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Calculation of Polymer Hydrophobicity

Project Information

cleanup, git, github, optimization, python, research-software
Project Status: Recruiting
Project Region: CAREERS
Submitted By: Rob Mathers
Project Email: rtm11@psu.edu
Project Institution: Penn State-New Kensington
Anchor Institution: CR-Penn State
Project Address: New Kensington, Pennsylvania

Preferred Start Date: As soon as possible.

Mentors: Recruiting
Students: Recruiting
Student Skill Level Required: - Experience with Python
- Experience with or interested in learning Git and using GitHub

Project Description

After writing python code to calculate physical properties of polymer molecules in 2021, we are interested in cleaning up the code, addressing some calculation issues, and putting the code on GitHub. The code is written using an open-source cheminformatics package called RDKit. Prior to using RDKit, we had been using commercial software (Materials Studio, Chem3D) from 2014 to 2019.

The physical property of interest relates to hydrophobicity or the oil-like characteristics of polymers. Our method is inspired by the medicinal chemistry approach to describe drug-like molecules using partition coefficients. These coefficients, which are often referred to as LogP values, can be positive or negative. Positive LogP values indicate oil solubility while negative LogP values suggest water soluble molecules. Since the 1980s, the pharmaceutical industry has spawned many computational methods to calculate LogP.

Our method constructs SMILES strings for a short segment of a polymer. These SMILES strings represent 3D chemical structures using ACSII symbols. Then, we use RDKit to convert the SMILES string to a 3D molecule, optimize the conformation, and calculate the surface area (SA). Afterwards, we calculate LogP. The resulting ratio of LogP/SA has provided predictive capability in a number of collaborative projects. Since 2015, we have published 18 journal articles that use LogP and LogP/SA values.

Project Information

cleanup, git, github, optimization, python, research-software
Project Status: Recruiting
Project Region: CAREERS
Submitted By: Rob Mathers
Project Email: rtm11@psu.edu
Project Institution: Penn State-New Kensington
Anchor Institution: CR-Penn State
Project Address: New Kensington, Pennsylvania

Preferred Start Date: As soon as possible.

Mentors: Recruiting
Students: Recruiting
Student Skill Level Required: - Experience with Python
- Experience with or interested in learning Git and using GitHub

Project Description

After writing python code to calculate physical properties of polymer molecules in 2021, we are interested in cleaning up the code, addressing some calculation issues, and putting the code on GitHub. The code is written using an open-source cheminformatics package called RDKit. Prior to using RDKit, we had been using commercial software (Materials Studio, Chem3D) from 2014 to 2019.

The physical property of interest relates to hydrophobicity or the oil-like characteristics of polymers. Our method is inspired by the medicinal chemistry approach to describe drug-like molecules using partition coefficients. These coefficients, which are often referred to as LogP values, can be positive or negative. Positive LogP values indicate oil solubility while negative LogP values suggest water soluble molecules. Since the 1980s, the pharmaceutical industry has spawned many computational methods to calculate LogP.

Our method constructs SMILES strings for a short segment of a polymer. These SMILES strings represent 3D chemical structures using ACSII symbols. Then, we use RDKit to convert the SMILES string to a 3D molecule, optimize the conformation, and calculate the surface area (SA). Afterwards, we calculate LogP. The resulting ratio of LogP/SA has provided predictive capability in a number of collaborative projects. Since 2015, we have published 18 journal articles that use LogP and LogP/SA values.