Advancing Molecular Machine (Learned) Representations with Stereoelectronics-Infused Molecular Graphs (2408.04520v1)

Abstract: Molecular representation is a foundational element in our understanding of the physical world. Its importance ranges from the fundamentals of chemical reactions to the design of new therapies and materials. Previous molecular machine learning models have employed strings, fingerprints, global features, and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a novel approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects. We show that the explicit addition of stereoelectronic interactions significantly improves the performance of molecular machine learning models. Furthermore, stereoelectronics-infused representations can be learned and deployed with a tailored double graph neural network workflow, enabling its application to any downstream molecular machine learning task. Finally, we show that the learned representations allow for facile stereoelectronic evaluation of previously intractable systems, such as entire proteins, opening new avenues of molecular design.

• The paper presents a novel SIMG framework that integrates quantum-chemical NBO data into molecular graphs for more accurate ML predictions.
• The authors employ a two-step GNN workflow to predict lone pairs and approximate detailed NBO features, reducing computational costs.
• The approach outperforms traditional models on datasets like QM9 and scales to large biomolecules, paving the way for advanced property predictions.

Advancing Molecular Representations with Stereoelectronics-Infused Molecular Graphs

The presented paper introduces an innovative approach to molecular ML representations that infuse quantum-chemical data into molecular graphs, termed Stereoelectronics-Infused Molecular Graphs (SIMGs). This paper addresses the limitations of traditional molecular representations by incorporating stereoelectronic effects to create a high-fidelity molecular representation.

Background and Motivation

Traditional molecular ML models utilize representations such as strings, fingerprints, global features, and simplistic molecular graphs. These methods, albeit useful, possess significant limitations in encoding detailed quantum-chemical information, which becomes critical as the complexity of prediction tasks escalates. This research proposes a method to augment molecular graphs with stereoelectronic information, leveraging Natural Bond Orbital (NBO) analysis data to enrich the graph representation.

Methodology

Construction of SIMGs

SIMGs are constructed by supplementing standard molecular graphs with additional nodes and edges that represent quantum chemical features. The nodes in SIMGs incorporate bond orbitals, lone pairs, and interactions between these entities, capturing three-dimensional (3D) relational information. Specifically, the paper exploits NBO analysis to include various features such as localized atomic orbitals, hybrid orbitals, and the corresponding bonding/nonbonding and antibonding orbitals. This representation is further extended with numerical data from NBO, creating a robust molecular graph that includes 26 bond features, 5 lone-pair features, and 3 interaction features.

Approximation with Graph Neural Networks (GNNs)

Given the computational demand of NBO calculations, the authors propose a surrogate model, SIMG*, to approximate SIMG graphs. They introduce a two-step GNN workflow where:

1. Lone Pair Prediction: A model predicts lone pairs and their types based on standard molecular graphs.
2. SIMG* Construction: An extended molecular graph, incorporating these lone pairs, serves as input for a multitask GNN, which predicts the NBO-derived features.

Key Results

Performance Evaluation

The SIMG representation substantially enhances molecular property predictions. The paper particularly highlights results on the QM9 dataset, showing marked improvements over traditional molecular graph and ChemProp baselines. The SIMGs consistently outperform standard representations in nearly all molecular property prediction tasks, demonstrating superior mean absolute error (MAE) values and bringing predictions closer to chemical accuracy targets.

Active Learning for Large Datasets

A significant portion of the work is dedicated to an active learning approach to efficiently gather training data for the surrogate model. By leveraging epistemic uncertainty estimation through ensemble models, the authors systematically select training samples that enhance model performance, particularly focusing on underrepresented chemical species and conformations.

Application to Proteins

A notable achievement of this research is the extension of the SIMG* model to large, previously computationally intractable molecules, such as proteins. The paper demonstrates successful predictions of stereoelectronic interactions within protein structures, verified through comparison with known NBO data for proteins. This capability paves the way for new computational studies of protein-ligand interactions and other complex biomolecular systems.

Implications and Future Directions

Practical Implications

The SIMG and SIMG* representations hold significant promise for various ML applications across chemical, biological, and material sciences. By improving the fidelity and interpretability of molecular representations, these models can enhance the accuracy of property predictions and enable high-throughput NBO analyses. This can further catalyze advancements in drug discovery, material design, and comprehension of biochemical processes.

Theoretical Implications

The integration of quantum-chemical principles into ML models represents a substantial step forward for theoretical chemistry and ML. This work bridges the gap between ML-driven predictions and quantum mechanical descriptions, opening new research avenues to explore the relationship between electronic structure and molecular properties.

Future Developments

Looking forward, further research could explore incorporating additional elements into the SIMG framework by leveraging physical properties as features, potentially reducing the need for extensive NBO datasets. Moreover, the evolver module could be refined to handle larger datasets with even greater computational efficiency.

Conclusion

This paper presents a significant advancement in molecular machine learning representations by infusing quantum-chemical information into molecular graphs. Through the development of stereoelectronics-infused molecular graphs and their approximation using graph neural networks, the research demonstrates improved molecular property predictions and enhanced applicability to large, complex molecules. This work not only addresses the limitations of existing molecular representations but also paves the way for further advancements in theoretical chemistry and molecular ML applications.