News

Work on Predicting Drug-Microbiome Interactions Published in ACS Pharmacology & Translational Science

We are excited to share our latest work on predicting drug-microbiome interactions using machine learning, now published in ACS Pharmacology & Translational Science: https://pubs.acs.org/doi/10.1021/acsptsci.5c00694. Congrats to Hrshita and all her co-authors on this work!

Review of Pairwise Learning for Molecular Property Prediction Published in Frontiers in Drug Discovery

We are excited to share that our review of pairwise learning for molecular property prediction has been published in Frontiers in Drug Discovery: http://dx.doi.org/10.3389/fddsv.2026.1859068. Congrats to Zach and Daniel on this work!

New Preprint on Machine Learning-Guided Discovery of Dye Nanoparticle Excipients

We are excited to announce a new preprint on our work "Fine-Tuned Machine Learning Models for the Discovery of Dye Nanoparticles with Enhanced Lung Delivery" where we paired machine learning with semi-automated robotic synthesis to discover FDA-approved dye excipients that stabilize high drug…

Marshall presented at GWDD poster session

Marshall presented a poster titled "Machine Learning Guided Combination Therapy Nanoparticle Design" at the Duke BME Graduation with Departmental Distinction Poster Session!

Sophia presented at CBTE Symposium

Sophia presented a poster titled "Pairwise Deep Learning for Predicting Drug-Drug Interactions" at the Center for Biomolecular and Tissue Engineering Symposium!

New Preprint on Physics and Deep Learning Guided Nanoparticle Design

We are excited to announce a new preprint on our work "Physics-informed design of drug-excipient nanoparticles via free energy calculation and yoked deep learning" where we combine the scalability of machine learning with the predictive power of molecular dynamics-derived free energy…

Rebeca presented at CQB

Rebeca gave a talk titled "Uncovering Nanoparticle Delivery to the Brain" at the Center for Quantitative BioDesign seminar series!

Sophia presented at Discover Engineering at Duke for NC SciFest

Sophia gave a talk titled ‘What do Ratatouille and a PhD have in common?’ providing an overview of life as a PhD student and the Reker lab to the general public

Lauren presented at CQB

Lauren gave a talk titled "A disease agnostic platform to modulate multi-drug nanoparticle formulation" at the Center for Quantitative BioDesign seminar series!

Presenting at SLAS

Daniel gave a talk entitled "Pairwise Molecular Learning Accelerates Drug Discovery & Nanoformulation Design" at the 2026 Society for Laboratory Automation and Screening Conference.

Zach presented at BME Impact Talks

Zach gave a chalk talk titled "Prodrug Design Powered by Machine Learning" for the BME Impact Talks series.

New Preprint on Prodrug Activation Prediction

We are excited to announce a new preprint on our work "Ensemble Siamese Neural Networks for Prodrug Activation Prediction" that develops and validates a tool to accurately anticipate if prodrugs will be activated within the body. It is now available on ChemRxiv: https://chemrxiv.org/engage/…

Sophia presented at Duke Research Cafe

Sophia gave a 3-minute lightning talk about her research for the Duke Research Cafe - why grapefruit juice and medicine don't mix.

Presenting at AAPS

Daniel gave a keynote presentation at American Association of Pharmaceutical Scientists (AAPS) PharmSci360 2025 about machine learning to accelerate, de-risk, and optimize drug development and drug delivery.

Work on Tunable Nanoparticle Design with AI Published in ACS Nano

We are excited to share that our tunable nanoparticle design work has been published in ACS nano: https://pubs.acs.org/doi/10.1021/acsnano.5c09066. Congrats to Zilu and all his co-authors on this work!