# Deep Molecular Representation Learning via Fusing Physical and Chemical Information

arXiv: https://arxiv.org/abs/2112.04624 · HTML: https://mlsanity.com/paper/2112.04624/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 60 references that are in the map.
- First posted on arXiv 28 Nov 2021.

## Builds on (most cited first, 23 of 23)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [MoleculeNet: A Benchmark for Molecular Machine Learning](https://mlsanity.com/paper/1703.00564/) (2017)
- [A Survey on Multi-Task Learning](https://mlsanity.com/paper/1707.08114/) (2017)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Junction Tree Variational Autoencoder for Molecular Graph Generation](https://mlsanity.com/paper/1802.04364/) (2018)
- [Directional Message Passing for Molecular Graphs](https://mlsanity.com/paper/2003.03123/) (2020)
- [Multi-Task Learning with Deep Neural Networks: A Survey](https://mlsanity.com/paper/2009.09796/) (2020)
- [Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation](https://mlsanity.com/paper/1806.02473/) (2018)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [Deep Potential Molecular Dynamics: a scalable model with the accuracy of quantum mechanics](https://mlsanity.com/paper/1707.09571/) (2017)
- [Fr'echet ChemNet Distance: A metric for generative models for molecules in drug discovery](https://mlsanity.com/paper/1803.09518/) (2018)
- [Learning Gradient Fields for Molecular Conformation Generation](https://mlsanity.com/paper/2105.03902/) (2021)
- [Molecular geometry prediction using a deep generative graph neural network](https://mlsanity.com/paper/1904.00314/) (2019)
- [SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug Discovery](https://mlsanity.com/paper/1911.04738/) (2019)
- [Exploiting Neuron and Synapse Filter Dynamics in Spatial Temporal Learning of Deep Spiking Neural Network](https://mlsanity.com/paper/2003.02944/) (2020)
- [Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference](https://mlsanity.com/paper/2005.07704/) (2020)
- [86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy](https://mlsanity.com/paper/2004.11658/) (2020)
- [HamNet: Conformation-Guided Molecular Representation with Hamiltonian Neural Networks](https://mlsanity.com/paper/2105.03688/) (2021)
- [3D-PhysNet: Learning the Intuitive Physics of Non-Rigid Object Deformations](https://mlsanity.com/paper/1805.00328/) (2018)
- [Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation](https://mlsanity.com/paper/1811.09794/) (2018)
- [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)

## Built on by (most cited first, 21 of 21)

- [Graph-based Molecular Representation Learning](https://mlsanity.com/paper/2207.04869/) (2022)
- [Uncovering Neural Scaling Laws in Molecular Representation Learning](https://mlsanity.com/paper/2309.15123/) (2023)
- [A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware](https://mlsanity.com/paper/2306.14052/) (2023)
- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport](https://mlsanity.com/paper/2202.06208/) (2022)
- [Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment](https://mlsanity.com/paper/2409.14106/) (2024)
- [ODE-based Recurrent Model-free Reinforcement Learning for POMDPs](https://mlsanity.com/paper/2309.14078/) (2023)
- [Impact of Domain Knowledge and Multi-Modality on Intelligent Molecular Property Prediction: A Systematic Survey](https://mlsanity.com/paper/2402.07249/) (2024)
- [PEMP: Leveraging Physics Properties to Enhance Molecular Property Prediction](https://mlsanity.com/paper/2211.01978/) (2022)
- [Improving Molecular Pretraining with Complementary Featurizations](https://mlsanity.com/paper/2209.15101/) (2022)
- [MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights](https://mlsanity.com/paper/2412.16483/) (2024)
- [3DMolFormer: A Dual-channel Framework for Structure-based Drug Discovery](https://mlsanity.com/paper/2502.05107/) (2025)
- [Recent Developments in GNNs for Drug Discovery](https://mlsanity.com/paper/2506.01302/) (2025)
- [Graph Scattering beyond Wavelet Shackles](https://mlsanity.com/paper/2301.11456/) (2023)
- [Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small Models](https://mlsanity.com/paper/2408.10124/) (2024)
- [GlycoNMR: Dataset and benchmarks for NMR chemical shift prediction of carbohydrates with graph neural networks](https://mlsanity.com/paper/2311.17134/) (2023)
- [TransPlace: Transferable Circuit Global Placement via Graph Neural Network](https://mlsanity.com/paper/2501.05667/) (2025)
- [HSA-Net: Hierarchical and Structure-Aware Framework for Efficient and Scalable Molecular Language Modeling](https://mlsanity.com/paper/2508.08334/) (2025)
- [Electron-Informed Coarse-Graining Molecular Representation Learning for Real-World Molecular Physics](https://mlsanity.com/paper/2602.07087/) (2026)
- [DMMRL: Disentangled Multi-Modal Representation Learning via Variational Autoencoders for Molecular Property Prediction](https://mlsanity.com/paper/2603.21108/) (2026)
- [Universal and efficient graph neural networks with dynamic attention for machine learning interatomic potentials](https://mlsanity.com/paper/2603.22810/) (2026)

## Abstract (arXiv)

Molecular representation learning is the first yet vital step in combining deep learning and molecular science. To push the boundaries of molecular representation learning, we present PhysChem, a novel neural architecture that learns molecular representations via fusing physical and chemical information of molecules. PhysChem is composed of a physicist network (PhysNet) and a chemist network (ChemNet). PhysNet is a neural physical engine that learns molecular conformations through simulating molecular dynamics with parameterized forces; ChemNet implements geometry-aware deep message-passing to learn chemical / biomedical properties of molecules. Two networks specialize in their own tasks and cooperate by providing expertise to each other. By fusing physical and chemical information, PhysChem achieved state-of-the-art performances on MoleculeNet, a standard molecular machine learning benchmark. The effectiveness of PhysChem was further corroborated on cutting-edge datasets of SARS-CoV-2.