Automatic Auxiliary Task Selection and Adaptive Weighting Boost Molecular Property Prediction

Zhiqiang Zhong, Davide Mottin

Keywords: Molecular Property Prediction, Auxiliary Task Learning, Automatic Task Selection

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.

Primary Area: Machine learning for sciences (e.g. climate, health, life sciences, physics, social sciences)

Submission Number: 18116