Beyond the Black Box: Explainable Chemical Artificial Intelligence From Real Space Quantum Chemistry

Machine-learned chemistry predicts molecular properties but is hard to interpret. This issue is addressed by using real-space quantum chemistry to efficiently predict atomic and interatomic terms, enabling interpretable chemical AI models and enhancing our understanding of molecular interactions.
Beyond the Black Box: Explainable Chemical Artificial Intelligence From Real Space Quantum Chemistry

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Traditionally, the rigorous study of chemistry has been limited to small molecules due to the high computational costs involved. This changed dramatically with the advent of Artificial Intelligence (AI) models capable of predicting a wide range of chemical descriptors and properties without the need for running expensive quantum mechanical calculations. Recently, AI models have rapidly advanced, enabling the computational simulation of complex and large molecules closer to the systems tackled by state-of-the-art research in Chemistry.

However, most chemical AI models face two major challenges. Firstly, they often display limited extrapolation abilities, which can result in erratic predictions when applied to previously unexplored domains of the chemical space. Secondly, they are uninterpretable, providing accurate estimates without explaining the underlying logic, thus behaving as black boxes.

Quantum Chemical Topology

To address this problem, we propose leveraging the rigor of modern physical theories, particularly Quantum Chemical Topology (QCT). This theory is founded on the topological analysis of various scalar fields, such as electron density, enabling the decomposition of real space into a collection of basins with well-defined local properties. As such, QCT provides an ideal foundation for developing advanced theoretical tools for chemistry, including the Quantum Theory of Atoms in Molecules (QTAIM) and the Interacting Quantum Atoms (IQA) energy partitioning scheme.

Illustration depicting the electron density distribution of two closely positioned Helium atoms, highlighting the presence of nuclear cusps. Each nucleus has an associated basin of attraction, providing a QCT partition of real space.

Thus, we believe that by creating AI architectures designed to learn local QCT properties, we can develop interpretable and transferable models. This strategy contrasts with the common approach in the field, which often involves directly predicting molecular properties as a sum of independent terms that frequently lack physical significance.

Transferability and Interpretability

The architecture developed, called SchNet4AIM, relies on SchNet to autonomously describe the chemical environments of each atom embedded in a molecule, which are then used to establish a correspondence between structure and property spaces. In this way, molecular quantities are reconstructed as a combination of atomic and pairwise contributions. Such an approach contrasts with what is commonly done in the field, which often employs terms lacking well-defined physical meanings to obtain the final predictions.

Our results reveal that this approach yields highly accurate and physically consistent atomic and pairwise predictions. However, the true innovation lies not solely in accuracy but in the broad applicability and adaptability of the models. SchNet4AIM demonstrates proficiency in predicting highly complex molecules beyond its training dataset, a departure from the typical limitations seen in many chemical AI models, known as extrapolation challenges. This unique capability, stemming from the transferability and robustness of QCT theories, contributes to addressing a major hurdle in contemporary chemical AI: the quest for universally applicable models that transcend accuracy constraints.

Furthermore, the utilization of chemical locality  leads to an unforeseen outcome: the models exhibit inherent interpretability. Our findings indicate that SchNet4AIM can discern and elucidate the electronic delocalization accompanying supramolecular binding processes. Consequently, it not only identifies the emergence of chemical contacts but also highlights the predominant pairwise interactions driving such binding. This marks a crucial advancement in the pursuit of chemically explainable AIs (XCAIs), an emergent area within AI research.

Evolution of the electronic delocalization, as forecasted by SchNet4AIM, during the formation of a supramolecular complex between a ligand and a receptor. Each distinct color represents the (explained) prevailing pairwise components elucidated by SchNet4AIM.

Future Work

This breakthrough heralds an exciting convergence between Quantum Chemical Topology and Artificial Intelligence, paving the way for the development of computational tools with vast potential applications. That said, SchNet4AIM encounters certain challenges, including comprehending how molecular properties are actually affected by the physical variables of the system. 

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Theoretical Chemistry
Physical Sciences > Chemistry > Theoretical Chemistry
Computational Chemistry
Physical Sciences > Chemistry > Theoretical Chemistry > Computational Chemistry
Artificial Intelligence
Mathematics and Computing > Computer Science > Artificial Intelligence
Quantum Chemistry
Physical Sciences > Chemistry > Theoretical Chemistry > Quantum Chemistry
Machine Learning
Mathematics and Computing > Statistics > Statistics and Computing > Machine Learning
Atomic, Molecular and Chemical Physics
Physical Sciences > Physics and Astronomy > Atomic, Molecular and Chemical Physics

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