
Recent studies in Machine Learning (ML) for biological research focus on investi- gating molecular properties to accelerate drug discovery. However, limited labeled molecular data often hampers the performance of ML models. A common strategy to mitigate data scarcity is leveraging auxiliary learning tasks provide additional supervision, but selecting effective auxiliary tasks requires substantial domain expertise and manual effort, and their inclusion does not always guarantee perfor- mance gains. To overcome these challenges, we introduce Automatic Auxiliary Task Selection (AUTAu'T), a fully automated framework that seamlessly retrieves auxiliary tasks using large language models and adaptively integrates them through a novel gradient alignment weighting mechanism. By automatically emphasizing auxiliary tasks aligned with the primary objective, AUTAUT significantly enhances predictive accuracy while reducing negative impacts from irrelevant tasks. Exten- sive evaluations demonstrate that AUTAUT outperforms 10 auxiliary task-based approaches and advanced molecular property prediction models.
Introduction
Machine Learning (ML) continues drive advancements across diverse scientific disciplines, with biology standing out as key beneficiary [28] 11 63). Despite these advances, the limited avail-
ability of annotations in molecule datasets constrains the performance of ML models for molecular
property prediction [50] 61 26]. This limitation underscores the importance of strategies that enhance learning efficiency with minimal annotated data, e.g., semi-supervised learning [66 12] transfer
learning 49 7.
and few-shot learning [10 46
Among these strategies, common approach is to introduce auxiliary learning tasks, such as predicting molecular solubility to help estimate toxicity levels, so as to provide additional supervision from related tasks with easily obtainable labels that improve primary molecular prediction tasks 41 [5] By incorporating chemical, physical, structural, and toxicological profiles, these tasks help ML models represent underlying data structures more effectively and enhance generalisation 44 57]
This approach has shown benefits in molecular property prediction, where the objective is to determine key characteristics e.g., bioactivity, and organ-specific drug effects 51).
However, the process of constructing high-quality auxiliary tasks is complex and resource-intensive. These tasks typically derived from existing datasets [52 20 manually designed by domain experts to align with the primary dataset [27] [3]. In disciplines such as biology and chemistry, these challenges are partieularly pronounced due the scarcity, cost, and time-intensive nature of acquiring domain-specifer knowledge [63]. As datasets grow in scale and complexity, manual evaluation of task relevance becomes impractical, creating scalability bottlenecks [67]. Furthermore, effectively
incorporating auxiliary tasks into training frameworks remains an open problem [42 27 A common
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Table 1: Model comparison of AUTAUT and related work. A model is self-contained if it does not require additional datasets.
MORKL
TASK RETRIEVAL
AUTOMATIC
TASK SELECTION
AUTOMATIC
ADAPTIVE WEIGHTING
SILE-CONTAINID
NECUSSARY INPUTS
UNWWIGGTTE AVERAGEE
AUXILJARY TASES
TAG [
AUXILIARY TASKS
TASK2VBC
RELEVANT AUXILIARY DATASETS DESCRIPTIOS
GRADNORM
AUXILIARY TASKS
GS-META 68
X
X
X
AUXILIARY TASKS & RELATION GRAPH
MOLGROUP
RELEVANT AUXILIARY DATASETS DESCRIPTIONS
INSTRUCTMOL
RELEVANT AUXILIARY DATASETS DESCRIPTIONS
AVIAUT (OuRs)
NoNe
approach is joint training, where the model is trained simultaneously on both primary and auxiliary task labels to leverage additional supervision. The challenge lies in balancing the contributions of auxiliary tasks such that they reinforce rather than interfere with the learning objectives of the primary task [18]. Auxiliary tasks should provide useful inductive biases without introducing noise or misguiding the model [29 17). Poorly chosen or improperly integrated auxiliary tasks can dilute learning signals, exacerbate overfitting, or degrade the primary task's performance.
In this paper, we introduce Automatic Auxiliary Task Selection (AUTAUT), a novel framework that automates the retrieval, selection, and udaptive integration of auxiliary task labels to enhance molecu- lar property prediction. AUTAUT leverages the capabilities of Large Language Models (LLMs) to identify relevant auxiliary task labels and employs an adaptive weighting strategy to dynamically adjust their contributions to the primary task. Specifically, AUTAUT consists of three main com- ponents: (/) Auxiliary Task Retrieval: AUTAUT employs the information retrieval capabilities of LLMs to collect candidate auxiliary task labels from domain-specific databases or online resources. (2) Auxiliary Task Selection: Using multi-step prompting mechanism [ AUTAUT investigates the relevance of each candidate auxiliary task label to the primary task, ensuring that only the most relevant tasks are selected. (3) Joint Training Strategy: The selected auxiliary task labels are incor- porated into the training pipeline through a dynamic weighting strategy. This approach adjusts the contribution of each auxiliary task during training, ensuring that they provide proper supervision without overshadowing the primary task's 's objectives. Importantly, by automating the process of auxiliary task selection, AUTAUT eliminates the need for manual intervention and domain expertise, making it particularly suitable for molecular property prediction tasks with limited annotations.
We evaluate AUTAUT on 9 molecular property prediction datasets, demonstrating its superiority over 10 auxiliary task-based methods and 18 state-of-the-art property prediction models, Ablation studies further validate the effectiveness of AUTAUT's task selection and adaptive reweighting in improving robustness, while also analyzing the impact hyperparameters and different LLM choices.
2 Related Work
ML for Molecular Property Prediction. ML models have become integral to molecular property prediction, leveraging their capacity to encode structural information and capture complex relation- ships in molccular data [24, [T]. State-of-the--a methods include graph neural networks [53] 62 and transformers 45 which utilize molecular SMILES strings, structures, and other molecular descriptors. Despite their success, these models often depend on large, annotated datasets, limiting To overcome this limitation, approaches like semi-supervised learning [66]]] [2], and transfer learning 49 have been proposed.
their applicability in data-scarce domains 63 26
Additional Supervision from Auxiliary Tasks, Auxiliary tasks have proven to be an effective strategy for enhancing the performance of the primary task by leveraging related, easier-to-obtain labels [52 27 In the context of molecular property prediction, auxiliary tasks have included predicting molecular fingerprints, physicochemical properties, and toxicity profiles [3] 44 36). These tasks enrich representation learning and improve generalization, particularly in data-scarce scenarios g 40 [7]. However, current approaches often depend on manually curated auxiliary
tasks informed by domain knowledge [27 63 5 This manual curation introduces biases and limits
scalability, posing challenges when adapting to larger datasets or emerging domains where expert annotations are unavailable (20 67].

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Furthermore, integrating auxiliary tasks into training frameworks remains significant challenge. Balancing their contributions and avoiding interference with the For example, Lyle et al. [35] investigate the effect of auxiliary tasks on learned representations, and Liu et al. [29] address the issue of conflicting gradients between auxiliary tasks, which can hinder convergence. Poorly chosen auxiliary tasks can lead to adverse outcomes such as overfitting or optimization divergence [17]
Integration of Auxiliary Tasks.
primary task is complex 42 18
Novel Capabilities of LLMss. The advent of LLMs has unlocked new possibilities for MI across L.Ms, with their vast pre-trained knowledge and advanced reasoning capabilities, have been leveraged to augment feature extraction, data annotation, and knowledge integration 4 [9]. Their ability to adapt across diverse contexts makes them particularly appealing for applications where traditional ML approaches struggle with data sparsity or require extensive domain expertise [21]
various domains, including molecular property prediction 6 38
Discussion. However, leveraging LLMs for auxiliary task retrieval is not only underexplored but also inherently challenging. Identifying relevant auxiliary tasks requires reasoning over complex domain-specific relationships, integrating structured and unstructured knowledge, and ensuring that selected tasks contribute meaningfully to the primary learning objective. The non-trivial nature of this problem stems from the need to balance task relevance, diversity, and generalization while avoiding spurious correlations. Addressing these challenges a key focus of our work.
Table ! summarizes the key advantages of the proposed AUTAUT, comparing it with several state-of- the-art art methods. Notably, AUTAUT the only fully automated framework that eliminates the need for human intervention in designing or selecting auxiliary tasks and curating additional datasets. It entirely self-contained, relying solely on the L.M without requiring any external data. more detailed discussion is provided in Appendix E due to space constraints..
3 Proposed Framework: AUTAUT
Request
Output
Predictions
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Muteculo looo.
MuRecuar 2 NOI Jons.
And model.
DSta & Primary bakk (3), Nuy)
Training
(Bny Miq...)
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(1) Acadllayy taok retrivevll
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Figure 1: Overview of the AUTAUT framework. There are three main steps: (i) Auxiliary task retrieval: AUTAUT leverages the information retrieval capabilities of LLMs to retrieve diverse set of obtainable auxiliary tasks from online resources or domain-specific databases. (ii) Auxiliary task selection: AUTAUT guides LLMS to evaluate the relevance of each auxiliary task to the primary task and select the most relevant ones. (iii) Joint training: AUTAUT employs dynamic weighting strategy to adjust the contribution of each auxiliary task during training, ensuring that they provide meaningful supervision without diluting the primary task signal.
3

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3.1 Preliminary
Let D denote the primary dataset, partitioned into training (DTrAiN) validation (DALLI), and test (Drest) subsets. Each molecule in D is represented as M = (X, Y). where X denotes the molecular representation, such as a SELFIES string, or graph structure, and Y corresponds to its associated property label, such as water solubility or toxicity.
The primary task, TM. aims to learn a mapping function fo : X' - VM that predicts molecular properties using the training dataset PIRAIN. The set of target molecular properties is denoted as I'M = {YM,, Y........ Beyond the primary task, we consider a set of auxiliary tasks, Ta. where each task is defined by an auxiliary label Ys, associated with the same representations as in DTRAIN. The corresponding training datasets, denoted as DIRAIN share molecular representations with D TRAAI but have different property labels. The objective primary task performance by leveraging both primary task labels Vv and auxiliary task labels V
optimize the model parameters improve
However, selecting useful auxiliary tasks is non-trivial. Not all auxiliary labels contribute positively to the primary task, and in practical scenarios, predefined auxiliary task labels VA may not be available. Thus, tackle two challenges. The first challenge is to automatically identify and select beneficial auxiliary task labels y&s S Vv. The second challenge is to develop an effective joint training strategy that integrates the selected auxiliary tasks into the primary learning process.
3.2 Overview of the Framework
Our proposed framework, AUTAUT, is illustrated in Figure I Given a user request specifying the primary task (e.g., predicting molecular toxicity) and the primary dataset, which includes the training dataset with labels, AUTÀUT operates in three stages: First, AUTAUT automatically retrieves potential auxiliary task labels from online resources or domain-specifte databases. Second, AUTAUT generates a brief summary describing the correlation between auxiliary task labels and the primary task and selects a subset of auxiliary task labels based on the generated summary. Finally, it applies a joint training strategy to integrate the selected auxiliary task labels into the primary task training process, improving predictive performance. By following these steps, AUTAUT enables molecular toxicity prediction with auxiliary task enhancement, without requiring manual efforts.
3.3 Automated Auxiliary Task Retrieval and Selection
This section outlines the methodology for designing prompts that enable LLMS to perform automated auxiliary task label retrieval and selection. We emphasize that prompt engineering is not the primary focus of this work, and the prompts used here are intentionally simple and minimally refined. See Appendix A for our complete prompts and obtained outputs.
Auxiliary Task Retrieval. Given a molecular dataset D and the primary task labels VM. a prompt is designed instruct LLMs retrieve potential auxiliary task labels V., The objective is to construct query Q that enables the LLM (fLLM) to generate a structured response A, where A = fLLM (). The response A represents a set of candidate auxiliary task labels VA, relevant the primary molecular property prediction task.
The prompt consists of two main components: (1) Instruction: Provides general guidance to the LLM, specifying its role in the retrieval process (e.g., expertise in chemistry, finance, biology, etc.). (2) Message: A direct and clear request for the LLM to identify potential auxiliary task labels based on the given context (e.g., molecular properties or computational tools). This approach yields a computable auxiliary task label set V/.. An example prompt and partial response from the LLM are provided in Appendix [] demonstrating that even basic prompts can generate meaningful and relevant auxiliary tasks. We provide the details of collected molecular properties in Table
Auxiliary Task Selection. After retrieving the potential auxiliary task label set Va. the next step evaluates the relevance of each auxiliary task label to the primary task labels Va. To achieve this, AUTAUT employs a multi-step prompting mechanism [8 59 9 that refines the L.M''s responses and summarizes the correlations to quantify the alignment between the objectives of the auxiliary and primary task labels.

