Drug-drug interactions (DDIs) are a major concern in clinical practice, as they can lead to reduced therapeutic efficacy or severe adverse effects. Traditional computational approaches often struggle to capture the complex relationships among drugs, targets, and biological entities. In this work, we propose HGNN-DDI, a heterogeneous graph neural network model designed to predict potential DDIs by integrating multiple drug-related data sources. HGNN-DDI leverages graph representation learning to model heterogeneous biomedical networks, enabling effective information propagation across diverse node and edge types. Experimental results on benchmark DDI datasets demonstrate that HGNN-DDI outperforms state-of-the-art baselines in prediction accuracy and robustness, highlighting its potential to support safer drug development and precision medicine.

Unlocking the potential of nanomaterials in medicine and environmental science hinges on understanding their interactions with proteins, a complex decision space where AI is poised to make a transformative impact. However, progress has been hindered by limited datasets and the restricted generalizability of existing models. Here, we propose NanoPro-3M, the largest nanomaterial-protein interaction dataset to date, comprising over 3.2 million samples and 37,000 unique proteins. Leveraging this, we present NanoProFormer, a foundational model that predicts nanomaterial-protein affinities through multimodal representation learning, demonstrating strong generalization, handling missing features, and unseen nanomaterials or proteins. We show that multimodal modeling significantly outperforms single-modality approaches and identifies key determinants of corona formation. Furthermore, we demonstrate its applicability to a range of downstream tasks through zero-shot inference and fine-tuning. Together, this work establishes a solid foundation for high-performance and generalized prediction of nanomaterial-protein interaction endpoints, reducing experimental reliance and accelerating various in vitro applications.

Drug-protein binding and dissociation dynamics are fundamental to understanding molecular interactions in biological systems. While many tools for drug-protein interaction studies have emerged, especially artificial intelligence (AI)-based generative models, predictive tools on binding/dissociation kinetics and dynamics are still limited. We propose a novel research paradigm that combines molecular dynamics (MD) simulations, enhanced sampling, and AI generative models to address this issue. We propose an enhanced sampling strategy to efficiently implement the drug-protein dissociation process in MD simulations and estimate the free energy surface (FES). We constructed a program pipeline of MD simulations based on this sampling strategy, thus generating a dataset including 26,612 drug-protein dissociation trajectories containing about 13 million frames. We named this dissociation dynamics dataset DD-13M and used it to train a deep equivariant generative model UnbindingFlow, which can generate collision-free dissociation trajectories. The DD-13M database and UnbindingFlow model represent a significant advancement in computational structural biology, and we anticipate its broad applicability in machine learning studies of drug-protein interactions. Our ongoing efforts focus on expanding this methodology to encompass a broader spectrum of drug-protein complexes and exploring novel applications in pathway prediction.

One of the LPDI's de novo protein binders (red) bound to the protein Bcl2 (blue) in complex with FDA-approved drug Venetoclax (beige) LPDI EPFL In 2023, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI), led by Bruno Correia, published a deep-learning pipeline for designing new proteins to interact with therapeutic targets. MaSIF can rapidly scan millions of proteins to identify optimal matches between molecules based on their chemical and geometric surface properties, enabling scientists to engineer novel protein-protein interactions that play key roles in cell regulation and therapeutics. A year and a half later, the team has reported an exciting advancement of this technology. They have used MaSIF to design novel protein binders to interact with known protein complexes involving small molecules like therapeutic drugs or hormones. Because these bound small molecules induce subtle changes in the surface properties ('neosurfaces') of these protein-drug complexes, they can act as'on' or'off' switches for the fine control of cellular functions like DNA transcription or protein degradation.

Numerous cellular functions rely on protein$\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic biophysical knowledge about protein interactions. Here, we review the growing ecosystem of deep learning methods for modeling protein interactions, highlighting the diversity of these biophysically-informed models and their respective trade-offs. We discuss recent successes in using representation learning to capture complex features pertinent to predicting protein interactions and interaction sites, geometric deep learning to reason over protein structures and predict complex structures, and generative modeling to design de novo protein assemblies. We also outline some of the outstanding challenges and promising new directions. Opportunities abound to discover novel interactions, elucidate their physical mechanisms, and engineer binders to modulate their functions using deep learning and, ultimately, unravel how protein interactions orchestrate complex cellular behaviors.

The geometric deep-learning method (PeSTo) used to predict protein binding interfaces. The amino acids involved in the protein binding interface are highlighted in red. Proteins are essential to the biological functions of most living organisms. They have evolved to interact with other proteins, nucleic acids, lipids etc., and all of those interactions form large, "supra-molecular" complexes. This means that understanding protein interactions is crucial for understanding many cellular processes.

Receptor-binding domain-binder designs displayed on yeast. From De novo design of protein interactions with learned surface fingerprints. Reproduced under a CC BY 4.0 licence. In 2019, scientists in the joint School of Engineering and School of Life Sciences Laboratory of Protein Design and Immunoengineering (LPDI) led by Bruno Correia developed MaSIF: a machine learning-driven method for scanning millions of protein surfaces within minutes to analyze their structure and functional properties. The researchers' ultimate goal was to computationally design protein interactions by finding optimal matches between molecules based on their surface chemical and geometric "fingerprints".

UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics. Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong's postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern.

DALLAS – Nov. 16, 2021 – UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics. Dr. Cong led the study with David Baker, Ph.D., Professor of Biochemistry and Dr. Cong's postdoctoral mentor at the University of Washington prior to her recruitment to UT Southwestern.

An international team led by researchers at UT Southwestern and the University of Washington predicted the structures using artificial intelligence techniques. UT Southwestern and University of Washington researchers led an international team that used artificial intelligence (AI) and evolutionary analysis to produce 3D models of eukaryotic protein interactions. The study, published in Science, identified more than 100 probable protein complexes for the first time and provided structural models for more than 700 previously uncharacterized ones. Insights into the ways pairs or groups of proteins fit together to carry out cellular processes could lead to a wealth of new drug targets. "Our results represent a significant advance in the new era in structural biology in which computation plays a fundamental role," said Qian Cong, Ph.D., Assistant Professor in the Eugene McDermott Center for Human Growth and Development with a secondary appointment in Biophysics.