ArticleChemical science2024
A physics-aware neural network for protein-ligand interactions with quantum chemical accuracy.
Article in Chemical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A QM-AI Approach for the Acceleration of Accurate Assessments of Halogen-π Interactions by Training Neural Networks.Journal of chemical information and modeling · 2025Article
- What is the future role of quantum computation in drug discovery?Emerging topics in life sciences · 2025Review
- Convergent Protocols for Computing Protein-Ligand Interaction Energies Using Fragment-Based Quantum Chemistry.Journal of chemical theory and computation · 2025Article
- Directional ΔJournal of chemical information and modeling · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Quantifying intermolecular interactions with quantum chemistry (QC) is useful for many chemical problems, including understanding the nature of protein-ligand interactions. Unfortunately, QC computations on protein-ligand systems are too computationally expensive for most use cases. The flourishing field of machine-learned (ML) potentials is a promising solution, but it is limited by an inability to easily capture long range, non-local interactions. In this work we develop an atomic-pairwise neural network (AP-Net) specialized for modeling intermolecular interactions. This model benefits from a number of physical constraints, including a two-component equivariant message passing neural network architecture that predicts interaction energies
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Registered trials
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