Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization

Area of Science:

• Machine Learning
• Computational Chemistry
• Scientific Research

Background:

• Neural processes (NPs) are meta-learning models that provide uncertainty estimates, but their application in realistic, diverse scientific settings is underexplored.
• Molecular property prediction often involves sparse datasets and novel tasks, posing challenges for traditional machine learning and transfer learning approaches.

Purpose of the Study:

• To evaluate the effectiveness of graph Neural Processes (NPs) for molecular property prediction using the DOCKSTRING dataset.
• To investigate strategies for improving the meta-generalization of NPs to divergent tasks in scientific applications.
• To compare NP performance against established baselines and explore their utility in Bayesian optimization for molecular screening.

Main Methods:

• Applied graph Neural Processes (NPs) to molecular property prediction tasks.
• Utilized the DOCKSTRING dataset, a diverse collection of molecular docking scores.
• Implemented and evaluated fine-tuning strategies to enhance NP meta-generalization.
• Conducted Bayesian optimization experiments comparing NPs with Gaussian processes.

Main Results:

• Graph NPs demonstrated competitive performance in few-shot learning scenarios compared to supervised and transfer learning baselines.
• Fine-tuning strategies significantly improved NP regression performance and maintained accurate uncertainty calibration, especially for divergent tasks.
• NPs showed potential advantages over Gaussian processes in iterative molecular screening tasks.

Conclusions:

• Neural Processes applied to molecular graphs are a promising approach for molecular property prediction, particularly in low-data regimes.
• The proposed fine-tuning strategies enhance the generalization capabilities of NPs for scientific meta-learning.
• NPs offer a robust framework for tackling data scarcity and task novelty in scientific discovery, including molecular property prediction and optimization.