Purdue University scientists are using machine learning models to create new options for drug discovery pipelines. One-step multicomponent reaction with interpretable machine learning innovation to develop chemical library for drug discovery. Machine learning has been used widely in the chemical sciences for drug design and other processes. The models that are prospectively tested for new reaction outcomes and used to enhance human understanding to interpret chemical reactivity decisions made by such models are extremely limited. Purdue University innovators have introduced chemical reactivity flowcharts to help chemists interpret reaction outcomes using statistically robust machine learning models trained on a small number of reactions.

IMAGE: Purdue University is joining with other organizations for an initiative to accelerate global collaborative research on COVID-19 through access to high-quality, real-time multi-center patient datasets. WEST LAFAYETTE, Ind. - During the COVID-19 pandemic, health care professionals and researchers have been confined mostly to using local and national datasets to study the impact of comorbidities, pre-existing medication use, demographics and various interventions on disease course. Now, Purdue University is joining with other organizations for an initiative to accelerate global collaborative research on COVID-19 through access to high-quality, real-time multi-center patient datasets. The National Science Foundation has provided funding to develop the Records Evaluation for COVID-19 Emergency Research (RECovER) initiative. Researchers are testing predictions of artificial intelligence drug discovery platforms from the lab of Gaurav Chopra, an assistant professor of analytical and physical chemistry in Purdue's College of Science, on patient datasets across a network of health care institutions.

WEST LAFAYETTE, Ind. – One of the challenges in using machine learning for drug development is to create a process for the computer to extract needed information from a pool of data points. Drug scientists must pull biological data and train the software to understand how a typical human body will interact with the combinations that come together to form a medication. Purdue University drug discovery researchers have created a new framework for mining data for training machine learning models. The framework, called Lemon, helps drug researchers better mine the Protein Data Base (PDB) – a comprehensive resource with more than 140,000 biomolecular structures and with new ones being released every week. The work is published in the Oct. 15 edition of Bioinformatics.

WEST LAFAYETTE, Ind. – Increased adoption of cloud applications, such as Dropbox and Google Drive, by private users has increased concern about use of cloud information for cybercrimes such as child exploitation, illegal drug trafficking and illegal firearm transactions. Researchers at Purdue University have developed a cloud forensic model using machine learning to collect digital evidence related to illegal activities on cloud storage applications. "It is crucial to detect illegal cloud activities in motion," said Fahad Salamh, a PhD student in the Purdue Polytechnic Institute, who helped create the system. "Our technology identifies and analyzes in real time incidents related to these cybercrimes through transactions uploaded to cloud storage applications." Salamh worked on the technology with Marcus Rogers and Umit Karabiyik, professors in Polytechnic who specialize in computer and information technology.