PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions
By Seokhyun Moon, Wonho Zhung, Soojung Yang, Jaechang Lim, Woo Youn Kim. 2020
149 citations recorded by the source corpus.
Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were highlighted for their high accuracy with affordable computational costs. Yet, the models' insufficient generalization remains a challenging problem in the practice of in silico drug discovery. We propose two key strategies to enhance generalization in the DTI model. The first is to predict the atom–atom pairwise interactions via physics-informed equations parameterized with neural networks and provides the total binding affinity of a protein–ligand complex as their sum. We further improved the model generalization by augmenting a broader range of binding poses and ligands to training data. We validated our model, PIGNet, in the comparative assessment of scoring functions (CASF) 2016, demonstrating the outperforming docking and screening powers than previous methods. Our physics-informing strategy also enables the interpretation of predicted affinities by visualizing the contribution of ligand substructures, providing insights for further ligand optimization.
Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
In Materials Chemistry
In Molecular Biology
Citations in the corpus
- TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction (2022, methodology): “We compare TankBind against two state-of-the-art sequencebased methods, TransformerCPI [53] and MONN [28], two complex-based methods, IGN [38] and PIGNet [54] both requiring prior knowledge of the inter-molecular structure to predict affinity, and two structure-based methods, HOLOPTOT [23] and STAMP”
- DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking (2022, neutral): “Then, the representations of the different [33], HOLOPTOT [34], STAMPDPI [35] and TANKBind [4].”
- Artificial intelligence in drug development (2025, unknown): “…biases (which refer to the model’s inherent tendency to prioritize certain types of solutions over others) or data augmentation (which refers to techniques used to artificially expand the diversity of a training dataset without collecting new data) might improve model generalizability 79,80 .”