# Message-passing neural networks for high-throughput polymer screening

arXiv: https://arxiv.org/abs/1807.10363 · HTML: https://mlsanity.com/paper/1807.10363/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

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

- [Graph neural networks for materials science and chemistry](https://mlsanity.com/paper/2208.09481/) (2022)
- [Keeping it Simple: Language Models can learn Complex Molecular Distributions](https://mlsanity.com/paper/2112.03041/) (2021)
- [TransPolymer: a Transformer-based language model for polymer property predictions](https://mlsanity.com/paper/2209.01307/) (2022)
- [Data-Efficient Graph Grammar Learning for Molecular Generation](https://mlsanity.com/paper/2203.08031/) (2022)
- [Discrete Diffusion Schr"odinger Bridge Matching for Graph Transformation](https://mlsanity.com/paper/2410.01500/) (2024)
- [A graph representation of molecular ensembles for polymer property prediction](https://mlsanity.com/paper/2205.08619/) (2022)
- [AugLiChem: Data Augmentation Library of Chemical Structures for Machine Learning](https://mlsanity.com/paper/2111.15112/) (2021)
- [Conditional β-VAE for De Novo Molecular Generation](https://mlsanity.com/paper/2205.01592/) (2022)
- [Molecular Hypergraph Neural Networks](https://mlsanity.com/paper/2312.13136/) (2023)
- [MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation](https://mlsanity.com/paper/2305.08457/) (2023)
- [LLamol: A Dynamic Multi-Conditional Generative Transformer for De Novo Molecular Design](https://mlsanity.com/paper/2311.14407/) (2023)
- [Representing Polymers as Periodic Graphs with Learned Descriptors for Accurate Polymer Property Predictions](https://mlsanity.com/paper/2205.13757/) (2022)
- [Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures](https://mlsanity.com/paper/2302.08647/) (2023)
- [Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties](https://mlsanity.com/paper/2101.05339/) (2021)
- [Employing Artificial Intelligence to Steer Exascale Workflows with Colmena](https://mlsanity.com/paper/2408.14434/) (2024)
- [Artificial Intelligence based Autonomous Molecular Design for Medical Therapeutic: A Perspective](https://mlsanity.com/paper/2102.06045/) (2021)
- [Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources](https://mlsanity.com/paper/2303.08803/) (2023)
- [Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19](https://mlsanity.com/paper/2102.04977/) (2021)
- [Photonic Quantum Computing For Polymer Classification](https://mlsanity.com/paper/2211.12207/) (2022)
- [Improving Molecule Properties Through 2-Stage VAE](https://mlsanity.com/paper/2212.02750/) (2022)
- [Are VAEs Bad at Reconstructing Molecular Graphs?](https://mlsanity.com/paper/2305.03041/) (2023)
- [Transfer Learning across Different Chemical Domains: Virtual Screening of Organic Materials with Deep Learning Models Pretrained on Small Molecule and Chemical Reaction Data](https://mlsanity.com/paper/2311.18377/) (2023)
- [Automatic design of novel potential 3CLpro and PLpro inhibitors](https://mlsanity.com/paper/2101.11890/) (2021)
- [GLAMOUR: Graph Learning over Macromolecule Representations](https://mlsanity.com/paper/2103.02565/) (2021)
- [Evening the Score: Targeting SARS-CoV-2 Protease Inhibition in Graph Generative Models for Therapeutic Candidates](https://mlsanity.com/paper/2105.10489/) (2021)
- [Machine Learning Approach to Polymerization Reaction Engineering: Determining Monomers Reactivity Ratios](https://mlsanity.com/paper/2301.01231/) (2023)
- [Objective-Agnostic Enhancement of Molecule Properties via Multi-Stage VAE](https://mlsanity.com/paper/2308.13066/) (2023)
- [Sparse Probabilistic Graph Circuits](https://mlsanity.com/paper/2508.07763/) (2025)
- [Exploring polymer classification with a hybrid single-photon quantum approach](https://mlsanity.com/paper/2512.18125/) (2025)
- [Periodic Topological Deep Learning for Polymer Design and Discovery](https://mlsanity.com/paper/2605.26833/) (2026)

## Abstract (arXiv)

Machine learning methods have shown promise in predicting molecular properties, and given sufficient training data machine learning approaches can enable rapid high-throughput virtual screening of large libraries of compounds. Graph-based neural network architectures have emerged in recent years as the most successful approach for predictions based on molecular structure, and have consistently achieved the best performance on benchmark quantum chemical datasets. However, these models have typically required optimized 3D structural information for the molecule to achieve the highest accuracy. These 3D geometries are costly to compute for high levels of theory, limiting the applicability and practicality of machine learning methods in high-throughput screening applications. In this study, we present a new database of candidate molecules for organic photovoltaic applications, comprising approximately 91,000 unique chemical structures.Compared to existing datasets, this dataset contains substantially larger molecules (up to 200 atoms) as well as extrapolated properties for long polymer chains. We show that message-passing neural networks trained with and without 3D structural information for these molecules achieve similar accuracy, comparable to state-of-the-art methods on existing benchmark datasets. These results therefore emphasize that for larger molecules with practical applications, near-optimal prediction results can be obtained without using optimized 3D geometry as an input. We further show that learned molecular representations can be leveraged to reduce the training data required to transfer predictions to a new DFT functional.