December 3, 2023
We are happy to share that our work on yoked learning for molecular data science has now been published in Artificial Intelligence in the Life Sciences: https://www.sciencedirect.com/science/article/pii/S266
October 27, 2023
Our DeepDelta work is now published in Journal of Cheminformatics, thank you to the excellent peer reviewers and everybody that has helped to make this manuscript better.
October 11, 2023
We are excited to present in full force for the 2023 BMES meeting. Details of talks and poster shown below:
September 28, 2023
We are thrilled to welcome another post-doctorial associate Chinmay Potnis! He recently recieved his PhD in Chemistry from the University of Louisville and we are excited to see what exciting discoveries here will make here.
September 25, 2023
Thrilled to see this work in print! Huge kudos to Natsuda Navamajiti for inventing bioinformatics-driven design of coatings for pancreatin-dependent drug release.
September 12, 2023
We happy to share a new review in Nature Reviews Drug Discovery: [
August 16, 2023
We are happy to share a new preprint on Yoked learning for molecular data science: https://chemrxiv.org/engage/api-gateway/chemrxiv/a
August 15, 2023
Daniel gave 2 contributed talks and 1 invited talk at the Fall 2023 ACS National Meeting. Details shown below:
August 8, 2023
We are happy to share a new paper that identified Angiopoietin-2 levels are predictive of capillary leak and subsequent edema formation following cardiac surgery. This was a great collaboration with Harvard Medical School and the University of Freiburg.
July 28, 2023
This Interpretable Molecular Property Predictions Using Marginalized Graph Kernels work is now published in JCIM, thank you to the excellent peer reviewers and everybody that has helped to make this manuscript better. Congratulations Yan et al!
July 26, 2023
Thank you for the support NIH's National Institute of General Medical Sciences (NIGMS)! We are excited for this major grant to help us develop innovative machine learning learning approaches to create novel drug delivery solutions.
July 21, 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 13, 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 14, 2023
Congrats to Ashley for graduating with her B.S. in Computer Science! We are looking forward to hearing about all your future successes.
May 1, 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 31, 2023
First NIH NOA for our lab, thank you for the support NIH's National Institute of Biomedical Imaging and Bioengineering! .
March 22, 2023
We are happy to welcome Jung, a new research resident, to the lab. Looking forward to the exciting research you will do!
March 7, 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 20, 2023
New preprint alert from our lab! “Interpretable Molecular Property Predictions Using Marginalized Graph Kernels” https://chemrxiv.org/engage/chemrxiv/article-details/63ef9e761d2d184063a...
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