# Learning Deep Architectures for Interaction Prediction in Structure-based Virtual Screening

arXiv: https://arxiv.org/abs/1610.07187 · HTML: https://mlsanity.com/paper/1610.07187/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2016.

## Built on by (most cited first, 6 of 6)

- [EquiPocket: an E(3)-Equivariant Geometric Graph Neural Network for Ligand Binding Site Prediction](https://mlsanity.com/paper/2302.12177/) (2023)
- [Protein-ligand binding representation learning from fine-grained interactions](https://mlsanity.com/paper/2311.16160/) (2023)
- [Decoding the Protein-ligand Interactions Using Parallel Graph Neural Networks](https://mlsanity.com/paper/2111.15144/) (2021)
- [Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling](https://mlsanity.com/paper/2209.13014/) (2022)
- [Dynamic Molecular Graph-based Implementation for Biophysical Properties Prediction](https://mlsanity.com/paper/2212.09991/) (2022)
- [Decoding Drug Discovery: Exploring A-to-Z In silico Methods for Beginners](https://mlsanity.com/paper/2412.11137/) (2024)

## Abstract (arXiv)

We introduce a deep learning architecture for structure-based virtual screening that generates fixed-sized fingerprints of proteins and small molecules by applying learnable atom convolution and softmax operations to each compound separately. These fingerprints are further transformed non-linearly, their inner-product is calculated and used to predict the binding potential. Moreover, we show that widely used benchmark datasets may be insufficient for testing structure-based virtual screening methods that utilize machine learning. Therefore, we introduce a new benchmark dataset, which we constructed based on DUD-E and PDBBind databases.