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The multi-step prompting mechanism proceeds as follows: (SI) The LLM retrieves relevant in- formation about the auxiliary task labels V' from online resources or nomain-speciffe databases. ensuring that essential domain-specific details are gathered. (S2) The LLM processes the retrieved information and generates a brief summary of each auxiliary task label. These summaries incorporate domain-specific knowledge or information from the previous step. (S3) The LLM selects the top K auxiliary task labels V/s S Vx, that are most relevant to the primary task labels Vn. ensuring that only the most useful auxiliary tasks are integrated into training. An example prompt and a partial response from the LLM are presented in Appendix AA. To improve the consistency of the LLM's behavior
during auxiliary task selection, we set the LLM's hyperparameter temperature to 0.2. The final set of selected auxiliary tasks is shown in Table 5
The selected auxiliary task labels V/s form the foundation for enhancing primary task performance in the subsequent joint training phase. By automating this process, AUTAUT identifies and selects auxiliary tasks without requiring manual curation or extensive domain-specific expertise.
3.4 Learning to Learn from Selected Auxiliary Tasks
The primary task aims to learn function fo with parameters 0 that predicts molecular properties V from molecular representations. At its core, this is an optimization problem where the objective is to maximize the likelihood of the primary task:
(0) - po (Drrn (0) - log p():
where P(PIRAIN ( represents the likelihood of the training data, and p(0) is prior knowledge over model parameters.
Besides, selected auxiliary task labels V's provide additional signals by acting as informative con- straints on the optimization landscape. Given an auxiliary task k, its corresponding objective is:
C (0) log p AR 10) log)(0).
By incorporating auxiliary tasks, the overall training objective is redefined as:
a) + Σ C
where ag represents the contribution of auxiliary task k and a are trainable parameters that dynami- cally adjust during training. Since auxiliary tasks vary in their usefulness, their weights should reflect their contribution to improving the primary task. A well-chosen auxiliary task should exhibit gradient alignment with the primary task. Auxiliary tasks with higher alignment are assigned greater weight during training, allowing the model to focus on tasks that reinforce relevant learning signals.
Theorem 1 (Subspace Alignment for Optimization). If the gradient of the primary task loss Cm) lies entirely within the subspace S spanned by auxiliary task gradients, then the auxiliary tasks fully support primary task optimization:
K LL Σ aK (O) =0 0.
(4)
This result ensures that when auxiliary tasks are properly weighted, they can serve effective surrogates for the primary task and contribute directly to its optimization (proof in Appendix B.II.
Optimizing auxiliary task weights. To dynamically update the auxiliary weights a, we minimize the Fisher divergence between the primary task gradient and the surrogate prior induced by the auxiliary tasks:
min Eg V log (TT)0)) Pl (0)
(5)
& This optimization ensures that the auxiliary task contributions are continuously refined based on their alignment with the primary task.
Theorem 2 (Generalization Bounds with Auxiliary Tasks). Incorporating well-aligned auxiliary tasks reduces the hypothesis class complexity, which we formalize via the Rademacher complexity RR, (H). This leads to improved generalization:
E()) < ε(0) + Rr (Ha):
(6)
5

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where (() is the empirical enror, Ha is the hypothesis class augmented with auxiliary tasks, and c a constant.
This bound highlights that by adjusting auxiliary task weights effectively, we constrain the hypothesis space, leading better generalization and improved predictive performance (proof in Appendix B.2).
Training procedure. The training process consists of three phases. First, in the weight initialization phase, the auxiliary task weights a are initialized based affinity scores derived from gradient aligmment. The model is then trained on PTAAIN to establish a baseline. Next, during dynamic weight adaptation, the auxiliary task weights are iteratively updated using gradient alignment and validation performance on DVALID- The update follows the rule:
(++1) = a - -V or V l(gg)(Tx))) V (a) 0%
(7)
where 3 is the learning rate for task weights. Finally, in the model fine-tuning phase, after the task weights stabilize, the model parameters 0 are optimized with fixed auxiliary task weights. The final model is evaluated on DIEST to assess improvements in molecular property prediction. Algorithm [ provides an overview of the AUTAUT's complete pipeline. Further implementation details are available in Appendix C Appendix D provides a computation complexity analysis and some training information. Overall, AUTAUT achieves a balance between improved learning efficiency and computational cost, making it scalable approach for molecular property prediction tasks.
4 Experiments
4.1
Datasets
This paper selects various datasets from a widely used benchmark, MoleculeNet 52) to examine the effectiveness of our algorithm for molecular property prediction. We include 6 representative datasets for the property classification tasks, BBBP, BACE, CLINTOX, Tox21, TOXCAST, and SIDER, and 3 for the property regression tasks, ESOL, FREESOLV, and LIPO. Note that these datasets do not We follow the previous work to adopt scaffold splitting (17 divide the datasets into training, validation, and test sets, where molecules are partitioned based on their core scaffolds to ensure structurally dissimilar compounds appear in different splits. Appendix presents additional details about the datasets and splits.
have features duplicated with the auxiliary tasks collected in Table 4
4.2 Competing Models
We compare AUTAUT with 10 auxiliary tasks selection approaches, using 3 base ML models (in- cluding GCN 23 GIN (55] and GRAPHORMER (60)). The 10 auxiliary task selection competing methods including beam search method (37], grouping- based methods and methods (including TAG 8 TAsK2vEC M. and MOLGROUP (17) follow the pre-training and fine-tuning pipeline. Moreover, we select Unweighted Uvera GRADNORM 33. MTDNN 25). GS-META (68), and INSTRUCTMoL. (51) Furthermore,
present the results of the Pretrain-Finetune (PF) strat- egy 15), where the model is first trained on PCQM4Mv2 14] and then finetuned on the down- stream dataset. In addition, we compare AUTAUT with 18 advanced molecular property prediction models to demonstrate its effectiveness and generalization. Such as GCN, GIN, GRAPHORMER, D-MPNN (58), ATTENTIONFP (54), MGCN (34), N-GRAM [1I] PRETRAINGNN 13), GPT-
GNN 16 GROVER BASE 39). GROVER
3D-INFOMAX
43
GRAPIMVP
33 MOL-
CLR 47). UNI-MOL 665 GEM 6,, PIN- TUN-TUNIG (30 and LAC [66]
Details about these compet-
ing methods and other experimental settings can be found in Appendix
E F Our code and data are
available at https::/ //github/ com http:/iiiggggggggg////aaaaa/a AUTAUT
4.3 Main Results
AUTAUT surpasses auxiliary task selection methods. T AUTAUT with 10 auxil- iary task selection methods across 9 molecular property prediction tasks and three ML models. The results consistently highlight AUTAUT's superior performance, showcasing its ability to effectively select and integrate auxiliary tasks to enhance downstream predictions.
AUTAUT achieves the highest performance on classification tasks, delivering a 6.4% improvement on BBBP with GCN and 5.1% improvement on BACE with GRAPHORMER. These significant

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Table 2: Performance of ML models with different auxiliary task selection methods on molecular prediction tasks. For classification tasks, we calculate the ROC-AUC, while for regression tasks, we use RMSE the evaluation metric. The number in the bracket is the standard deviation of 5 runs.
CLASSIFICATION (ROC-AUC (-2
REGRESSION (RMSE
DATASETS
BRBP 2,039
BACE 1,513
CLINTOX 1,478 2
Tox21 7,831
TOXCAST
SIDER 1,427 27
ESOL 1,128
FREESOLN 642
LIPO 4,200
# MOLDCCURS
8,575
# TASKS
12
617
GCN
23.45200s
24
56. 28
74.63
65.38 m5.38
62.24 27
3.165 20
3.752
1.672
BEAM SEARCH
66.08.0003
68.42.0m
58.32 38.3220-5
75. 30
66.45_mmm
62.87
3.205
3.415g00s
1.685.0058
+TAG
64.65.00
72.10_mm
57.1240.06
71.7810m
62.6340.3s 65.08_6.51
1.340
3.885 ao
1.12220014
TTSKKVvc
68.25 .mm:
2m.
49.55x0.37
20.20..o:
63.62_...0
62.25....5
3.278 40.44
3..37
1.742.0.06
+MIDNN
66.63
70.
53. 53.25
s0.95.0mm
64.53 0.22
61.95
s0.45
3.420
1.715 s0.15
+UA
60.38
62.48:nam
51. $1.95
70.95 200:
62.75
60.18
3.428 s0.472
4.210
1.810:0m1
+GRADNORM
61.55ammm
65.12
53.45an.0r
59 59.88
=2.05
58 58.20
3.558 20.00
1.855 44410aumm
57.12.00s
65.22220m
45an
11.12
58 28.62.1.0
3.658 4m
go 4.508gu
199022000m
Min GROUP
08.47.20.3
2mm
60. 38
12
66.95_09
63. 12.122
2.125
1.70220001
+GS- -GS-META
66.72 20.72
g5.7
63.25 (3.255...0
75. os
65.94 g0.
63.2220..0
3.038 10.41
3.444
1.8.9
+NNSTBUCTMOL
68.50
80.21sa.m
64 64.02
76. 18.09
64.30 a0
2.612
2. 108 e0.0us
1.232 1.232
+AUTALT
69.72.00:
81.00 83.10gam
a7.
68.05
64.6220.4
1.885
2.285 199
GIN
66 12.00
77.
56.48 56.48 27.4
75. 17
62.35_..o
60. 60
3.865 10895
1 1.692.0011
BEAM
67.78
81.92.n.s
76.95.o.12
97.05....z
65.28
62.57
2.943
3,375
1.688...
+TAG
61.25.:002
58.25
22.08.10m:
61.75 64.38-6.1
3:275 10.00
1.002
1.752.0015
+TASK2VEC
68.48.0m
62.9..4
69. 12.12
63.62_naa
60.38
21242
3.452
1.775 1.775 20017
+MTDNN
66. 66.9220m
53 2822
72. 12.32.a0
64.58
61
61.922n.
3.182 20
3.468
1.72220044
+IA
60.75200:
52. 52.188 68.mm
71 71.22200:
62.18_6.
59 59.82210
2.4
1 1.8222001
GGRADNORM
61.68:102
65.38_00:
33.58
60.25 60.251005
58.55 62.08_6.a
4.508
10878
65.
51.38 56.38
60.92 77.72.smm
18.38
3.695 s0.77
4.608
10.70
1.932.0021
MMLGROLP
69m
83.12
81.25
67.55 0:22
10.28
1.882
1.952
10.025
1,288 50.02
+GS- META
66.46.300
79.16=m.
73.5 23.53
76.27.20.s
66. 14..4
62. 22.96
2.060
3.848
1 1.4312000
INSTRUCTTMO..
69. 20.87
81 21.002001
76 16
77.91 77.91an.0
68 14
64 64:25_20.2
1 08
1.960ga.00
1.289 2.289.n.com
AUUTUTT
70.5M
83.62
82.52.0.24
78.32:0.0
69.38
1.870 s0.c25
1.900
1.270
GRAPHORMER
67.05
89.18-
78. 12.32
67.12 15.55
70.08
2.102
1.795
1.282
+BEAM SEARCH
67.78
81.12
28.2223..0
67.82 70.55 m
1.825
1.3222u004
+TAG
35.35
g
66.25_mmm m8
69. 25 75
go
2.8
2.375
TTASK2NIC
68. 20
48 35
69. 2:
65.12_g0
67.55
2.275 20
1.852
1.362 10162-0015
4 MMDDNN
67.08
20.52.amm
53 53.5220.3r
72.45.00:
65.55
68. 18.12..0.a6
2.202
1,875
1.335
UA
60.82 1o.0:
52.52.0.36 62.
91.52.00.
63.52
66..72.0.24
2.455
2,055
1.432 2:205
+GRADNORM
62.02:102
65.52
53.82
60.52.200
65.52.0.25 62.82-0.25
2.605 so.cTo
1.525 20020
+PF
57.52,um0
65.82anus
36.5229..4
51.52200z
61.22220:2
64. 21.82
2.653 2n.eu
2.305
I 1662520ms
+MoL GROUP
69. m
83.15
81 ..22
77. m
68.85_m.m
70. a7
1.902
1.805 2000
1 1,292 000
-GS-META
68. 68.1220.03
81.06=0.1
80 90.00
77. os
67.90_2650
70: 34
2.06420455
1.881
UNSTRUCTMML.
10.02
81.4 81.41.00 83.522m.0
78.0510.04
60.03.007
70.49_6.44
1.901 20.01
20.00
1.230,o.co
+AUTALT
70.322.0m
83.72gm
82.552n.34
78.52200:
69.38_6.2
71.55
1.87230.2
1.772g00:8
1,262g000
gains highlight AUTAUT's ability to identify meaningful task relationships and leverage them effectively for prediction. For regression tasks, AUTAUT consistently achieves the lowest RMSE values across all datasets, reflecting its strong predictive performance and ability to generalize effectively across diverse molecular datasets. Moreover, the low standard deviations observed across five runs underscore AUTAUT's robustness and reliability. Its adaptive weighting strategy effectively balances contributions from auxiliary tasks, mitigating overfitting and ensuring consistent improvements in prediction accuracy.
AUTAUT demonstrates superior molecular property prediction performance. Table 3 bench- marks AUTAUT against 18 state-of-the-art molecular property prediction methods on 9 datasets. The results consistently demonstrate AUTAUT's superiority, establishing it as effective and practical solution for molecular property prediction. AUTAUT achieves competitive performances across all datasets. These results emphasize AUTAUT's strong generalization capabilities and its ability to outperform competitive baselines. In addition, AUTAUT exhibits remarkable robustness, as evidenced by its low standard deviation across tasks, further reinforeing its reliability in practical applications. These characteristics, combined with its consistently superior performance, establish AUTAUT as an effective solution for diverse molecular property prediction tasks.
superion
4.4 Analysis
LLM-selected auxiliary tasks are chemically meaningful and stable across runs. Table 5 in the Appendix presents the auxiliary tasks selected by the LLM across datasets. We observe that key molecular descriptors, such as LogP, TPSA, and Molecular Weight, are the tasks of choice across repeated runs and diverse prediction tasks. This indicates that LLMs can infer chemically meaningful and task-relevant patterns from textual cues, without relying on handcrafted templates molecular priors. The selections are also sensitive to the target task. For instance, polarity-related properties are more frequently selected for solubility prediction highlighting task-specific adaptation. To reduce
7

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Table 3: Performance on molecular property prediction tasks. For classification tasks, we calculate the ROC-AUC, while for regression tasks, we use RMSE as the evaluation metric. The number in the bracket is the standard deviation of 5 runs. 1 indicates higher is better and + indicates lower is better.
CLASSIFICATION (ROC-AUC
REGRESSION (RMSE
DATASETS
BBBP
BACE
CLINTOX
Tox21
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SIDER
ESOL
FREESOLY
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63. 40.05
74.83
56.28
74.63
65.38
62.24
3.165
3.752
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GIN
s0.00
+0.45
56.48
75.32sa.17
62.35
60.25-0.0
2.895
3.865
1.692nnm11
GRAPHORMER
27.05
79.18
78.12
75.55
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2.102
1.795
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D D-MPNN
70. 20.83
81.19
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76.24
64.97
27.31
2. 2.080
0.685
ATTENTIONEP
64.15
78.24
78.24.0.31
$0.51
72.00
63.52.a.54
60.12-5.m
a.850
2.075
0. 0.720
MOCN
65.2 65.27
73.01
89.51
77.15
66.03
58.02-1M4
1.100
2.800=631
0.730
N-GRAM
20.15
25.45
77.21
62.04goom
58, 58.94_5.00 00
1.150go.os
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0.735
PEETRAINGINN
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63.96 63.96g1.58
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GROVER
65.02 65.02:0.10
$1.01
74.92
77.31
62.97
61.01
1,085
2.270
0.820
GROVER
60.04 60.04:0.31
80.99
74.01
77.10
63.99.0.5
60.02-..0
0.980
2..80
0.815
3D-INFOMAX
27.02
84.01=1.31
78.03
77.25
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=.6000
8000
GRAPHMVP
71.98 11.98.1..4m
83.02.0.77
85. 66.00
a7.40
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-3.00
0.890 s0.000
2.330
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MOLCLR
71.51
83.48 83.4880.52
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66.98
64.05
0.940
0.650 2.580
MOL
72. 72.83aa.48
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89.97
77.50
67.03
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AUTAUT
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0.580
OTTPAA
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0.2
Training loss
weight
2
0.2
Training loss AUTALT
Training AUTAUT
Training loss wlo AUTAUT
0.1
Training loss wlo AUTAUT
0.1
50
100
300
100
150
Epach
Epoch
(a) BACE
(b) ClinTox
Figure 2: Auxiliary-task weights (left axes) and training-loss trajectories (right axes) for two datasets.
generation variance and improve reproducibility, we set the LLM's 's temperature to 0.2, which helps the model produce consistent outputs while remaining responsive to different task contexts.
Auxiliary task influence is correctly inferred. Figure 2 shows the evolution of the learned auxiliary weights (ax) throughout the training process. AUTAUT progressively increases the contribution of auxiliary tasks that correctly align with the main prediction objective, while suppressing the influence of others. This results in more targeted supervision and faster convergence, as reflected in the consistently lower training loss compared to the baseline without adaptive integration. The adaptive weighting mechanism plays critical role in filtering out noisy gradients and preventing negative transfer, which is particularly important when auxiliary tasks vary in relevance.
Auxiliary task selection and adaptive weighting complement each other. Naively introducing auxiliary tasks, especially without proper selection or weighting, can hamper model performance due to conflicting or irrelevant supervision signals. As UA draws auxiliary targets uniformly at random from Table 4 and attaches them the backbone with equal weight. Its results in Table 2 support this observation: random auxiliary tasks significantly reduce performance, with regression tasks like FREESOLV and LIPO being particularly affected. Applying task seecispec weighting (e.g., GRAD- NORM) mitigates the drop by suppressing noisy gradients, but still fails to match the performance of our full method. Ablation analysis in Figure [a confirms that both components are necessary: selection ensures the relevance of auxiliary tasks to the primary objective, while weighting adjusts their influence during training to prevent negative transfer. The strongest gain is achieved only when both mechanisms are applied together, highlighting their complementary nature.
8

