# SHA-256 Infused Embedding-Driven Generative Modeling of High-Energy Molecules in Low-Data Regimes

arXiv: https://arxiv.org/abs/2510.25788 · HTML: https://mlsanity.com/paper/2510.25788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 72 references that are in the map.
- First posted on arXiv 28 Oct 2025.

## Builds on (most cited first, 20 of 20)

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- [Structure-based drug design with geometric deep learning](https://mlsanity.com/paper/2210.11250/) (2022)
- [ChemTS: An Efficient Python Library for de novo Molecular Generation](https://mlsanity.com/paper/1710.00616/) (2017)
- [Fully Understanding the Hashing Trick](https://mlsanity.com/paper/1805.08539/) (2018)
- [An Equivariant Generative Framework for Molecular Graph-Structure Co-Design](https://mlsanity.com/paper/2304.12436/) (2023)
- [GEN: Highly Efficient SMILES Explorer Using Autodidactic Generative Examination Networks](https://mlsanity.com/paper/1909.04825/) (2019)

## Abstract (arXiv)

High-energy materials (HEMs) are critical for propulsion and defense domains, yet their discovery remains constrained by experimental data and restricted access to testing facilities. This work presents a novel approach toward high-energy molecules by combining Long Short-Term Memory (LSTM) networks for molecular generation and Attentive Graph Neural Networks (GNN) for property predictions. We propose a transformative embedding space construction strategy that integrates fixed SHA-256 embeddings with partially trainable representations. Unlike conventional regularization techniques, this changes the representational basis itself, reshaping the molecular input space before learning begins. Without recourse to pretraining, the generator achieves 67.5% validity and 37.5% novelty. The generated library exhibits a mean Tanimoto coefficient of 0.214 relative to training set signifying the ability of framework to generate a diverse chemical space. We identified 37 new super explosives higher than 9 km/s predicted detonation velocity.