July 20, 2023
Daniel presented "Novel Active Learning Strategies for Preclinical Research" at the Computer Aided Drug Design (CADD) Gordon Research Conference (GRC). A great opportunity to connect with others researching machine learning for drug design.
July 6, 2023
Our paper "Improving molecular machine learning through adaptive subsampling with active learning" has been published and is open access in Digital Discovery. Congrats to Holly Wen and the whole team on this work!
July 3, 2023
We are very excited to welcome our sixth PhD student Lauren! We are looking forward to all of your future exciting discoveries.
June 16, 2023
Congratulations to Chloe on becoming a T32 Pharmacological Sciences Training Program Fellow. This program provides two years of funding and valuable tranining and connections for students in the pharmacological sciences field.
June 12, 2023
Daniel presented "Molecular Machine Learning for Drug Discovery, Delivery, and the Microbiome" at the Computational & Systems Biology Annual Symposium at Colorado State University.
May 18, 2023
We had a great time making a fun video to explain our group's research for the biomedical engineering departmental retreat and recieved second place based on popular vote. Looking forward to enjoying our lunch party reward and doing it again next year!
May 13, 2023
Congrats to Ashley for graduating with her B.S. in Computer Science! We are looking forward to hearing about all your future successes.
April 30, 2023
Out of many college acceptances, Brian has decided to attend Princeton for his undergrad. We wish you the best of luck and know you will do great things!
April 11, 2023
New preprint from the lab: “DeepDelta: Predicting Pharmacokinetic Improvements of Molecular Derivatives with Deep Learning”
March 30, 2023
First NIH NOA for our lab, thank you for the support NIH's National Institute of Biomedical Imaging and Bioengineering!
March 21, 2023
We are happy to welcome Jung, a new research resident, to the lab. Looking forward to the exciting research you will do!
March 6, 2023
Daniel presented "Predicting and Improving ADMET Properties of Medications with Machine Learning" at the Molecular Engineering & Sciences Institute at the University of Washington.
February 19, 2023
New preprint alert from our lab! “Interpretable Molecular Property Predictions Using Marginalized Graph Kernels” https://chemrxiv.org/engage/chemrxiv/article-details/63ef9e761d2d184063a... Marginalized graph kernels are a new approach to quantify...
February 15, 2023
New preprint alert from our lab! “Improving Molecular Machine Learning Through Adaptive Subsampling with Active Learning” https://doi.org/10.26434/chemrxiv-2023-h8905 We and others had previously shown that active learning can learn on a fraction of...
November 15, 2022
The lab had a great time discussing quantitative biology of the microbiome at the Duke Center for Quantitative Biodesign Symposium today. We had two poster presentations from Amy and Hrshita and a talk from Daniel. Lots of fun and stimulation...