News

DST LAUNCH Grant

Thank you for the support Duke Science & Technology (DST) LAUNCH fund! We are excited for this grant and partnership with Professor Ru-Rong Ji to help us develop artificial intelligence tools for the design of longer lasting anesthetic nanoformulations.

Presenting at US-Japan Symposium on Drug Delivery Systems

Daniel presented at the 16th US-Japan Symposium on Drug Delivery Systems

Bert Presents at Anesthesiology Drug Discovery Day

Bert presented his work, titled "Anesthetic-Excipient Nanoparticles for Prolonged Analgesia" at the Anesthesiology Drug Discovery Day!

Presenting at BioTM

Daniel presented "Machine Learning for Drug Discovery and Delivery" at the 8th Bioengineering and Translational Medicine Conference.

Presenting at 2024 BMES Annual Meeting

We are excited to present again at BMES for the 2024 meeting. Details of talks and posters are shown below:

News and Views in Nature Computational Science

We are excited to share a News and Views article in Nature Computational Science about an exciting new study by Derek van Tilborg and Francesca Grisoni ( rdcu.be/dViua ) where we discuss the application of active learning with deep machine learning models: rdcu.be/dXOWX

Daniel Presenting at AAPS

Daniel presented an invited talk at the American Association of Pharmaceutical Scientists on "Machine Learning for Drug Discovery and Drug Delivery"

Presenting and Trainee Award at nanoDDS for Zilu

Zilu presented his work on a hybrid kernel machine to design tunable nanoparticles for drug delivery at the 22nd International Nanomedicine and Drug Delivery Symposium (NanoDDS 2024) and won a trainee travel award, congrats!

Welcome Lamis, Sophia, and Rebeca!

We are very excited to welcome three new members to our lab. Lamis is a NIH PRIME-PREP fellow, Sophia is a graduate student, and Rebeca is a post-doc. Looking forward to the exciting research from you all!

Work Using Paired Molecular Representations for Active Learning Published

We are excited to share that our work entitled "Finding the most potent compounds using active learning on molecular pairs" has been published in the Beilstein Journal of Organic Chemistry: https://www.beilstein-journals.org/bjoc/articles/20/185

Presenting at ACS 2024

Daniel gave 3 talks at the Fall 2024 ACS National Meeting.

News And Views Published in Nature Chemistry

We are excited to share a News and Views article in Nature Chemistry about an exciting new study by Lucy Colwell and colleagues ( https://www.nature.com/articles/s41557-024-01532-x ) where we discuss the the potential of machine learning to support peptide drug design: https://www.nature.com/…

Presenting at CRS 2024

Daniel presented two talks and a poster at the Controlled Release Society meeting.

Structure Augmented Modeling Published in PLOS Computational Biology

Excited to share "Data-driven learning of structure augments quantitative prediction of biological responses" has been published in PLOS Computational Biology. Thank you and congratulations to our collaborators in the You Lab at Duke!

Work Using Bounded Data to Classify Potency Improvements Published in RSC Med Chem

We are happy to share that our work entitled "Leveraging bounded datapoints to classify molecular potency improvements" has been published open source in RSC Medicinal Chemistry. Link: https://pubs.rsc.org/en/content/articlelanding/2024/md/d4md00325j