# Molecular Design Based on Artificial Neural Networks, Integer Programming and Grid Neighbor Search

arXiv: https://arxiv.org/abs/2108.10266 · HTML: https://mlsanity.com/paper/2108.10266/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 45 references that are in the map.
- First posted on arXiv 23 Aug 2021.

## Builds on (most cited first, 8 of 8)

- [Automatic chemical design using a data-driven continuous representation of molecules](https://mlsanity.com/paper/1610.02415/) (2016)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation](https://mlsanity.com/paper/2001.09382/) (2020)
- [Grammar Variational Autoencoder](https://mlsanity.com/paper/1703.01925/) (2017)
- [GraphNVP: An Invertible Flow Model for Generating Molecular Graphs](https://mlsanity.com/paper/1905.11600/) (2019)
- [ChemTS: An Efficient Python Library for de novo Molecular Generation](https://mlsanity.com/paper/1710.00616/) (2017)
- [A Novel Method for Inference of Acyclic Chemical Compounds with Bounded Branch-height Based on Artificial Neural Networks and Integer Programming](https://mlsanity.com/paper/2009.09646/) (2020)
- [A novel method for inference of chemical compounds with prescribed topological substructures based on integer programming](https://mlsanity.com/paper/2010.09203/) (2020)

## Built on by (most cited first, 6 of 6)

- [Molecular Design Based on Integer Programming and Quadratic Descriptors in a Two-layered Model](https://mlsanity.com/paper/2209.13527/) (2022)
- [Molecular Design Based on Integer Programming and Splitting Data Sets by Hyperplanes](https://mlsanity.com/paper/2305.00801/) (2023)
- [Combining Graph Neural Networks and Mixed Integer Linear Programming for Molecular Inference under the Two-Layered Model](https://mlsanity.com/paper/2507.03920/) (2025)
- [Cycle-Configuration: A Novel Graph-theoretic Descriptor Set for Molecular Inference](https://mlsanity.com/paper/2408.05136/) (2024)
- [A Unified Approach to Inferring Chemical Compounds with the Desired Aqueous Solubility](https://mlsanity.com/paper/2409.04301/) (2024)
- [Mixing Vector Model for Copolymer Inference via Mixed Integer Linear Programming](https://mlsanity.com/paper/2605.29329/) (2026)

## Abstract (arXiv)

A novel framework has recently been proposed for designing the molecular structure of chemical compounds with a desired chemical property using both artificial neural networks and mixed integer linear programming. In the framework, a chemical graph with a target chemical value is inferred as a feasible solution of a mixed integer linear program that represents a prediction function and other requirements on the structure of graphs. In this paper, we propose a procedure for generating other feasible solutions of the mixed integer linear program by searching the neighbor of output chemical graph in a search space. The procedure is combined in the framework as a new building block. The results of our computational experiments suggest that the proposed method can generate an additional number of new chemical graphs with up to 50 non-hydrogen atoms.