To support MoDaC’s objectives, ATOM has released its best model and datasets into MoDaC’s repository for public users to use. The idea is for the user to re-use the machine learning models with ATOM's open-source ATOM Modeling Pipeline (AMPL) software to evaluate small-molecule compounds against selected targets for their drug discovery or chemical toxicology research.
In Michelle Arkin's, Ph.D., Ask the Expert interview with Lab Manager, she describes how ATOM is using Machine Learning and Artificial Intelligence to bring together data from public databases and from pharmaceutical partners to perform multi-parameter optimization on a drug target - coupled with ATOM's pipeline (AMPL) to do automated experiments.
The program aimed to teach the students how to write code, perform machine learning, analyze models, and understand assay development. They built the models using the open-source RuleBender software and AMPL (ATOM Modeling PipeLine) packages. Their models and the abundance of analyzed data advance ATOM’s modeling and molecular design program.
Purdue is answering the call for data science expertise with The Data Mine – a unique program that promotes learning from data and transforming it into important information to help solve real-world problems. The students, living in a community setting, partner on projects with established corporations.