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Parforetanno (Relaive to AUTAUT)
0C CAIN
B
ACTAUT
0
2000
wio Auaiiar Tenk Selection
DSCCN
4 MUTTATT (GPE-
with Weighing
AATATRT (Grmi 2.00
DROP
BACE
Chaffon
Tocl
ToaCatt
SIDER
cora
(a) Relative performance compari- son of AUTAUT w/ and w/o task selection and adaptive weighting.
(b) Relative performance across datasets for K E (3,5,7.9) on the Graphormer with AUTAUT.
(c) Relative performance compari- son AUTAUT and AUTAUT vari- ants with different I.M.s.
Figure 3: Ablation study.
The number of selected auxiliary tasks (K) balance informativeness and noise. The perfor- mance of AUTAUT depends on the number of selected auxiliary tasks, K. As shown in Figure 3bb using too few tasks (e.g., K = 3) underutilizes auxiliary supervision, while larger values (e.g.. K 10) introduce redundant or noisy signals. Yet, we find that K = 5 consistently yields the best results across datasets, providing strong balance between task diversity and relevance. The sensitivity to K is more pronounced in regression tasks such as FREESOLV, ESOL, and LIPO, where auxiliary signal quality is critical. In contrast, classification tasks like BBBP and BACE are more robust to variation K. These results highlight that AUTAUT requires only minimal hyperparameter tuning tailored to the primary task in order to prevent underfitting or negative transfer.
All major LLMs produce useful task suggestions and improve over baseline. We evaluate the robustness of AUTAUT across three LLMs: GPT-4, Gemini 2.0, and LLaMA 3. As shown in Figure 3c| all variants consistently outperform the no-auxiliary-tass baseline across all 9 datasets, confirming the general effectiveness of LLM- -LMggudded auxiliary supervision. GPT-4 yields the most stable performance overall and serves as our default configuration. Gemini 2.0 occasionally surpasses GPT-4, particularly on datasets like CLINTOX, Tox21, and FREESOLV, where its selected tasks, such as Eccentricity and TPSA, better align with regression or toxicity objectives. In contrast, LLaMA 3 tends to select a smaller and more conservative set of descriptors, achieving slightly lower but still competitive results. These findings show that while the choice of L.M can influence auxiliary task quality, our framework remains -agnol-agnostic and effective across different foundation models-even in the absence of comainn-specife pretraining or external chemical knowledge.
Furthermore, to isolate the effect of the LLM from that of the optimization procedure, we report the selected auxiliary tasks by different LLMS and the learned weights in Table D Across datasets, physicochemical descriptors such as LogP, Topological Polar Surface Area, and Number of Hydrogen Bond Donors are selected with both high frequency (k) and relatively large weights (w), indicating that the LLM repeatedly identifies domain-relevant features and that the optimizer further amplifies their contributions. Dataset-specific descriptors (e.g.. Balaban J Index or Labute Approximate Surface Area) appear less frequently and receive smaller weights, suggesting complementary but secondary roles. The overall consistency of top-k tasks across GPT-4, I.lama and Gemini 2.0 supports that the improvement observed in Figure Ba originates from the semantic alignment introduced by the LLM, rather than from random variation or optimization alone. These results further confirm that the LLM mainly contributes to identifying semantically relevant auxiliary tasks, while adaptive weighting refines their relative importance during training.
Auxiliary tasks outperform static feature inclusion. We further compare the effect of integrating selected molecular descriptors as static input features versus treating them as auxiliary prediction tasks. Table in the Appendix shows that feature inclusion leads to marginal or inconsistent improvements, while modeling the same descriptors as supervised tasks with adaptive weighting yields substantial gains across all models and datasets. This highlights that auxiliary information is more effective when used as structured supervision rather than passive input, reinforcing our design choice to treat descriptors tasks rather than features. More discussions are provided in Appendix G

