# In silico generation of novel, drug-like chemical matter using the LSTM neural network

arXiv: https://arxiv.org/abs/1712.07449 · HTML: https://mlsanity.com/paper/1712.07449/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2017.

## Built on by (most cited first, 7 of 7)

- [Deep Learning Methods for Small Molecule Drug Discovery: A Survey](https://mlsanity.com/paper/2303.00313/) (2023)
- [Budget-Aware Sequential Brick Assembly with Efficient Constraint Satisfaction](https://mlsanity.com/paper/2210.01021/) (2022)
- [Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning](https://mlsanity.com/paper/2204.08682/) (2022)
- [De Novo Drug Design with Joint Transformers](https://mlsanity.com/paper/2310.02066/) (2023)
- [Investigation of chemical structure recognition by encoder-decoder models in learning progress](https://mlsanity.com/paper/2210.16307/) (2022)
- [Multi-view deep learning based molecule design and structural optimization accelerates the SARS-CoV-2 inhibitor discovery](https://mlsanity.com/paper/2212.01575/) (2022)
- [SOLD: SELFIES-based Objective-driven Latent Diffusion](https://mlsanity.com/paper/2509.25198/) (2025)

## Abstract (arXiv)

The exploration of novel chemical spaces is one of the most important tasks of cheminformatics when supporting the drug discovery process. Properly designed and trained deep neural networks can provide a viable alternative to brute-force de novo approaches or various other machine-learning techniques for generating novel drug-like molecules. In this article we present a method to generate molecules using a long short-term memory (LSTM) neural network and provide an analysis of the results, including a virtual screening test. Using the network one million drug-like molecules were generated in 2 hours. The molecules are novel, diverse (contain numerous novel chemotypes), have good physicochemical properties and have good synthetic accessibility, even though these qualities were not specific constraints. Although novel, their structural features and functional groups remain closely within the drug-like space defined by the bioactive molecules from ChEMBL. Virtual screening using the profile QSAR approach confirms that the potential of these novel molecules to show bioactivity is comparable to the ChEMBL set from which they were derived. The molecule generator written in Python used in this study is available on request.