# AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery

arXiv: https://arxiv.org/abs/1510.02855 · HTML: https://mlsanity.com/paper/1510.02855/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Oct 2015.

## Built on by (most cited first, 30 of 38)

- [EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction](https://mlsanity.com/paper/2202.05146/) (2022)
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- [DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery -- A Focus on Affinity Prediction Problems with Noise Annotations](https://mlsanity.com/paper/2201.09637/) (2022)
- [Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs](https://mlsanity.com/paper/2207.08824/) (2022)
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- [textttMiniMol: A Parameter-Efficient Foundation Model for Molecular Learning](https://mlsanity.com/paper/2404.14986/) (2024)
- [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)
- [Learning Neural Set Functions Under the Optimal Subset Oracle](https://mlsanity.com/paper/2203.01693/) (2022)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [SS-GNN: A Simple-Structured Graph Neural Network for Affinity Prediction](https://mlsanity.com/paper/2206.07015/) (2022)
- [Predicting Protein-Ligand Binding Affinity via Joint Global-Local Interaction Modeling](https://mlsanity.com/paper/2209.13014/) (2022)
- [An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries](https://mlsanity.com/paper/2211.04468/) (2022)
- [DEL-Dock: Molecular Docking-Enabled Modeling of DNA-Encoded Libraries](https://mlsanity.com/paper/2212.00136/) (2022)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists](https://mlsanity.com/paper/2305.05601/) (2023)
- [AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research](https://mlsanity.com/paper/2505.12039/) (2025)
- [An Oracle for Guiding Large-Scale Model/Hybrid Parallel Training of Convolutional Neural Networks](https://mlsanity.com/paper/2104.09075/) (2021)
- [Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer](https://mlsanity.com/paper/2110.07347/) (2021)
- [FragXsiteDTI: Revealing Responsible Segments in Drug-Target Interaction with Transformer-Driven Interpretation](https://mlsanity.com/paper/2311.02326/) (2023)
- [Regulating Multifunctionality](https://mlsanity.com/paper/2502.15715/) (2025)
- [A Novel Framework Integrating AI Model and Enzymological Experiments Promotes Identification of SARS-CoV-2 3CL Protease Inhibitors and Activity-based Probe](https://mlsanity.com/paper/2105.14224/) (2021)
- [HydraScreen: A Generalizable Structure-Based Deep Learning Approach to Drug Discovery](https://mlsanity.com/paper/2311.12814/) (2023)
- [FREED++: Improving RL Agents for Fragment-Based Molecule Generation by Thorough Reproduction](https://mlsanity.com/paper/2401.09840/) (2024)
- [Dumpling GNN: Hybrid GNN Enables Better ADC Payload Activity Prediction Based on Chemical Structure](https://mlsanity.com/paper/2410.05278/) (2024)
- [Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning](https://mlsanity.com/paper/2511.21900/) (2025)
- [Beyond the Desk: Barriers and Future Opportunities for AI to Assist Scientists in Embodied Physical Tasks](https://mlsanity.com/paper/2603.19504/) (2026)
- [ParaVS: A Simple, Fast, Efficient and Flexible Graph Neural Network Framework for Structure-Based Virtual Screening](https://mlsanity.com/paper/2102.06086/) (2021)

## Abstract (arXiv)

Deep convolutional neural networks comprise a subclass of deep neural networks (DNN) with a constrained architecture that leverages the spatial and temporal structure of the domain they model. Convolutional networks achieve the best predictive performance in areas such as speech and image recognition by hierarchically composing simple local features into complex models. Although DNNs have been used in drug discovery for QSAR and ligand-based bioactivity predictions, none of these models have benefited from this powerful convolutional architecture. This paper introduces AtomNet, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications. We demonstrate how to apply the convolutional concepts of feature locality and hierarchical composition to the modeling of bioactivity and chemical interactions. In further contrast to existing DNN techniques, we show that AtomNet's application of local convolutional filters to structural target information successfully predicts new active molecules for targets with no previously known modulators. Finally, we show that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin, achieving an AUC greater than 0.9 on 57.8% of the targets in the DUDE benchmark.