---

5
Concluding Remarks
This paper introduces AUTAUT, a fully automated framework that leverages LLMs to retrieve auxiliary tasks and integrates them via adaptive weighting to improve molecular property prediction. AUTAUT outperforms 10 auxiliary task methods and /8 advanced prediction models, with empirical analysis highlighting the complementary roles of task selection and adaptive weighting strategy. Future work will explore incorporating domain-specifer knowledge into the retrieval process and extending AUTAUT to other scientific domains such as materials science.
Broader Impact. This work presents an automated framework for selecting and integrating aux- iliary tasks in molecular property prediction using LLMs. By reducing reliance manual feature engineering and expert-curated descriptors, AUTAUT Towers the barrier to applying auxiliary-tass based learning in data-scarce scientific domains. This may accelerate applications in drug discovery, materials design, and environmental modeling, particularly in settings where annotated data expen- sive limited. However, AUTAUT depends LLM- -generated auxiliary task candidates, there is potential for biased or irrelevant suggestions, especially if the LLM has been exposed to skewed or incomplete chemical knowledge during pretraining, Misguided task selection could propagate into downstream predictions, especially in safety-critical domains like pharmacology. Responsible use of AUTAUT should include validation of selected tasks and awareness of LLM limitations.
Limitations. While AUTAUT achieves strong performance across molecular datasets, it has several limitations. First, it assumes that auxiliary tasks are computable and available in standard chemin- formatics toolkits (e.g., RDKit); tasks requiring experimental data or rare descriptors are excluded. Second, although the method is fully automated, it relies L.M outputs, which may vary with prompt phrasing, temperature settings, or model version. Although we observe stability across five runs, reproducibility across future LLM updates may require prompt standardization or model pinning. Moreover, our experiments are limited to MoleculeNet-style benchmarks. The generalization of AUTÀUT other domains (e.g., materials science, systems biology) remains to be validated. Lastly, the gradient alignment strategy assumes that auxiliary tasks can be meaningfully aligned with the primary task, which may not hold in more complex or noisy multi-modal settings.
Acknowledgments
We gratefully acknowledge the EuroHPC Joint Undertaking for awarding computational GPU re- sources under the EuroHPC Benchmark Access and EuroHPC Al and Data-Intensive Applications Access Calls. Specifically, we thank EuroHPC for providing access to the MeluXina supercomputer hosted by LuxProvide Luxembourg, which was essential for conducting our computational experi- ments. Additionally, this work is partly supported by the funding from the Institute for Advanced Studies the University of Luxembourg, and the European Union's 's Horizon 2020 research and innovation program under the Marie Skłodowska-Cun Actions(GA # #101081455)
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NeurIPS Paper Checklist
Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The claims in the abstract and introduction well-aligned with the paper's actual contributions. The paper proposes fully automated framework for auxiliary task selection and adaptive integration using large language models and gradient alignment. These contributions are consistently reflected in the method section, theoretical analysis, and empirical results. The scope and limitations are appropriately bounded.
Justification:
2. Limitations
Question:
Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: The paper includes limitations and future work discussions in Section and throughout the text. It acknowledges the dependency the quality of LLMs, the computational cost of query generation, and the fact that results are only demonstrated on Molte datasets. The discussion is honest and appropriately scoped for research.
3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?
Answer: [Yes]
The paper presents two formal theorems, each clearly stating assumptions and objectives (e.g., gradient alignment aiding optimization and generalization.. Full proofs are provided in the appendix. Notations and conditions are well-defined, and references to prior theory are appropriately cited.
Justification:
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: The paper provides extensive implementation details (Section Appendix A Appendix C Appendix Appendix (F), including prompts, model variants, training curves, hyperparameter choices, and ablation setups. Auxiliary task sources and LLM settings (e.g., temperature) are documented. The provided supplemental material supports the reproduction of the main claims.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [Yes]
The paper uses public datasets (e.g.., MoleculeNet, PCQM4Mv2), and code and scripts are promised to be released upon publication, with implementation details included in the appendix. GitHub link (https: / tig.....b... com/ zhiqi wng.hhngggghgddd AAUAAd///AAAAAUAA AUTAUT) suggested in the supplemental material for reproducibility.
Justification:
6. Experimental setting/details
Question: Does the paper specify all the training and test details (c.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
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The experimental design, dataset splits, model architecture, optimizer settings, hyperparameter ranges, and evaluation metrics are all clearly specified (Appendix Ap- pendix FD. Extensive ablations and L.M variations are provided to show robustness across settings.
Justification:
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: Results are averaged over five runs with standard deviation reported (see T Table 3] and ablation figures in Section (.4).. Variability is due to random seeds and LLM outputs. The methodology for reporting is clearly stated, including consistent use of standard deviation as error bars.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Appendix F reports training on NVIDIA A100 GPUs and estimates wall- clock time for each experiment. LLM calls are limited to task retrieval and executed once per run. The total compute cost is moderate and disclosed.
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurlPS Code of Ethics https: http://nnurippppe http:///wb...ttticccciieeeeeeee
Answer: [Yes]
Justification: The research follows NeurIPS Code of Ethics. All datasets are public, no sensitive data is used, and LLMs are queried through standard APIs under accepted terms of use. No human subjects or private data are involved.
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification:
The paper (SectionS) discusses both potential positive impacts (lowering barri- ers to applying saalliay-ttskk-aee based learning in molecular discovery) and risks (over-reliance LLMs or domain hallucinations). It also acknowledges limitations in generalization beyond molecules.
Safeguards
Does the paper deseribe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
The paper does not release any high-risk model or dataset with potential misuse concerns. The use LLMs confined narrow auxiliary-task queries, and no generative models or sensitive data are released.
Justification:
12. Licenses for existing assets
Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Answer: [Yes]
Justification: All datasets (e.g., MoleculeNet, PCQM4Mv2) are cited with their original licenses. parrd-party libraries and L.LMs (e.g., OpenAl GPT-4, Ilama 3) are used under documented terms of service and cited properly.
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13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: Any new datasets, code, or generated descriptions are documented and accom- panied by usage instructions and license information in the linked project file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve any human participants or crowdsourcing. 15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: No human subjects are involved; therefore, IRB approval is not applicable.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology. scientific rigorousness, originality of the research, declaration is not required.
Answer: [Yes]
Justification: The use of LLMs is central to the method (automatic auxiliary task retrieval and selection). This usage clearly described the methodology and experimental setup, including prompt design and temperature setting.
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A Prompts
A.1 Auxiliary Task Retrieval Prompt
Auxiliary task retrieval converzation:
(For better visualization, complete converzation details can be found in our GitHub project.)
Instruction:
expert in computational chemistry and machine learning for molecular property prediction. Your task is to provide detailed and accurate information about computable molecular properties.
You
Message 1:
Find all available computable molecular properties.
Response: Computable molecular properties refer properties of molecules that can be calculated or predicted using computational chemistry, cheminformaiics, or machine learning techniques. These properties be derived from the molecular structure and used in various applications like drug discovery, material science, and chemical engineering. Below is categorised list of computable molecular properties:
1. Physical and Chemical Properties
Molecular weight
LogP
2. Geometric and Structural Properties
Radius of gyration
3D molecular shape descriptors
3. Topological Properties
Topological polar surface area (TPSA)
Shannon entropy
If you are looking for tools to compute these properties, some commonly used software includes:
RDKit (cheminformatics)
MOPAC (semi-empirical quantum chemistry)
I want to use RDKit to compute the properties of molecules. Find those computable properties using RDKit. Response:
Message 2:
RDKit provides comprehensive set of functions to compute molecular properties.
Here is a categorized list of computable properties using RDKit:
1. Physical and Chemical Properties
Molecular weight: tikit.Chem..l Www..hhheee...meeeee Descrripoors...aaaMlWi..
LogP: rdkit.Chem. Crippen.MolLog
2. Geometric and Structural Properties
Radius of gyration: rdkit. SakittChem.Deserree
You compute these properties using Python by importing the relevant RDKit modules. For example:
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Table 4: Details of collected molecular properties.
Name
Category
Brief Description
Molecular Weight
Constitutiona
Total mass of a molecule calculated as the sum of atomic weights of all aloms,
providing fundamental information about molocular size and mass distribution.
Haavy Atom Molecular Weight
Sum of atomic weights of all non-bydrogen stoms in the molecule, uscful for comparing molecular frameworks.
Constitutional
Number of Valence Electrons
Total number of electrons in the outer shells of all atoms, cracial for understanding chemical booding and reactivity patterss.
Constitutional
Sum of all formal charges charge state and ionic character.
the molecule, indicating overall molecular
Total Formal Charge
Constitutional
Topulogical Polar Surface Area
surfaces all polar atoms (mainly oxygen nitrogen), correlating with
Topological
drug absorption, including intestinal absorption and blood bartier penetration.
Labute Apprximaate
Topological
Approximate molecular ar surface area calculated using Labute's method, useful for predicting physical properties and molecular interactions.
Surface Area
Topological index based on molecular connectivity, indicating molecular branching and cyelicity. Higher values suggest more branched structures.
Bulahun Index
Topological
Mcasure of molecular complexity considering both size and branching pattems.. Higher values indicate more complex molecular structures.
Bertz Complexit
Topological
Logarithm octanol-water partition cocfficient. prodicting molocular lipophilicity and membrane permeability. Key for drug absorption.
LogP
Electronic
Measure of total polarizability of a molecule, related molecular volume and electronic propenies. Important for predicting optical behavior.
Molar Refractivity
Electronic
Sum of van der Wauls surface areas of atoms with electrotoooogiial state values first range. Relates electronic state to molocular surface.
ESuute VSA1
Electronic
Number of Hydrogen
Count of NH and OH groups capable of donating hydrogen bonds. Critical for predicting molecular interactions and drug- like properties.
Hydrogen bonding
Bond Donces
Total number of nitrogen and exygen atoms with attached bydrogens, indicating potential hydrogen bond donor sites.
NHOH Group Count
Hydrogen bonding
Total count of nitrogen and oxypen atoms, regardless of hydmgen attachment. Indicates overall polar stom content.
NO Group Count
Hydrogen bonding
rings with conjugated pi electron systems exhibiting anomaticity. Important for molecular stahility and interactions.
Number of Aromatic Rings
Ring descriptors
Aliphatic Rings Count
Ring descriptors
Number of non aromatic (saturated or partially saturated) rings in the molecule.
Alfects molecular llexiiliiy and properties.
Number of rings containing at least one heteroatom (nen--carben). Impoetant for
Heterocycles Count
Ring descriptors
biological activity and drug- properties.
Count of single bonds that can freely rotate, excluding those in rings. Key
Number Rotatable Bonds
Molecular llexibility
indicator of molecular dexibility and oral bionvailability.
Fraction Csp3 Carbon Atoms
Ratio of sp3 hybridized carbons total carbon count. Higher values indicate more 3D character and better dmi- like properties.
Molecular ficxibility
Molecular Volume in A
3D descriptors
Three dimensional space occupied by the molecule, calculated using van der Wauls radii. Important for molecular packing and interactions.
Meusure of how much molecular shape deviates from perfect sphere. Higher elongated irregular shupes.
Asphericity
3D deseriptors
values indicate
Ratio of the longest to shortest molecular axis. Indicates degree of molecular clongation 3D space.
Eccentricity
3D descriptons
Average distance of molecular mass from its center of mass. Deseribes molecular size and mass distribution 3D
Radius of Gyration
descriptors
Ratio of principal moments inertia, describing overall 3D shape and mass distribution. Distinguishes rod-like from disk-like melocules.
Inertial Shape Factor
3D descriptors
Quantitative Estimate Drug-likeness, combining multiple molecular
QED
Drug. likeness
propettes into a single score (0- I). Higher values indicate more drug- like characteristics.
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A.2
Auxiliary task selection Prompt
Auxiliary task selection prompt:
(For better visualization, converzation details can be found in our GitHub project.
You are now an expert in computational chemistry and machine learning for molecular property prediction. Your task to provide detailed and accurate information about computable molecular properties.
Instruction:
Message 1: Search available information about these properties and write a brief summary for each property.
Response: Here is brief explanation of each property RDKit can compute:
1. Physical and Chemical Properties
Molecular weight:
Total mass of molecule calculated the sum of atomic weights atoms, providing fundamental information about molecular size and mass distribution.
LogP: Logarithm of octanol-water partition coefficient, predicting molecular lipophilicity and membrane permeability. Key for drug absorption.
2. Geometric and Structural Properties
Radius of gyration: Average distance of molecular mass from its center mass. Describes molecular size and mass distribution in 3D.
3. Topological Properties
Topological polar surface area (TPSA): Sum of surfaces of all polar atoms (mainly oxygen and nitrogen), correlating with drug absorption, including intestinal absorp- tion and blood-brain barrier penetration.
For the primary task [TASK] (TASK DESCRIPTION), you assess the relevance between retrieved properties and the primary task with affinity scores, based domain-specific knowledge and the summarised information. Response:
Message 2:
Message 3: For the primary task [TASK] (TASK DESCRIPTION), you recommend which [K] properties auxiliary tasks to improve the machine learning model performance the primary task. You provide: a list of [K] selected auxiliary tasks and the affinity score of each auxiliary task the primary task [TASK]. Response:...
A.3 Discussion of selected auxiliary tasks
3Jble|[pprssen the auxiliary tasks selected by different LLMs (GPT-4, LLaMA 3, and Gemini 2.0) across each molecular prediction dataset. Across all models, observe consistent selection of chem- ically relevant descriptors such as LogP, Topological Polar Surface Area, and Number of Hydrogen Bond Donors, confirming their general importance for molecular property prediction.
For classification datasets like BBBP and Tox21, 1, all three LLMs prioritize physicochemical and toxicity-relatie related deseriptors. Notably, LogP and TPSA appear in nearly all runs, underscoring their relevance to tasks involving membrane permeability and diug-liseness. In contrast, for regression datasets such as ESOL, FREESOLV, and LIPO, the selected auxiliary tasks increasingly include 3D structural properties like Molecular Volume in 83, Radius of Gyration, Eccentricity, and Asphericity, which crucial for modeling continuous-valle
properties.
Comparing across LLMs, we find that GPT-4 selections are more stable, with strong alignment to domain-relevant properties across all datasets. I.LMAA 3 maintains good coverage of core features, but introduces additional diversity in selections, occasionally replacing QED or TPSA with alternatives like Balaban Index Heavy Atom Molecular Weight. Gemini 2.0 shows
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Table 5: Illustration of selected auxiliary tasks. The numbers in parentheses (k, w) indicate the number of times each task was chosen out of five repeats (k), and its average learned weight (w).
Dataset
X]
LLM
LLM Selected Auxiliary Tusks
Topological Polar Surface Area (5, 0.28), LagP (5, 0.27), Number of Hydregen Bond Danors (5, 0.24).
GPT-4
Molccular Weight (5. 0.21), QBD (5, 0,200
BBBP
5
Topological Folar Surface Area 0.20, LogP (5,2555, Number Hydragen Bond Donors (3, D:21),
Llama 3
Bahaban Index (4, 0.13), NO Group Count (3, 0.11), QED (2, 0.009.
Molecular Weight (1. 0.04)
Topological Polaz Surface Area 0.215, LogP (S, 0,261, Number Hydrogen Bond Doners (5, 0:23)
Gemini 2.0
Molecular Weight (4, 0.13), QED (3. 0.10), Balaban Index (2, 0.07).
Hewvy Atom Molecular Weight (1, 0.08)
GPT-4
LogP (5,
Topological Polar Surface Aren 0.255), Numbher Hydrogen Bond Donors (5, (..22)) Refractivity (5, 0,219) (5, (5)).
BACK
5
Llama 3
Weight (3, 0.15), EState VSAI [2, 0.08), Number of Hydrogen Bond Donors (2, 0.07), QED 0,03)
Log (5. 06, topologiial Polar Surtace Area (5, 0.251, Molar Rerractvvlly (3,0.2), Hewvy Arrin Mulcontar
Gemini 2.0
Molar Refractivvtt (4, 0.15), QED (3. 0.09), Total Formal Charge (1, 0.03), NO Group Count (1. 0.02)
Logl" 0.271, Topological
Surtace Arey 0,26), Number Hydrogen Bond
0.17)
LogP (5. 0.277), Topological Polar Surface Arca (S, 0,242), Number of Hydrogen Bond Donoes (5, 0.243),
GPT-4
QED (3, 0.10), Bertz Complexity (3, 0.150), Fraction Cxp3 Carbon (2, 0.110).
ClinTox
5
Molecular Weight (1, 0.08), Number of Arematic Rings (1.0.09)
LagP 01233, Topological Polar Surface Area (5, 0,231, Beriz Complexity (4, 0.131, NHOH Group Count (1. D. D.TT). Balaban Index 0,10), Number Hydroyon Bond Donors 0.09), QZD 0.09)
Laama
LogP (3, 0.263, Topolngical Polar Surface Area 0.15, QED13,
Gemini 2.0
Bertz Complexity (3. 0.10), Melecular Weight (2, 0.08), Aliphatic Rings Count (2,0.08).
NHOH Group Count (1, 0.04)
LogP (5, 0.23), Topological Polar Surface Area (5, 0.22), Number of Hy Hydragen Bond Donors (5, 0.21).
GPT-4
Renz Complexity (5, 0.19) NO Group Count (5, 0:18)
Tox21
5
LagP 0.21), Topolagical Polar Surface Area (5, 0.201, NO Group Count 0.18%.
Llama
Number of Hydrogen Rond Donors (4, 0.15), ESttte VSAI (3, 0,10) Benzy Complexity (2, 0.07)
Logi 0.22), Number of Hydrogen Bond Donors (5, 0,20), QED 0.12),
Gemini 2.0
Exventricity (2, 0.09), NO
Count (2, 0.09), Fraction Cxp3 Carbon Atomm (2, 0.08), TPSA (1. 0.04)
GPT-4
Number of Hydrogen Bond Donors (4, 0.14), Balabonn J Index (3, 0.09), Number of Rotatable Bonds (3, 0.08)
LogP (5, 0.21), Topological Polar Surface Area (5, 0,20), QED (S, 0.19)
Toxtast
5
Log (3,0.2),, Balahan J Index (4, 0,14), Labute Appreximase Surface Aren (4, 0.13),
Llama
VSAi 0.), Topological Polar Surface
12, 0.07), QED (2. 0.06), Rotatable Bonds (I. 0.03)
Gemini 2.0
LagP (3, 0.11, Iopological Polar Surface Aren (4, d. Balahin J Index (5, D.18),
Rotatablc Bonds (3, 0.08), Labuli Approsimati Surface Area (2, 0.05), QED (2, 0.06), TPSA 01,0.2)
GPT-4
LogP 0.20), Topological Polar Surface Area (5, 0.18), QED (4, 0.12).
Namber of Hydrogen Bond Demors (4, 0.12), Numbher of Rotatable Beeds (4, 0.10), Molocular Weight (3, 0.08)
SIDER
Logl" 0.19, Topological Polar Surface Area (1,0.12) 0.121, Aliphatic Rings Count (4, 0,10),
Iaama
Fraction Csp3 Carhon Atoms 0.08), Number of Hydrogen Bood Donors (2, 0.07), QED 0.06)
Topological Polar Surface Area (5. (5.).19) LogP (5, 0.18), Number of Hydrogen Bond Donors 0,12).
Gemini 2.0
QED (3, 0.99), Molecular Weight (2, 0.06), Aliphatic Rings Count (1,0)
LogP (5, 0.19), Molecular Weight (5. 0.18), Topological Polar Surface Area (5, 0.17),
GPT-4
Molecalar Volume in A (5. O 0.17). QED 0.05). Pmiztion of Cxp3 Curbon Atoms (1, 0.94).
ESOL
s
Radius of Gynation (1, 0.03), Eccentiiity
0.03), Asphericity (1, 0,03)
LogP (3. 0.17), Molccnlar Waight (3. U T6), Topological Polar Surface Area (5, 0.13),
Liana 3
Molccular Volume X (4, 0.100, Excenticiiyy (2, 0,05), Incatial Stape Factor (2. 0.04), QED (103)
LogP (3, 0.161, Molecuaar Waight 0.15), Topological Polar Surface Area (4, 0.10),
Gemini 2.0
Melccular Volume in A² (3. 0,08), Radius of Gyration (2, 0.05), Becentricity (2, 0.05).
Asphericity (2, 0.05), Inertial Shope Factor (1. 0.03). QED 0.03)
LagP (5, 0.19), QED (5, 0.18), Topological Polar Surface Area (4, 0.12).
GPT4
Molecalor Volume A (4, 0.12), Molar Refractivity D.10), Radius of Gynsion (3, (.088
FreeSollv
LogP 0.83. 0.18,, QED 0.12), Topologscal Polar Surfice Arca 0,12,
Llama 3
Molecalar Volume X2 (3, 0.09), Molar Refractivity (3, D.08), Fccentricity (2, 0.05)
LogP (5, 0.17), Mosccular Volume in A" (4, 0.11), QED (3, 0.08).
Gemini 2.0
Topological Polar Surface Area (3. 0.08), Fraction of Csp3 Carbon Atoms (2, 0.06).
Molar Refractivity 0.06), Rodius of Gyration (1. 0.05)
LogP (5, 0.18), Molccular Weight (5. 0.17), Topological Polar Surface Area (1. 0.13), Number of Hydrogen
GPT-4
Bond Donors (4, 0,13), Molar Refractivity (4. 0,12), Fraction of Csp3 Carbon (2, 0.06), Radius of Gyration (1, 0.03)
Lipo
5
(5, Molccnlar Waight U. 10, Molar Reractivel (410.
Llama
Topological Polar Surface Area (3, 0.09), Number Hydrogen Bond Domors (2. 0.06).
Heavy Atom Molccular Weight (2. 0.05), Friction of Capá Carbon Asoms (1, (.05)
Logi' (5,0.15), Molccular Weight (5. 0.15), Molar Retractivity 0.11). Topological Polar Surface Area (3, 0.08).
Gemini 2.0
Number of Hydrogen Bond Donors (2, 0.05), Fraction of Cxp3 Carbon Atoms (2, 0.05), Eccentricity (1. 0.03)
greater variability and less task consistency, particularly in regression settings such as FREESOLV and ESOL, where it selects more diverse and occasionally less relevant 3D shape descriptors. This aligns with the drop in performance observed in Figure 3c
Despite the inherent stochasticity of LLM outputs, the consistency in repeatedly selected descriptors (shown by the frequencies in parentheses) confirms that all models--sppecially GPT-4-are capable of capturing task-relevant molecular knowledge. These results support our claim that L.LM-guided auxiliary task selection is both feasible and chemically meaningful, and also highlight that L.IM quality impacts the robustness of selection.
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Costs of LLM Usages. Our automatic auxiliary-tain task pipeline made 137 calls to GPT-4, two retrieval and three selection requests for each of the target datasets in each of the five training runs. And 135 additional calls to Gemini 2.0 for the ablation in Figure 3c A GPT-4 request needed 21.3 + 2.4 on average, while a Gemini 2.0 request required 7.8 + 1.1 Using the research-tier prices (S 0.06 / K prompt tokens and $ 0.12/ / 1 K completion tokens for GPT-4; USD 0.02 / K prompt tokens and USD 0.02/
completion tokens for Gemini 2.0) and the logged token counts of roughly 1 K prompt 1 K completion tokens per call, the GPT-4 portion cost about $23 and the Gemini portion about $9. The total large-lagguage-mmoee expenditure is therefore approximately S32, or $3.6 per downstream dataset across all five runs. GPT-4 accounts for most of the bill because retrieves and ranks all auxiliary tasks and their textual descriptions; Gemini 2.0 used only to show that less expensive model can replace GPT-4 without measurable drop in accuracy. Llama 3 runs on our GPUs, so incurs no additional cost beyond electricity.
B Proof
B.1 Proof of Theorem
Let (0) E Rd denote the gradient of the primary task loss, and let (VC represent the gradients of the auxiliary task losses. These auxiliary gradients span a subspace & C Rd.
Step 1:
Gradient Representation. Since S is the span of the auxiliary task gradients, any vector S can expressed as linear combination these gradients:
Oak (0) ES
where a = {a1.-... ,x] are the auxiliary task weights. If V (m)()) S, then there exist coefficients {3,} such that:
VL
3 ()).
Step 2: Exact Reconstruction of (0). The primary task gradient can be optimally recon- structed using the auxiliary task gradients if there exist weights {ay} such that:
K
a& (0)
(10)
In this case, the reconstruction error zero:
K VLv Σ az (000 =.
(11)
A=1
This implies that the auxiliary tasks fully capture the primary task gradient, leading to complete alignment.
Step 3: Decomposition with Orthogonal Components. into two components:
If Vf S, it can be decomposed
Lc,(0) vcll( (C)
(12)
where:
(O) E S is the projection of (o) onto S,
VCJ (0) 1 S is the orthogonal component.
The best approximation of Cl)) within S is given by its projection:
K
a ())
())
(13)
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The reconstruction error in this case is:
K
Σ k=
- 0.
(14)
The presence of V (0) implies that the auxiliary task gradients cannot fully reconstruct the primary task gradient, reducing their effectiveness.
Step 4: Implications for Optimization.
When (G) E S, we obtain:
VLL
aA V 0.
(15)
Thus, the auxiliary tasks provide complete gradient information for optimizing the primary task, resulting in:
Improved convergence rates, as optimization follows an aligned gradient trajectory.
Reduced variance, since the primary task gradient is entirely captured by the auxiliary tasks.
Conversely, if V C (0) * O, the orthogonal component introduces misalignment the optimization process, leading to:
Slower convergence, as optimization influenced by gradients that do not fully capture the primary task.
Potential suboptimal solutions, since the primary task is not entirely represented in the auxiliary gradient space.
Conclusion. The auxiliary tasks contribute maximally to primary task optimization when L+) S, ensuring full alignment with the optimization trajectory. When this condition holds, the auxiliary tasks effectively serve as surrogates for the primary task, guiding learning efficiently. However, when V (0) & 0, the mismatch introduces misalignment, emphasizing the need for selecting auxiliary tasks whose gradients best align with the primary task gradient.
B.2 Proof of Theorem
Step 1: Generalization Error Definition. The generalization error (()) the difference between the expected loss on the data distribution and the empirical loss on the training set:
Σ (X.().
{() = Ex,,Y (Coo),,,[[___))))))))))
(16)
where ((-)) the loss function, Dm represents the primary task data distribution, and the number of training samples.
Step 2: Auxiliary Tasks and the Hypothesis Class.
Incorporating auxiliary tasks T, modifies the training objective by introducing a set trainable weights a that dynamically adjust the contribution of auxiliary task losses:
a( (0)
Σ ()).
(17)
This formulation does not change the hypothesis class itself but adjusts the optimization trajectory by guiding parameter updates through gradient alignment.
Step 3: Rademacher Complexity with Dynamic Weighting. measures the expressive capacity of the function class:
The Rademacher complexity R,,
(H!) = E,r sup
oo
(18)
SoCH
23

