## Abstract

A key factor in computational drug design is the consistency and reliability with which intermolecular interactions between a wide variety of molecules can be described. Here we present a procedure to efficiently, reliably and automatically assign partial atomic charges to atoms based on known distributions. We formally introduce the molecular charge assignment problem, where the task is to select a charge from a set of candidate charges for every atom of a given query molecule. Charges are accompanied by a score that depends on their observed frequency in similar neighbourhoods (chemical environments) in a database of previously parameterised molecules. The aim is to assign the charges such that the total charge equals a known target charge within a margin of error while maximizing the sum of the charge scores. We show that the problem is a variant of the well-studied multiple-choice knapsack problem and thus weakly \mathcal {NP} NP -complete. We propose solutions based on Integer Linear Programming and a pseudo-polynomial time Dynamic Programming algorithm. We demonstrate that the results obtained for novel molecules not included in the database are comparable to the ones obtained performing explicit charge calculations while decreasing the time to determine partial charges for a molecule from hours or even days to below a second. Our software is openly available.

Original language | English |
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Article number | 1 |

Pages (from-to) | 1-10 |

Number of pages | 10 |

Journal | Algorithms for Molecular Biology |

Volume | 14 |

Issue number | 1 |

DOIs | |

Publication status | Published - 5 Feb 2019 |

### Funding

This research was partially supported by the Netherlands eScience Center (NLeSC), Grant Number 027.015.G06, we thank all members of the NLeSC-ASDI project Enhancing Protein-Drug Binding Prediction for valuable discussions. We thank Ulrich Pferschy from the University of Graz for providing insight and expertise on the multiple-choice knapsack problem. We also thank Koen Visscher from Vrije Universiteit Amsterdam for providing the expert-supervised charge assignments. We acknowledge support by the Heinrich Heine University Duesseldorf.

Funders | Funder number |
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Heinrich Heine University Duesseldorf | |

Netherlands eScience Center | 027.015 |

## Keywords

- Integer Linear Programming
- Molecular dynamics simulations
- Multiple-choice knapsack
- Partial charge assignment
- Pseudo-polynomial Dynamic Programming