Quantum and Classical Graph Convolutional Neural Networks for Protein Ligand Dissociation Constant Prediction
Salamatov, A.; Bai, J.; Atluri, G.; Guan, C.
Show abstract
How long a drug stays bound to its target - the residence time - is now recognized as a stronger in vivo efficacy driver than binding affinity alone. Yet current machine learning (ML) models for dissociation kinetics (koff) ignore two critical sources of structure information: (1) the change in protein-ligand geometry across time, and (2) the ability to represent complex spatial interactions with fewer parameters. We extend a state-of -the -art spatial GNN for protein-ligand complexes with two innovations. First, we introduce a 2-timestep GCN+GRU model that learns structure changes before and after short molecular dynamics simulations. Second, we compress the model head using variational quantum circuits, preserving expressivity while removing 66% of parameters. On the full PDBbind-koff-2020 benchmark, temporal integration improves predictive accuracy, and quantum compression matches the full classical models accuracy at a fraction of its size. Our results show that time and quantum structure are underexplored, high-leverage axes for advancing kinetic ML models used in drug design.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- EGRET: Edge Aggregated Graph Attention Networks and Transfer Learning Improve Protein-Protein Interaction Site Prediction 97%
- Interpretable and Generalizable Attention-Based Model for Predicting Drug-Target Interaction Using 3D Structure of Protein Binding Sites: SARS-CoV-2 Case Study and in-Lab Validation 97%
- A New Paradigm for Applying Deep Learning to Protein-Ligand Interaction Prediction 97%
Similar papers in this journal
Similar papers in this journal
- Novel, provable algorithms for efficient ensemble-based computational protein design and their application to the redesign of the c-Raf-RBD:KRas protein-protein interface 96%
- Predicting Affinity Through Homology (PATH): Interpretable Binding Affinity Prediction with Persistent Homology 95%
- Computational design of novel Cas9 PAM-interacting domains using evolution-based modelling and structural quality assessment 95%
Similar papers in this journal
- DeepLPI: a novel deep learning-based model for protein-ligand interaction prediction for drug repurposing 96%
- Smart Distributed Data Factory: Volunteer Computing Platform for Active Learning-Driven Molecular Data Acquisition 95%
- Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.