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Unlike traditional multi-task learning, which may increase R2 (H) by introducing additional objec- tives, your framework does not expand the hypothesis space but rather optimizes within the same space using dynamically weighted gradients.
The key effect of auxiliary tasks in your framework is that it improves gradient alignment, ensuring that updates to 0 are more consistent with the primary task objective. This leads to:
Rr (Ha) S (H),
(19)
where Ha denotes the function class learned with dynamically weighted auxiliary tasks.
Step 4: Generalization Bound.
From statistical learning theory, the generalization error satisfies: E(0) S E()) c- R. (Ha).
(20)
where:
(0) is the empirical enror minimized during training.
R., (Ha) is the Rademacher complexity of the function class trained with auxiliary task reweighting.
is constant dependent the Lipschitz continuity of ((..)).
Since dynamic weighting ensures auxiliary tasks contribute positively through gradient alignment, lowers the effective complexity of the hypothesis class while improving empirical performance.
Conclusion. The auxiliary task rewcighting strategy in AUTAUT improves generalization by enhancing gradient alignment rather than modifying the hypothesis class itself. This leads to better generalization bounds without introducing unnecessary complexity.
C Algorithm
The proposed algorithm, AUTAUT, optimizes the primary task by leveraging auxiliary tasks through a structured three-phase learning procedure. The key idea is to dynamically integrate auxiliary supervision to refine the learning process while ensuring that auxiliary tasks positively contribute to the primary task. To achieve this, AUTAUT systematically retrieves auxiliary tasks, adapts their influence during training, and fine-tunes the model for optimal performance.
AUTAUT begins by retrieving a set of potential auxiliary task labels using pre-trained LLM. This step identifies molecular property prediction tasks that are related the primary task, which are then ranked based on their gradient alignment with the primary task. Only the most relevant auxiliary tasks are retained, and their initial weights are set according their alignment scores. This step ensures that the model does not incorporate noisy or misleading auxiliary signals.
During training, AUTAUT jointly optimizes the primary and auxiliary tasks. The model parameters are updated based on the primary task gradient, combined with weighted gradients from the selected auxiliary tasks. The auxiliary task weights not fixed but rather adapted dynamically using gradient-based Fisher divergence minimization strategy. This allows AUTAUT to progressively emphasize auxiliary tasks that contribute positively while down- weighteggt those that introduce conflicting gradients. The auxiliary task weights are constrained to a probability simplex, ensuring a balanced contribution of tasks.
Once training stabilizes, the final fine-tuning phase optimizes the model exclusively on the primary task while keeping the auxiliary task weights fixed. This prevents auxiliary supervision from dominating the optimization and ensures that the learned model parameters generalize well to unseen data.
The complete training procedure outlined in Algorithm T phases:
The algorithm follows three structured
Retrieval and Initialization, where relevant auxiliary tasks are selected based on LLM retrieval and gradient alignment.
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Algorithm AUTAUT: Optimizing Primary Task with Auxiliary Task Integration
Input: Primary task dataset PIRAN auxiliary task dataset PRAIINS pre-trained LLM fILM
2: Parameters: Learning rates Et. 3: convergence threshold é; maximum epochs E, fine-tuning
epochs Linne
3: Output: Optimized model parameters l*
4: Phase 1: Retrieval and Initialization
5: Query LLM to retrieve potential auxiliary task labels:
6: Ve + fLLMM(PRAIN)
7: Compute gradient alignment scores for tasks in 3/2 w.r.t DTRAIN
8: y. <- -opp-K auxiliary task labels selected based on alignment scores
9: Initialize task weights a proportionally to alignment scores
10: Initialize model parameters d,
11: Phase 2: Joint Training with Adaptive Weighting
12: for epoch e = 1 to E do
13:
for iteration f to 7 do
14:
Compute primary task gradient: 9p +- V log PD (6/-1)
15:
Compute auxiliary task gradients: gk t- log pl (
TRAIN (02-1) Ak
16:
Update model parameters: 0g t ft-1 - c 9p Σ k= (1.9k)
Adaptive Task Weight Optimization if not converged(a) then
18:
Update task weights
Fisher divergence minimization:
a
a
BV V log ((PtaaiN (0.) log Po (0.)
Project onto simplex end if end for
D Ensure oik 2
Ex ag
=
21:
22:
23: Evaluate model on validation set DAALID
24: if validation performance plateaus or shows marginal improvement then
break 27: end for
25:
26: end
28: Phase 3: Fine-Tumme Fine-Tuning
29: for epoch 1 to Fine do
30: Fix auxiliary task weights a
31:
Update model parameters: 0 <- A c ( log -EK, Og V V P(
32:
Evaluate model on validation set D
33:
if validation performance plateaus or shows marginal improvement then break end if
34:
35:
36:
end for
37: Return: Optimized model parameters 0*
2. Joint Training with Adaptive Weighting, where model parameters and auxiliary task weights are updated iteratively using gradient alignment and Fisher divergence minimization.
3. Fine-Tuning, where the model is optimized on the primary task with fixed auxiliary task weights to ensure stable generalization.
By integrating these steps, AUTAUT automatically learns an effective task weighting strategy, reducing the reliance manual task selection while enhancing the predictive performance of the primary task.
D Computational Complexity Analysis
AUTAUT consists of three phases: retrieval and initialization, joint training with adaptive weighting, and fine-tuning. The computational complexity primarily determined by the auxiliary task selection process, the gradient-based joint training, and the adaptive weight optimization.
25

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D.1 Phase 1: Retrieval and Initialization
Retrieving auxiliary tasks using an LLM query has a complexity of ((1) the LLM response time is independent of dataset size. However, if retrieval involves searching database of N₂ potential auxiliary tasks, the complexity ((N.). Computing gradient alignment for auxiliary task selection requires evaluating the cosine similarity between the primary task gradient and each auxiliary task gradient, which results in a cost of (NND), where D is the number of model parameters. Selecting the top K auxiliary tasks incurs a sorting cost of (N log N.). Thus, the total complexity for retrieval and initialization is:
O(N,D + N₂ log
(21)
D.2 Phase 2: Joint Training with Adaptive Weighting
Each training iteration involves computing gradients for both the primary and auxiliary tasks, requiring O(D) operations per task. Since there are K selected auxiliary tasks, the total cost per iteration O((K + 1)) Updating model parameters using stochastic gradient descent (SGD) incurs an additional O(D) cost per step. Computing gradient alignment scores requires dot products between the primary task gradient and K auxiliary gradients, adding an extra ((K per iteration. Updating auxiliary task weights via Fisher divergence minimization requires computing weight gradients and projecting onto simplex, which contributes another O(K Over E epochs with iterations per epoch, the total complexity for joint training is:
O(ET(KD+ + D + KD)) O(ETKD).
(22)
D.3 Phase 3: Fine- Fine-Tuning
Fine-tuning updates only the primary task parameters with fixed auxiliary task weights, resulting in a per-iteration complexity of O(D). Over Eine fine-tuning epochs, the total complexity is:
OLEnnT
(23)
D.4 Overall Complexity
Summing the complexity of all phases, the total computational cost of AUTAUT is:
O(N log + O(ETKD) O
(24)
If Eúne E, this simplifies to:
O(N Na log No + FTKD).
(25)
D.5 Comparative Analysis
For comparison, standard single-task learning has complexity OEETD), while traditional multi- task learning with fixed auxiliary tasks has complexity OEFKKD) but lacks dynamic weighting. AUTAUT introduces additional costs from auxiliary task retrieval and weight adaptation but avoids the need for manual task selection.
D.6 Scalability Considerations
If K < D (i.e., a small number of auxiliary tasks), AUTAUT remains comparable in complexity to standard multi-task learning. If No is large, auxiliary task selection may be costly, but it is one-time process and does not impact training iterations. Since gradient-based weight updates scale linearly with K, AUTAUT remains computationally feasible for moderate values of K.
In Table 6 we report the number of model parameters, GPU memory usage, and average execution time of ML models with and without AUTAUT across 5 runs. In practice, the additional computational burden from auxiliary task integration is minimal.
Overall, AUTAUT achieves a balance between improved learning efficiency and computational cost, making it scalable approach for molecular property prediction tasks.
26

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Table 6: Training information.
Model
Dataset
#Poram
GPL Mem (MB)
Time (min) Baseline
Time (min) +AUTAUT
BACE
296,813
534
2.57
2.47
BBBP
296,833
536
3.14
3.25
ClinTox
Tox21
298.252
532
12.17
12.64
GCN
ToxCasi
576.297
538
18.75
19.44
SIDER
300.167
562
2.42
2.50
ESOL
FreeSaly
296,833
506
1.00
1.04
Lipo
296,833
516
6.44
6.68
BBBP
196,005
3.19
Tox
496.134
548
2.28
2.36
Tox21
197,424
512
12.00
12:47
ToxCast
SIDER
499,359
570
2.41
2.53
ESOL
496,001
518
1.76
1.8%
FreeSolv
496,005
1.03
Lipo
496,005
BACE
2,9441100
10,957
10.59
11.12
BRBP
7.944.600
10,997
14.48
15.07
7946,740
Tox21
7,972,540)
10,808
59:31
66.55
Graphormer
ToxCast
9,533,440
10,998
92.52
94.28
SIDER
PSOL
7,9441160
10:417
FracSolv
7,944,160
10,338
3.90
4.05
Lipo
7,944,160
11,018
31.02
32.50
E
Related Work Discussion
Auxiliary task selection related work:
1. Beam Search 37 Beam Search is task selection method that incrementally builds set of auxiliary tasks by scoring candidates based on intermediate performance gains. It selects the top-scoring sequence of tasks using beam width exploration. Important settings: It adopts all collected auxiliary tasks as shown in Table Selection is performed by evaluating model performance on the validation at each expansion step.
2. TAG 8. TAG (Task Affinity Grouping) clusters tasks by computing pairwise gradient similarities and forming groups to minimize negative interference. It selects task groups with high internal compatibility.
Important settings: It uses all collected auxiliary tasks shown in Table 4 and forms group that includes the primary task. Task affinity computed via cosine similarity of gradient vectors.
3. Task2vec M. Task2vec represents tasks embedding vectors derived from the Fisher Information Matrix and selects auxiliary tasks based on embedding proximity. Important settings: It adopts all collected auxiliary tasks as shown Table and uses em- bedding similarity to rank and select top-K tasks closest to the primary task in representation space.
4. MolGroup I7
MolGroup groups tasks by jointly optimizing group assignments and shared representations using a bi-level optimization framework. Important settings: It adopts all collected auxiliary tasks as shown Table 4 and learns task groupings end-to-end during training.
5. GS-Meta [68] GS-Meta (Graph Sampling- based Meta-Learning) learns task-specific
initializations using meta-learning, adapting quickly to new tasks with few updates. Important settings: We adopt the auxiliary datasets and molecular property relation graph provided in their official implementation.
6. InstructMol 51 InstructMol leverages instruction-based supervision by encoding auxil- iary task semantics through natural language prompts during pretraining. Important settings: We adopt the auxiliary datasets provided in their official implementation.
Adaptive multi-tase task learning models:
27

