# Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions

arXiv: https://arxiv.org/abs/2404.04224 · HTML: https://mlsanity.com/paper/2404.04224/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 61 references that are in the map.
- First posted on arXiv 5 Apr 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Batch Active Learning by Diverse, Uncertain Gradient Lower Bounds](https://mlsanity.com/paper/1906.03671/) (2019)
- [Drug discovery with explainable artificial intelligence](https://mlsanity.com/paper/2007.00523/) (2020)
- [Metrics for Graph Comparison: A Practitioner's Guide](https://mlsanity.com/paper/1904.07414/) (2019)
- [A Deep Generative Model for Molecule Optimization via One Fragment Modification](https://mlsanity.com/paper/2012.04231/) (2020)
- [Interpretable and Explainable Machine Learning for Materials Science and Chemistry](https://mlsanity.com/paper/2111.01037/) (2021)
- [Reliable and Explainable Machine Learning Methods for Accelerated Material Discovery](https://mlsanity.com/paper/1901.02717/) (2019)
- [Inverse design of two-dimensional materials with invertible neural networks](https://mlsanity.com/paper/2106.03013/) (2021)
- [Molecular geometric deep learning](https://mlsanity.com/paper/2306.15065/) (2023)
- [Scalable Fragment-Based 3D Molecular Design with Reinforcement Learning](https://mlsanity.com/paper/2202.00658/) (2022)
- [Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space](https://mlsanity.com/paper/2301.02665/) (2023)
- [PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular Generation](https://mlsanity.com/paper/2207.00821/) (2022)
- [Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling](https://mlsanity.com/paper/2212.01016/) (2022)

## Built on by (most cited first, 1 of 1)

- [Born-Qualified: An Autonomous Framework for Deploying Advanced Energy and Electronic Materials](https://mlsanity.com/paper/2605.00639/) (2026)

## Abstract (arXiv)

Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have become standard for predictions, but they face challenges when applied across different datasets due to reliance on correlations between molecular representation and target properties. These approaches typically depend on large datasets to capture the diversity within the chemical space, facilitating a more accurate approximation, interpolation, or extrapolation of the chemical behavior of molecules. In our research, we introduce an active learning approach that discerns underlying cause-effect relationships through strategic sampling with the use of a graph loss function. This method identifies the smallest subset of the dataset capable of encoding the most information representative of a much larger chemical space. The identified causal relations are then leveraged to conduct systematic interventions, optimizing the design task within a chemical space that the models have not encountered previously. While our implementation focused on the QM9 quantum-chemical dataset for a specific design task-finding molecules with a large dipole moment-our active causal learning approach, driven by intelligent sampling and interventions, holds potential for broader applications in molecular, materials design and discovery.