# PotentialNet for Molecular Property Prediction

arXiv: https://arxiv.org/abs/1803.04465 · HTML: https://mlsanity.com/paper/1803.04465/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2018.

## Built on by (most cited first, 30 of 46)

- [Geometric Deep Learning on Molecular Representations](https://mlsanity.com/paper/2107.12375/) (2021)
- [A Comprehensive Survey on Deep Graph Representation Learning](https://mlsanity.com/paper/2304.05055/) (2023)
- [Structure-based drug design with geometric deep learning](https://mlsanity.com/paper/2210.11250/) (2022)
- [DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science](https://mlsanity.com/paper/2106.14232/) (2021)
- [Artificial Intelligence in Drug Discovery: Applications and Techniques](https://mlsanity.com/paper/2106.05386/) (2021)
- [Graph Neural Networks for Molecules](https://mlsanity.com/paper/2209.05582/) (2022)
- [Differentiable Physics: A Position Piece](https://mlsanity.com/paper/2109.07573/) (2021)
- [Graph Neural Networks in Modern AI-aided Drug Discovery](https://mlsanity.com/paper/2506.06915/) (2025)
- [A Systematic Comparison Study on Hyperparameter Optimisation of Graph Neural Networks for Molecular Property Prediction](https://mlsanity.com/paper/2102.04283/) (2021)
- [Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport](https://mlsanity.com/paper/2202.06208/) (2022)
- [Molecular relaxation by reverse diffusion with time step prediction](https://mlsanity.com/paper/2404.10935/) (2024)
- [Uncertainty Quantification for Molecular Property Predictions with Graph Neural Architecture Search](https://mlsanity.com/paper/2307.10438/) (2023)
- [Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity](https://mlsanity.com/paper/2103.14536/) (2021)
- [HAC-Net: A Hybrid Attention-Based Convolutional Neural Network for Highly Accurate Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2212.12440/) (2022)
- [Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations](https://mlsanity.com/paper/2306.01631/) (2023)
- [Machine Learning Small Molecule Properties in Drug Discovery](https://mlsanity.com/paper/2308.12354/) (2023)
- [High-Throughput Virtual Screening of Small Molecule Inhibitors for SARS-CoV-2 Protein Targets with Deep Fusion Models](https://mlsanity.com/paper/2104.04547/) (2021)
- [MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties](https://mlsanity.com/paper/2203.09456/) (2022)
- [Structure-based drug discovery with deep learning](https://mlsanity.com/paper/2212.13295/) (2022)
- [Improved prediction of ligand-protein binding affinities by meta-modeling](https://mlsanity.com/paper/2310.03946/) (2023)
- [CITED: A Decision Boundary-Aware Signature for GNNs Towards Model Extraction Defense](https://mlsanity.com/paper/2602.20418/) (2026)
- [Multiparameter Persistent Homology for Molecular Property Prediction](https://mlsanity.com/paper/2311.10808/) (2023)
- [Game-theoretic Counterfactual Explanation for Graph Neural Networks](https://mlsanity.com/paper/2402.06030/) (2024)
- [Knowledge-enhanced Relation Graph and Task Sampling for Few-shot Molecular Property Prediction](https://mlsanity.com/paper/2405.15544/) (2024)
- [Drug-Target Interaction/Affinity Prediction: Deep Learning Models and Advances Review](https://mlsanity.com/paper/2502.15346/) (2025)
- [MetamatBench: Integrating Heterogeneous Data, Computational Tools, and Visual Interface for Metamaterial Discovery](https://mlsanity.com/paper/2505.20299/) (2025)
- [Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction](https://mlsanity.com/paper/2102.01649/) (2021)
- [An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design](https://mlsanity.com/paper/2110.05478/) (2021)
- [Target specific peptide design using latent space approximate trajectory collector](https://mlsanity.com/paper/2302.01435/) (2023)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)

## Abstract (arXiv)

The arc of drug discovery entails a multiparameter optimization problem spanning vast length scales. They key parameters range from solubility (angstroms) to protein-ligand binding (nanometers) to in vivo toxicity (meters). Through feature learning---instead of feature engineering---deep neural networks promise to outperform both traditional physics-based and knowledge-based machine learning models for predicting molecular properties pertinent to drug discovery. To this end, we present the PotentialNet family of graph convolutions. These models are specifically designed for and achieve state-of-the-art performance for protein-ligand binding affinity. We further validate these deep neural networks by setting new standards of performance in several ligand-based tasks. In parallel, we introduce a new metric, the Regression Enrichment Factor EF_chi(R), to measure the early enrichment of computational models for chemical data. Finally, we introduce a cross-validation strategy based on structural homology clustering that can more accurately measure model generalizability, which crucially distinguishes the aims of machine learning for drug discovery from standard machine learning tasks.