Title: Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: Molecular Property Prediction

Auxiliary Task Learning

Automatic Task Selection
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.


Peer reviews:
comments: 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.

STRENGTHS AND WEAKNESSES:
pros: 
*  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. 
* Experiments on MoleculeNet benchmarks show the effectiveness of the proposed method.
* 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. 
* 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.

cons:
* Why not directly use auxiliary task label as model input? It seems using auxiliary task label as model input is more straightforward and effective.

QUESTIONS:
Please see pros & cons

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-06-29

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n__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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nThe limitations are briefly discussed in Appendix H.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nSome subsets in MoleculeNet are skipped, such as HIV and MUV datasets. What is the reason for this?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nThe 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?\n\nQUESTIONS:\nSee above.\n\nLIMITATIONS:\nSee above.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}