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1. MITNNN 225
MTDNN (Multi tultii-task Deep Neural Network) trains on all tasks simultane- ously and learns to balance them via shared backbone and task discriminator. Important settings: It uses the same number of randomly selected auxiliary task set as AUTAUT (as shown in Table The task discriminator is trained to identify relevant tasks dynamically during training.
2. Unweighted Averages (UA). UA is a naive baseline where all auxiliary task losses are included with equal weight. It assumes uniform contribution from each auxiliary task. Important settings: It uses the same number of randomly selected auxiliary task set as AUTAUT (as shown in Table but without adaptive weighting.
3. GradNorm [3. GradNorm adjusts task-specific learning rates to balance gradient magni- tudes across tasks, aiming for balanced training dynamics.
Important settings: It uses the same number of randomly selected auxiliary task set as AUTAUT (as shown in Table 3) and dynamically reweights task losses by normalizing their gradient norms.
4. Pretrain-Finetune (PF) 15]. PF is a two-stage pipeline that first pretrains the model on a large source dataset and then fine-tunes it on the target task without auxiliary tasks. Important settings: The model is pretrained on PCQM4Mv2 [4] and then fine-tuned on each downstream dataset individually.
Discussion. Table summarizes the key differences between AUTAUT and prior auxiliary-task based methods. Most existing approaches, including TAG, TASK2VEC, GRADNORM, and MTDNN, rely on manually curated auxiliary tasks or require detailed task descriptions and additional datasets. While some methods (e.g., GS-META, MOLGROUP) support automatic task selection adaptive weighting, they still depend heavily on external supervision or relation graphs to guide selection.
In contrast, AUTAUT is the only method that achieves both automatic auxiliary task retrieval and automatic task selection without requiring any numan-generatie task lists, labels, or external datasets. Furthermore, it uniquely integrates selected tasks using gradient alignment-based adaptive weighting mechanism, which dynamically adjusts the importance of each auxiliary task during training. This combination enables ACTAUT to operate entirely without manual input domain knowledge, setting it apart as the first fully automated framework for auxiliary task discovery and integration in molecular property prediction.
This design significantly lowers the barrier for deploying auxiliary-task--bat learning in domains where expert-curated auxiliary signals are unavailable or difficult to define, highlighting AUTAUT's scalability and practical utility.
F Experimental Settings
We conduct experiments on the MoleculeNet benchmark (52), a widely used dataset collection designed evaluate molecular property prediction models. MoleculeNet includes a diverse range of tasks spanning physicochemical properties, bioactivity, toxicity, and side effects, providing comprehensive testbed for assessing the effectiveness of our algorithm. For our evaluation, we select 9 representative datasets covering classification and regression tasks, described as follows:
Datasets.
1. BBBP. The Glood-Brain Barrier Penetration (BBBP) dataset contains binary classification labels indicating whether a molecule can permeate the blood-brai barrier. This property is crucial for designing central nervous system drugs and understanding their pharmacokinetics.
2. BACE, The BACE dataset provides both quantitative (IC50 values) and qualitative (binary classification) binding data for inhibitors of human 3-secretase (BACE-1) 1), a key target for Alzheimer's ' disease treatment.
3. CLINTOX. The ClinToo dataset compares FDA-approved drugs with those that failed clinical trials due to toxicity. It presents binary classification challenge for identifying toxic compounds early in drug discovery.
4. Tox21. The "Toxicology in the 21st Century" (Tox21) initiative compiles a dataset of qualitative toxicity measurements for 12 biological targets, including nuclear receptors and stress response pathways. serves as a multi-label classification problem for assessing compound toxicity.
28

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5. TOXCAST. An extension of Tox21, the ToxCast dataset contains in vitro high-throughput screening data for thousands of environmental and pharmaceutical compounds, spanning over 600 toxicity-related assays.
6. SIDER. The Side Effect Resource (SIDER) dataset aggregates information on adverse drug reactions (ADRs) for marketed drugs. The dataset categorizes side effects into 27 system organ classes, making it multi-label classification task.
7. ESOL. The ESOL dataset comprises solubility measurements (log solubility in mol/L) for small organic molecules in water. Predicting solubility is a fundamental task in drug formulation and molecular design.
8. FREESOLV. The FreeSolv dataset provides both experimental and calculated hydration free energy values for small molecules in water. This dataset is valuable for studying solvation effects and molecular interactions in aqueous environments.
9. LIPO. Lipophilicity, quantified by the octanol/water distribution coefficient (logD at pH 7.4), is a critical property influencing drug absorption, membrane permeability, and solubility. This dataset provides experimentally measured logl values for a variety of drug-like compounds.
Data Split. To ensure rigorous evaluation, we follow prior work 66 and adopt scaffold splitting to divide datasets into training, validation, and test sets with an 80%-10%- 10% ratio. Unlike random splitting, scaffold splitting in different subsets do not share structural scaffolds [65]. This setup better simulates real-world scenarios where models must generalize structurally novel compounds [52]. Zhou et al. [65] also highlight that chirality considerations in RDKit scaffold generation significantly impact the resulting partitions. For fair comparisons, we select the checkpoint with the best validation loss for final evaluation and report the corresponding test set performance.
more challenging and realistic partitioning strategy, molecules
Auxiliary Task-Based ML Models. We compare AUTAUT against three categories of auxiliary task-based learning approaches:
1. (i) Search-based methods: Beam search 37 iteratively explores auxiliary task candidates based on scoring criterion, training the model for few epochs at each step to improve efficiency.
2. (ii) Grouping-hasee based methods: TAG 8] Task2vec []] and MolGroup 17
employ gradient- based strategies compute pairwise affinities and group similar tasks for joint training.
3. (iti) Train-on-all all approaches: Methods such as Unweighted Averages (UA), GradNorm [3] and MTDNN 25 train models on all available tasks without explicit selection. MTDNN further applies a task discriminator to select relevant tasks dynamically.
Additionally, we evaluate the Pretrain-Fineture Finetune (PF) strategy 11 where models are first pretrained on PCQM4Mv2 [14] and then fine-tuned on downstream tasks. Appendix E provides a comprehensive discussion of these competing methods.
General ML Models. To assess the robustness and effectiveness of AUTAUT, we benchmark against 18 state-of-the-att molecular property prediction models, spanning GNN-based Transformer- based, and self-supervised learning approaches:
GNN-based models:
1. GCN [23]: Graph Convolutional Networks use spectral graph convolutions to aggregate local neighborhood information and are among the earliest GNN architectures applied to molecular graphs.
2. GIN [55]: Graph Isomorphism Networks are designed to be powerful the graph isomorphism test, using sum aggregators to distinguish graph structures.
Weisfeiler-
3. D-MPNN 558 : Directed Message Passing Neural Networks pass messages along
directed edges, better capturing bond directionality in molecular graphs.
4. MGCN 34]: Multi-level Graph Convolutional Networks incorporate quantum interac- tions between atoms using multiple levels of molecular representations.
Transformer-based models:
29

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1. GRAPHORMER 660]: A graph Transformer architecture that encodes both structural and positional information using attention mechanisms tailored for molecular graphs ATTENTIONFP S44 : Attentive Fingerprint uses attention to weight atom features for
molecular representations, capturing both local and global chemical context.
Other molecular prediction models:
1. N-GRAM 31 Represents molecules as bag-of- substructtee n-gram graphs, enabling unsupervised molecular representation without requiring large labeled datasets.
PRETRAINGNN [5]: Pre-training GNNs on large molecular datasets using node and edge prediction tasks, followed by fine-tuning on downstream tasks.
GPT-GNN [6]: A generative pre-training approach for GNNs using node sequence prediction to capture graph-level semantics in a self-superrviee fashion.
4. GROVERBASE [39]: Uses self-supervised Transformer encoder pre- preitrined on mil- lions of molecules with atom-level and graph-level objectives.
5. GROVERLAKGES A larger variant of GROVER with more parameters and deeper architecture, achieving stronger performance on data-rich tasks.
3D-INFOMAX [3]: Learns joint representations molecular graphs and their 3D
structures via mutual information maximization across views.
7. GRAPHMVP 3 A multi-view pre-training method that integrates 2D and 3D
molecular views using contrastive learning and cross-modal prediction.
8. MOLCLR 47: Uses contrastive learning over augmented molecular graphs to learn invariant and transferable molecular representations.
9. UNI-MOL 65: universal framework for 3D molecular pretraining that models atomic coordinates and predicts spatial properties using SE(3)-eqiivariat layers.
10. GEM 6 EeoomtrryEnnannee Models integrate both molecular topology and geomet- ric structure to capture spatial inductive biases.
11. PIN-TUNING 30] Introduces parameter--effiient tuning strategy for few-shot molec-
ular property prediction using prompt-injected features.
12. LAC [56]: Learns from surriculum-based training, progressively increasing task complexity to stabilize learning and improve generalization in molecular prediction.
For fair comparison, AUTAUT adopts GEM as the base molecular property prediction model, ensuring that any improvements stem from auxiliary task selection rather than model architecture.
conducted on 8 NVIDIA A100 GPUs. We use the Adam optimizer [22] with a weight decay of 1e-16 for all models. ReduceLL schedule is applied with patience of 10 epochs to dynamically adjust the learning rate. For baseline auxiliary selection methods, we adopt the official reimplementations provided by Huang al. [17] For AUTAUT,, fix the number of selected auxiliary tasks at K 5, with detailed ablation study on the impact of K in Section 4.4 Our code and data are available at https htwp:t/b.co//aiiiiiaagggngg////u
Implementation Details. All experiments
G Additional Results
To better understand the impact of how auxiliary molecular information is integrated, we conduct a controlled experiment comparing three configurations: (1) a baseline model trained only on the primary task; (2) model that appends selected molecular descriptors (e.g., LogP, TPSA) static input features (+Feature); and (3) our full AUTAUT, which treats these descriptors as auxiliary prediction tasks with adaptive weighting. All three configurations use the same primary datasets and backbone models (GCN, GIN, and GRAPHORMER).
As shown in Table 7 adding descriptors as input features leads to marginal or inconsistent improve- ments. In some cases, performance even drops slightly- instance, on GCN for BBBP, ROC-AUC decreases from 63.45% to 63. 63.39%. This suggests that static features may introduce redundant or weakly aligned information that does not reliably enhance the main learning objective.
http::///ggocctoooooooooo/ollole optim. Ir ir_scheduler. ReduceLL@OmPLateau.
30

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Table 7: Performance of ML models using selected auxiliary labels tasks features. For classification tasks, we calculate the ROC-AUC, while for regression tasks, we use RMSE as the evaluation metric. The number in the bracket is the standard deviation of 5 runs.
CLASSIFICATION (ROC-AUC T)
RECRESSION (RMSE J)
DATASETS
ВВBP 2,039
BACE 1,513
CLINTOX 1,478
Tox21 7,831
TOxCAST
SIDER 1,427
ESOL 1,128
FREESOLY
LIPO 4,200
MOLUCULES
8,575
642
# TASKS
12
617
27
GCN
63. 23.45 go.00
24.83
56.28gmm
74.63,005
40.000
24
2.165
4.752
1. 1.672amm
+ FEATURE
63.39 63.39
74.91
56.21
74.58.045
65. 65.42.0.1s
62. 62.30p
3.154a0000
3.765
1 1.699
+AUTAUT
69.7228.01
83.10
81.00anm1
77.55a0m
68.05
64.622o.2
1.855
1.975
1.285
GIN
66. 82
27.45-0m1
16.18au1s
75.32a0.17
62.35 62.35auce
60 an
3.865 29
1 6.622nmm
g2.00
77
36.982014
75.8220117
85g0.s
75
1.1922001
+AUTAUT
83.62gm.m
82.52gno1
78.322001
38
08ga.s
1.8
1.900go.com
2700
GRAPBORMER
80.04
19.18-...1
18.42a0.13
75.55
124..05
4o.a1
4.795
1.282
+FEATURE
67 v5.00
79.38-6.11
78.62an.1
75.75
67.32.0.25
28.
2.302
1.995 s0.000
1 1.822nmm
+AUTAUT
70.32ss.00
83.72z0.0z
82.55
78.5230m1
69. 38 38..111
71
55..
1.772p0.005 1887230925
2622smmo
In contrast, modeling the same descriptors as auxiliary tasks with adaptive weighting yields substantial and consistent gains. Across all datasets and architectures, AUTAUT significantly outperforms both the baseline and the +Feature variant. For example, on GIN, AUTAUT reduces RMSE on FREESOLV from 3.865 1.900, and improves ROC-AUČ on BACE from 77.45% 83.62%. These results highlight that auxiliary tasks provide richer supervision by contributing task-specific gradients that guide optimization more effectively than raw inputs.
This comparison underscores a central design insight: how auxiliary information is incorporated matters as much as what information is used. Static features are passively encoded and do not benefit from task-level supervision or relevance modulation. In contrast, our formulation enables the model learn explicit auxiliary objectives and dynamically adjust their influence during training through gradient-based alignment. This not only improves convergence but also reduces the risk of negative transfer from irrelevant signals.
Together, these findings validate the importance of treating auxiliary descriptors as supervised tashs-rather than static ihen designing molecular property prediction models.
31


Reviews:
[{"review_id": "1Dm4SgdUwR", "reviewer": "Reviewer_mprS", "summary": "This paper proposes an automatic framework for auxiliary task selection in molecular property prediction. The method aims to automatically identify the top k auxiliary tasks that are related to a given primary task in order to improve its performance, and then apply them during training to assist the learning of the primary task. An adaptive weighting scheme is used to assign different weights to the auxiliary tasks based on their gradient alignment with the primary task. The authors evaluate their method on several molecular property datasets and show performance gains over various baselines.", "questions": "See above.", "limitations": "See above.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "The paper is generally well written and easy to follow. The method is clearly described, and the implementation details are sufficient to support reproducibility. The tables and results are presented clearly, and the comparisons across different model backbones make the findings convincing.\n\nWeaknesses:\n- It is not entirely clear how the authors validate that the selected auxiliary tasks are truly meaningful beyond their quantitative effect. For example, when presenting a generated summary of the auxiliary-primary task relationship, it is unclear how one can assess whether the generated summary is accurate or potentially hallucinated. \n\n- The size and content of the auxiliary task pool could be better specified. It is important to understand how many auxiliary labels are available in each dataset and what their sources are.\n\n- Since the method involves using LLMs and potentially expensive search over tasks, some discussion of computational cost would be valuable. How long does the search take? How much GPU time or memory is needed? How much does it cost?\n\n- The results, while consistent, show only modest gains. Given the complexity of the approach and the use of large models, a deeper analysis of when and why auxiliary tasks provide significant benefit would help strengthen the impact.\n\n- It would be helpful to include more qualitative or quantitative analysis of the selected auxiliary tasks. For example, do the selected tasks correspond to chemically meaningful groupings? Do they vary across different primary tasks?", "was_revised": true, "final_justification": "This work points to a meaningful direction for leveraging and exploring LLMs in property prediction tasks. The experiments presented in both the paper and the rebuttal are extensive and could offer useful insights to the community. Although there are some discussions regarding the validity of the selected tasks, this remains open for further exploration, as automatic auxiliary task selection is a relatively new topic. Overall, I remain positive about the contributions.", "rebuttal": "We sincerely thank Reviewer mprS for the thoughtful comments and valuable feedback on our submission. We appreciate the recognition of our method’s clarity, reproducibility, and comprehensive evaluation across models and datasets. Below, we address the main questions and remain open to providing further clarifications if needed.\n\n> It is not entirely clear how the authors validate that the selected auxiliary tasks are truly meaningful beyond their quantitative effect. \n\nTo validate that the auxiliary tasks selected by AUTAUT are chemically meaningful, we conducted qualitative analyses based on domain knowledge and established chemical literature. As detailed in Section 4.4 (line 254-262), Appendix A.3 (line 530-551), and Table 5, repeatedly selected tasks such as LogP, Topological Polar Surface Area (TPSA), and Hydrogen Bond Donors align strongly with recognized chemical principles for predicting molecular properties. \n\nTo further reduce the risk of hallucinated or inconsistent outputs, we set the LLM temperature to 0.2 (Section 4.4), which promotes stable and reproducible task selection. Across multiple runs and diverse primary tasks, the selected auxiliary tasks remained both stable and domain-relevant, supporting the robustness of our method.\n\nHowever, such manual assessments are not practical at scale, given the limited availability of domain expertise in many real-world applications. This limitation is a central motivation for our work - to provide an automatic and scalable solution for auxiliary task selection.\n\n> The size and content of the auxiliary task pool could be better specified. \n\nOur auxiliary task pool, referenced in Section 3.3, line 145 and described in Table 4, Appendix A, comprises 25 computable molecular descriptors, all retrieved from the internet and filtered using RDKit [1] for benchmarking and reproducibility. The descriptors cover a broad range of chemical property categories, including constitutional, topological, electronic, hydrogen bonding, ring descriptors, molecular flexibility, 3D descriptors, and drug-likeness. Importantly, none of these auxiliary labels are present in the original benchmark datasets (see Section 4.1). \n\nTo further improve clarity, we will explicitly state the total number and source of auxiliary tasks in Section 3.3 and add more direct cross-references to Table 4 in the revised manuscript.\n\n> Some discussion of computational cost would be valuable.\n\nWe agree that understanding computational costs is essential. The overall cost consists of two components: (1) LLM usage for auxiliary task selection and the subsequent (2) model training.\n\n**LLM usage:** The LLM-based retrieval and selection steps incurred approximately 21.3 seconds per GPT-4 request and 7.8 seconds per Gemini 2.0 request, resulting in a total expense of approximately \\$32 across all five training runs (details in Appendix A.3). Search time is affected by the  API traffic situation. Llama 3 instead runs on our GPUs, so it incurs no additional cost beyond electricity.\n\n**Model training:** We provide an analysis of computational complexity in Appendix D. To summarize: during training, the adaptive weighting adds <5% computational overhead due to gradient alignment computation, as it is amortized across mini-batches. All experiments were conducted on 8 NVIDIA A100 GPUs. The GPU time and memory requirements vary across datasets and models. \n\nBelow, we report the number of model parameters, GPU memory usage, and average execution time of ML models with and without AUTAUT across 5 runs. In practice, the additional computational burden from auxiliary task integration is minimal. We will add this table and further clarify these points in Appendix D of the next revision.\n\n| Model | Dataset | #Params | GPU Mem (MB) | Time (min) Baseline | Time (min) +AUTAUT |\n| ---------- | -------- | --------- | ------------ | ------------------- | ------------------ |\n| GCN | BACE| 296,833| 534 | 2.37 | 2.47 |\n| | BBBP | 296,833 | 536 | 3.14 | 3.23 |\n| | ClinTox | 296,962 | 536 | 2.30 | 2.37 |\n| | Tox21 | 298,252 | 532 | 12.17 | 12.64 |\n| | ToxCast | 376,297 | 538 | 18.75 | 19.44 |\n| | SIDER | 300,187 | 562 | 2.42 | 2.50 |\n| | ESOL | 296,833 | 508 | 1.77 | 1.84 |\n| | FreeSolv | 296,833 | 506 | 1.00 | 1.04 |\n| | Lipo | 296,833 | 536 | 6.44 | 6.68 |\n| GIN | BACE | 496,005 | 546 | 2.31 | 2.39 |\n| | BBBP | 496,005 | 548 | 3.08 | 3.19 |\n| | ClinTox | 496,134 | 548 | 2.28 | 2.36 |\n| | Tox21 | 497,424 | 542 | 12.00 | 12.47 |\n| | ToxCast | 575,469 | 546 | 18.69 | 19.34 |\n| | SIDER | 499,359 | 570 | 2.41 | 2.53 |\n| | ESOL | 496,005 | 518 | 1.76 | 1.83 |\n| | FreeSolv | 496,005 | 512 | 0.99 | 1.03 |\n| | Lipo | 496,005 | 550 | 6.38 | 6.64 |\n| Graphormer | BACE | 7,944,160 | 10,957 | 10.59 | 11.12 |\n| | BBBP | 7,944,160 | 10,997 | 14.48 | 15.07 |\n| | ClinTox | 7,946,740 | 10,997 | 10.45 | 10.88 |\n|| Tox21 | 7,972,540 | 10,898 | 59.31 | 60.56 |\n|| ToxCast | 9,533,440 | 10,998 | 92.52 | 94.28 |\n| | SIDER | 8,011,240 | 11,478 | 11.02 | 11.57 |\n| | ESOL | 7,944,160 | 10,417 | 7.81 | 7.96 |\n| | FreeSolv | 7,944,160 | 10,338 | 3.90 | 4.05 |\n| | Lipo | 7,944,160 | 11,018 | 31.02 | 32.50 |\n\n> a deeper analysis of when and why auxiliary tasks provide significant benefit would help strengthen the impact.\n\nWe emphasize that these gains are consistent across all nine datasets, three backbone architectures, and multiple LLM variants (see Tables 2 and 3, and Figure 3c). Importantly, in regression tasks such as ESOL and FreeSolv, AUTAUT achieves RMSE reductions up to 47% in some settings.\n\nOur ablation studies (Figures 2 and 3a,b) indicate that both auxiliary task selection and adaptive weighting are essential for stable improvements. Gains are most pronounced in scenarios where the primary task is underdetermined (e.g., limited training data or noisy labels) and when auxiliary tasks exhibit strong gradient alignment with the main objective. In particular, regression tasks with sparse or complex labels benefit more from targeted auxiliary supervision. This is because the adaptive weighting mechanism emphasizes auxiliary tasks that provide non-redundant, mechanistically relevant signals and filters out noisy or less relevant gradients, resulting in faster convergence and lower training loss.\n\nWe will incorporate this discussion in Section 4.4..\n\n> include more qualitative or quantitative analysis of the selected auxiliary tasks. \n\nOur analysis confirms that the selected auxiliary tasks consistently form chemically meaningful groupings tailored to each primary task. For example, polarity-related descriptors such as LogP and TPSA are repeatedly prioritized for solubility prediction, while toxicity and drug-likeness descriptors (e.g., Hydrogen Bond Donors, QED) are emphasized for toxicity-related endpoints. These patterns are highly stable across multiple runs and clearly reflect underlying chemical knowledge.\n\nAs demonstrated in Section 4.4 and Appendix A (Table 5), the selected auxiliary tasks show strong domain alignment: for instance, descriptors related to molecular polarity (e.g., TPSA, Dipole Moment) are preferred for solubility tasks, while LogP and QED are frequently selected for toxicity prediction. The groupings also vary appropriately across different primary tasks, highlighting the adaptability of our approach.\n\n[1] RDKit: Open-source cheminformatics."}, {"review_id": "Jq0f6KQVnG", "reviewer": "Reviewer_eSg9", "summary": "This paper aims to solve a critical problem in molecule properties prediction, where selecting effective auxiliary learning tasks are critical but challenging. To address this, this paper introduces Automatic Auxiliary Task Selection (AUTAUT), a fully automated framework that first retrieves auxiliary tasks using large language models and adaptively integrates them through a gradient alignment weighting mechanism. This paper evaluates AUTAUT on 9 molecular property prediction datasets, demonstrating its superiority over 10 auxiliary task-based methods and 18 state-of-the-art property prediction models.", "questions": "Some subsets in MoleculeNet are skipped, such as HIV and MUV datasets. What is the reason for this?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n(1) This paper is well motivated by addressing an important problem in molecular property prediction on auxiliary learning tasks selection. The proposed workflow of LLM based searching and gradient based weighting mechanism are simple yet make sense to me. This paper shed light on a potential new workflow on machine learning and LLM based molecule property prediction.\n\n(2) Results on MoleculeNet demonstrate the effectiveness of AUTAUT as it outperformed 18 state-of-the-art property prediction models.\n\n**Weaknesses**\n\n(1) My concerns for this paper are mainly on evaluation settings. First, AUTAUT can be regarded as a pretrained molecular model, as it is actually extracting information from GPT-4. This makes the comparison to lots of baselines in this paper unfair, especially those GNN-like methods that are trained from scratch. Some baselines, such as InstructMol, have leveraged large-scale molecular data. But there should be more baseline methods of its kind, such as “*Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language*” in ICML. A literature review on these large-scale pretrained molecular models is necessary.\n\n(2) The evaluation of AUTAUT is solely based on the MoleculeNet benchmark, making the evaluation very limited. This is a famous yet relatively old benchmark, so it is possible that GPT-4 has information on even the test set of this benchmark. Although the LLM is only doing task selection in AUTAUT, it still could lead to potential information leakage. It will be more convincing to evaluate this workflow on more recent high quality molecular datasets.", "was_revised": true, "final_justification": "I maintain my positive score after author response. It addresses most of my concerns, with only concerns on more baselines and datasets unresolved, which cannot be finished due to the rebuttal time limits.", "rebuttal": "We sincerely thank Reviewer eSg9 for the thoughtful and insightful comments, which have significantly helped clarify our contributions and strengthen the evaluation rigor of our paper. Below we systematically address the concerns and questions raised by the reviewer and remain open to providing further clarifications if needed.\n\n> AUTAUT can be regarded as a pretrained molecular model, as it is actually extracting information from GPT-4. \n\nWe fully agree that methods such as [1] are relevant. Indeed, our experiments already include a wide range of pretraining-based baselines, including PretrainGNN, GPT-GNN, GROVER, 3D-INFOMAX, GraphMVP, Uni-Mol, GEM, Pin-Tuning, LAC, and InstructMol (see Tables 2 and 3 in the manuscript). We will include further results with [1], though it may not be feasible to report these before the rebuttal deadline. In the revised manuscript, we will expand Section 2 to include a dedicated discussion of these large-scale pretrained molecular models and more clearly distinguish their training strategies from the retrieval-only, joint-training approach used in AUTAUT.\n\nWe also briefly clarify that AUTAUT itself does **not** perform pretraining. As illustrated in Figure 1, the base ML predictive models (including GCN, GIN, and Graphformer) are always trained from scratch on the target dataset, just like most baselines in Table 2. The automatically selected auxiliary tasks serve as additional sources of supervision in a joint training process, not in a separate pretraining stage.\n\n> The evaluation of AUTAUT is solely based on the MoleculeNet benchmark,\n\nWe selected MoleculeNet as our primary benchmark due to its broad adoption and comprehensive coverage of molecular property prediction tasks [1,2,3], which enables rigorous and standardized comparison with a wide range of prior work.\n\n> Although the LLM is only doing task selection in AUTAUT, it still could lead to potential information leakage.\n\nLLMs in AUTAUT are **never** exposed to any dataset description, data splits, or molecular features from the benchmarks. The LLMs are used exclusively to suggest and prioritize types of auxiliary tasks (e.g., “Molecular Weight”, “LogP”) based on the name of the primary prediction task. At no point are they exposed to, or queried with, dataset content, actual molecules, or their measured properties. The downstream predictive models are always trained and evaluated strictly following standard train/validation/test splits, ensuring that test molecules remain entirely unseen during both LLM prompting and model training.\n\n> Some subsets in MoleculeNet, like HIV and MUV, were skipped. \n\nOur dataset selection followed recent relevant work [3], focusing on the most commonly evaluated MoleculeNet benchmarks for fair and standardized comparison. Consequently, we adopted the same set of datasets as [3], which excludes HIV and MUV.\n\nRegarding these two datasets:\n- HIV: This dataset suffers from severe class imbalance and noisy labels, which complicate the application of auxiliary supervision and often result in unstable or non-representative outcomes. For these reasons, several recent works (e.g., MolGroup [2], InstructMol [3]) also omit HIV from their evaluations.\n- MUV: The MUV dataset is highly sparse, with very few positive samples per task. This makes it particularly challenging for both single- and multi-task learning frameworks, and recent literature on auxiliary task selection or molecular property prediction rarely reports results on MUV.\n\nNevertheless, to address the reviewer’s concern, we have conducted supplementary experiments on both HIV and MUV. The results, summarized below, show that AUTAUT, even in these problematic datasets, achieves better results.\n\n| Model | Dataset | Performance (ROC-AUC) Baseline | Performance (ROC-AUC) +AUTAUT |\n| ---------- | -------- | --------- | ------------ |\n| GCN | HIV| 71.58 | 75.64 |\n| | HUV| 69.64 | 71.15 |\n| GIN | HIV| 78.23 | 78.43 |\n| | HUV| 71.03 | 71.61 |\n| Graphformer | HIV| 77.80 | 78.16 |\n| | HUV| 69.26 | 70.96 |\n\nWe will include this table and additional analysis in Appendix G of the revised manuscript.\n\n[1] Enhancing Activity Prediction Models in Drug Discovery with the Ability to Understand Human Language, ICML, 2023.  \n[2] Learning to Group Auxiliary Datasets for Molecule, NeurIPS, 2023.  \n[3] Instructor-inspired Machine Learning for Robust Molecular Property Prediction, NeurIPS, 2024."}, {"review_id": "Kz60sROh4S", "reviewer": "Reviewer_8GVh", "summary": "The article presents AutAuT, an automation framework for improving the prediction of molecular properties. AutAuT uses large language models (LLM) for extraction, selection and dynamic integration of auxiliary tasks. The key contribution is full automation of the process (from task extraction to adaptive weighting of their contribution) and gradient alignment using Fischer minimization to reduce the negative impact of irrelevant tasks. Experiments on 9 datasets showed superiority over 10 methods based on auxiliary tasks and 18 state-of-the-art models.", "questions": "1. Due to the absence of certain files and directories (`data/ogbg_molbace/mapping/mol.csv.gz`, also, due to the absence of the `generate_description`, `generate_compressor_message`, `query_chatgpt` sections in the code directory and dataset in the shared directory), the following commands are not working:\n\n- `python -m code.generate_description base_demo_test`\n- `python -m code.generate_compressor_message base_demo_test`\n- `python -m code.query_chatgpt base ogbg_molbace`\n- `python -m main_GN base ogg_molbace scratch n.model.name`  \n\nCould you please attach the GitHub code for these files?\n\n2. Regarding the prompts given in Appendix A.1 and A.2, were these used for experiments on all databases, or are they just examples? If they are just examples, please provide the prompts that were actually used for your experiments.\n\n3. The method is positioned as a fully automated framework that searches for auxiliary tasks _\"from domain-specific databases or online resources\"_ (page 2, line 28), but experiments use only descriptors from Table 4. Nowhere is it said on what principle the list of descriptors in this table was formed. How is it formed and would the method work with the same accuracy if it searched for properties from the Internet?\n\n4. Table 5 shows that lipophilicity and molecular weight of the molecule contribute to all sets. Would it be possible to exclude them from the task search and look to select 3 tasks instead of 5? In practice, such a configuration could reduce computational costs significantly. Is this possible in the current setup?\n\n5. On page 4 lines 27-28 say, _“By following these steps, AutAuT enables molecular toxicity prediction with auxiliary task enhancement, without requiring manual efforts.”_ The authors seem to test the framework on databases with known toxicity, but in no way generalize the findings to other databases. Could you comment on that?\n\n6. Taking a closer look at Table 5, I notice that many of the properties appear selected by virtually all LLMs in all datasets (e.g., Topological Polar Surface Area, Number of Hydrogen Bond Donors, and others). It is, therefore, uninformative to report the selection of those properties as a result. On the other hand, some properties appear selected at random – by some LLMs and not frequently (e.g., Radius of Gyration and Eccentricity). It is, therefore, unclear how important the prediction of such properties as an auxiliary task really is. Can you provide a table with high specificity properties selected as auxiliary tasks for each dataset along with the information on consistency of the selection and the measured impact on the prediction performance of the target property?\n\n7. Can you provide some evidence and comment on the consistency of the auxiliary-task weights? Is it possible to compile a table with means and standard deviations of the converged weights for at least some datasets? In my understanding, this would support the overwhelming dominance of your method shown on Table 3.\n\n8. Have you experimented with any of the Small Language Models?\n\n9. Do you plan to release an open-source package or toolkit?", "limitations": "The limitations are briefly discussed in Appendix H.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "__Strengths:__\n\n1. It is a very interesting topic and a potentially impactful contribution. AutAuT eliminates manual task selection by using LLM to extract and select tasks based on their relevance to the primary goal.\n2. The authors, with rare exceptions, focus on articles no older than 5 years, which indicates the actuality and relevance of their work.\n3. A detailed mathematical proof of the algorithm's operation has been carried out: dynamic weighting of gradients adaptively enhances the contribution of tasks aligned with the main goal and suppresses noise from irrelevant tasks.\n4. The analysis of the computational complexity of the algorithm and its cost is carried out, which contributes to the transparency of the application of this algorithm.\n5. Superiority over 28 methods in accuracy on 9 datasets was shown. Ablation studies have confirmed the importance of adaptive weighting and task selection.\n6. The paper is very well organized and structured. I found answers to many of my questions either in the main text or in the Appendix.\n\n__Weaknesses:__\n1. A significant disadvantage of the work is the lack of reproducibility. The GitHub repo does not have the files necessary to run the code. The questions section clarifies which files are missing.\n2. No experiments were conducted with searching for additional tasks on the Internet, they were selected by LLM from Table 4 (see question 4).\n3. No open-source resource is advertised or announced in the paper, which limits the wide adoption of the proposed framework.", "was_revised": true, "final_justification": "The authors have made a significant effort in addressing my questions and concerns, which I appreciate. This work has many strengths already outlined. Despite the reproducibility issues, I trust that the authors will address them in the camera-ready submission, as they promised. I would like to keep my positive evaluation.", "rebuttal": "We sincerely appreciate the thorough and constructive feedback provided by Reviewer 8GVh. Below, we systematically address each of your concerns and questions.\n\n> The GitHub repo does not have the files necessary to run the code. \n\n> Do you plan to release an open-source package or toolkit?\n\nWe apologize for this omission. Due to an oversight, the anonymized repository was not kept fully synchronized with the active development repository, resulting in the absence of several utility scripts and data mapping files. As this year’s rebuttal process prohibits any addition or update to the repository, we are unable to upload the missing files at this stage. Upon acceptance, we will ensure that the complete codebase, all required files, and detailed instructions are made publicly available, so that the experiments can be reproduced in full.\n\nThe data files, such as data/ogbg_molbace/mapping/mol.csv.gz, are automatically downloaded by the code when the corresponding dataset is accessed. \n\n> Regarding the prompts given in Appendix A.1 and A.2, were these used for experiments on all databases, or are they just examples? \n\nThe prompts provided in Appendix A.1 and A.2 are precisely those used for auxiliary task retrieval and selection in all our experiments. \n\n> The method is positioned as a fully automated framework that searches for auxiliary tasks… but experiments use only descriptors from Table 4.\n\nThe computable molecular properties in Table 4 are retrieved from the internet using LLMs. As detailed in Section 3.3 and Appendix A.1-A.2, the automated auxiliary task retrieval process begins with a standard prompt (“Find all available computable molecular properties.”), where LLMs are allowed to freely search molecular properties from relevant databases or online resources. There are no restrictions placed on this search process. \n\nFor benchmarking and reproducibility, we focused on properties that are supported by RDKit, as these are both widely used in the literature and guaranteed to be computable for arbitrary molecules [1,2,3]. This ensures all auxiliary tasks can be implemented reliably in practice. Table 4 presents the full list of properties obtained through this automated, internet-based, retrieval process and their brief descriptions.\n\nYet the framework is not inherently constrained by the use of any specific toolkit (e.g., RDKit). If additional computable properties are found on the internet, the method can incorporate them as well, provided they are compatible with the task setup. However, properties that are not computable for arbitrary molecules cannot be used as auxiliary task labels in this context.\n\nWe hope this clarifies the automated, internet-based, nature of our auxiliary task retrieval process. We are happy to provide further details if needed.\n\n> Table 5 shows that lipophilicity and molecular weight of the molecule contribute to all sets. Would it be possible to exclude them from the task search.\n\nWe have investigated the effect of varying the number of auxiliary tasks in our experiments. As illustrated in Figure 3b, reducing the number of selected auxiliary tasks from 5 to 3 does lead to a modest decrease in performance. However, the drop is relatively minor and may be acceptable in scenarios where computational resources are limited. The framework is flexible: users can adjust the number and selection of auxiliary tasks to balance accuracy and efficiency according to their specific requirements.\n\nExcluding specific properties, such as lipophilicity and molecular weight, is also supported in our setup. This can be achieved either by filtering these properties out from Table 4 or by modifying the LLM prompts to exclude them during auxiliary task selection.\n\n> On page 4 lines 27-28 say, “...” The authors seem to test the framework on databases with known toxicity, but in no way generalize the findings to other databases.\n\nThe statement on page 4, lines 27-28, appears in the framework overview section, where molecular toxicity prediction is used as a representative example to illustrate the workflow (see lines 120-121). This _should not be interpreted as a limitation to toxicity prediction tasks_. In fact, _our evaluation covers 9 diverse datasets_ encompassing a broad range of molecular property prediction tasks. For example, the ESOL dataset is focused on predicting water solubility for small organic molecules; the SIDER dataset involves classification of adverse drug reactions grouped by organ system; and the BACE dataset concerns quantitative and qualitative binding affinity predictions for inhibitors of human β-secretase 1 (BACE-1). A full description of all datasets and their corresponding prediction objectives is provided in Appendix F.\n\n> Taking a closer look at Table 5, I notice that many of the properties appear selected by virtually all LLMs in all datasets (...). It is, therefore, uninformative to report the selection of those properties as a result. \n\nWe report the selected auxiliary tasks in Table 5 to provide transparency regarding which properties are consistently chosen by AUTAUT across experiments. The frequent selection of descriptors such as LogP, TPSA, and Number of Hydrogen Bond Donors is scientifically meaningful, as these properties are well established in the chemical literature to be highly relevant for a wide range of molecular prediction tasks. This pattern not only validates the chemical soundness of the LLM-driven selection process but also demonstrates that AUTAUT is able to recover domain-relevant knowledge in a fully automated way.\n\nWe report the actual selected tasks to provide clear evidence about what the framework learns and helps validate the scientific relevance of the output. \n\n>  Can you provide a table with high specificity properties selected as auxiliary tasks for each dataset along with the information on consistency of the selection and the measured impact on the prediction performance of the target property?\n\nWe are not entirely clear on the motivation for emphasizing “high specificity properties” or the precise analysis expected for these properties. We would appreciate it if the reviewer could further explain what is meant by “a table with high specificity properties selected as auxiliary tasks for each dataset, along with the information on consistency of the selection and the measured impact on the prediction performance of the target property.” Additional details or an example would be helpful for us to address this request more precisely.\n\nSome information that might be relevant is that Table 5 reports the number of times each auxiliary task was selected across 5 runs (see the number within round brackets), and Appendix A.3 discusses the behavior of different LLMs in auxiliary task selection.\n\n> On the other hand, some properties appear selected at random - by some LLMs and not frequently.\n\nFigure 2 illustrates the evolution of auxiliary task weights during training for BACE and ClinTox. This shows that AUTAUT adaptively adjusts the influence of each auxiliary task according to its alignment with the main objective, leading to improved convergence and reduced training loss compared to baselines without adaptive weighting. As a result, even auxiliary tasks selected with lower frequency are dynamically weighted by their actual utility during training.\n\n> Can you provide some evidence and comment on the consistency of the auxiliary-task weights?\n\nFigure 2 presents the evolution of auxiliary-task weights (left axes) and training-loss trajectories (right axes) for the BACE and ClinTox datasets. These results demonstrate that the auxiliary-task weights converge consistently across training runs and that the weighting mechanism is stable. The weights increase for auxiliary tasks that align well with the primary objective, while less relevant tasks are automatically down-weighted. This adaptive behavior contributes to both improved convergence and robust performance, as shown in Table 3.\n\n> Have you experimented with any of the Small Language Models?\n\nWe have experimented with several LLMs via API and local deployment. As discussed in Appendix A, our cost analysis indicates that this configuration remains practical for most applications. Based on these results, and given the stable performance achieved with these models, we did not extend our experiments to even smaller language models. However, our framework is model-agnostic and can be readily adapted for use with smaller models if desired.\n\n[1] Strategies for Pre-training Graph Neural Networks, ICLR.  \n[2] Self-Supervised Graph Transformer on Large-Scale Molecular Data, NeurIPS.  \n[3] Physical Consistency Bridges Heterogeneous Data in Molecular Multi-Task Learning, NeurIPS."}, {"review_id": "DRDAWfadb4", "reviewer": "Reviewer_D2QM", "summary": "The paper targets the problem of molecular property prediction, especially when labeled data is scarce. It proposes Automatic Auxiliary Task Selection (AUTAUT) to automatically select auxiliary tasks and adaptively weight them to boost the performance of molecular property prediction. Experiments on MoleculeNet benchmarks show the effectiveness of the proposed method.", "questions": "Please see pros & cons", "limitations": "yes", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "pros: \n*  Traditional methods use auxiliary learning tasks to supplement main tasks, improving performance when labeled molecular data is limited. However, selecting and integrating effective auxiliary tasks requires domain expertise, manual effort, and does not always guarantee better results. This work applies large language models to automatically select auxiliary tasks, which is novel and effective. \n* Experiments on MoleculeNet benchmarks show the effectiveness of the proposed method.\n* This work utilizes a multi-step LLM prompting process to assess and select the most relevant auxiliary tasks for a given primary molecular prediction goal. \n* This work introduces a novel gradient alignment weighting mechanism that dynamically adjusts the influence of each auxiliary task during training, amplifying tasks aligned with the primary objective and minimizing negative interference.\n\ncons:\n* Why not directly use auxiliary task label as model input? It seems using auxiliary task label as model input is more straightforward and effective.", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely thank Reviewer D2QM for the constructive comments and recognition of the novelty and effectiveness of our proposed approach, AUTAUT, in molecular property prediction tasks. We appreciate the positive remarks on our use of LLMs and the introduction of a novel gradient alignment weighting mechanism. Below, we carefully address the specific concern raised by the reviewer.\n\n> Why not directly use auxiliary task labels as model input? It seems using auxiliary task labels as model input is more straightforward and effective.\n\nWe appreciate this insightful question and acknowledge that directly including auxiliary task labels as input features might appear straightforward. We also explored this solution in the early stages of our project. However, empirical evidence from our extensive ablation studies (Section 4.4, Table 6, Appendix G) shows that directly integrating molecular descriptors as static input features results in marginal or inconsistent improvements. For example, using Graphormer, the ROC-AUC on BACE slightly improves from 79.18% to 79.38% when auxiliary labels are added as features, which is minimal compared to the significant boost to 83.72% obtained by our proposed adaptive weighting of auxiliary tasks.\n\nThis observation arises because static inclusion of features does not benefit from the explicit task-level supervision provided by treating these descriptors as auxiliary prediction tasks. Moreover, static inputs do not dynamically adapt during training and thus fail to leverage gradient-based information alignment with the primary task. In contrast, modeling descriptors as auxiliary tasks enables explicit supervision signals and adaptive weighting based on gradient alignment, significantly enhancing predictive performance and preventing negative transfer.\n\nThus, our adaptive weighting strategy ensures the auxiliary tasks positively contribute to the primary objective, dynamically filtering irrelevant or conflicting signals. This distinction is central to the effectiveness and robustness demonstrated by AUTAUT across multiple datasets and models.\n\n---\n\nSince this was the only concern raised, and the relevant details and evidence were already included in the original manuscript, we kindly ask Reviewer D2QM to reconsider updating the rating in light of this clarification. We are happy to provide any further clarification if required."}]

Author rebuttal:

Meta-review: The paper introduces a novel task-selection framework to improve molecular property prediction. The pipeline queries an LLM to identify auxiliary tasks, which are then adaptively weighted during training.

The core experiments are conducted on the MoleculeNet benchmark using RDKit-derived features.

The main strength of the paper is that the framework delivers competitive performance compared to a wide range of other task-selection methods. This outcome is somewhat surprising and was noted positively by the reviewers.

However, as several reviewers observed, the experiments are conducted in a relatively contrived setting. The RDKit-based auxiliary tasks may not carry substantial information given the simplicity of many RDKit functions, and MoleculeNet has known issues, including overlap between training and test splits.

Furthermore, it is not clear whether the observed gains are primarily due to the LLM-based selection or the adaptive weighting.

These issues were discussed during the decision-making phase but were not deemed sufficient to warrant rejection.

Overall, it is my pleasure to recommend accepting the work. For the camera-ready version, please include an ablation that isolates the effect of the LLM from that of the optimization procedure.
Decision: Accept (poster)