Workshop on Innovations in Biomaterials Science

Machine Learning and Artificial Intelligence in Biomaterials Design and Development

October 19, 2021
11:00 am EDT

Machine learning (ML) and artificial intelligence (AI) have been transformative in areas of chemistry and materials in which large datasets have or can be generated. However, when data is scarce and expensive, as is the case for biomaterials design, computational modeling has been limited. Computer-aided design of biomaterials often relies on ab initio modeling (e.g., molecular dynamics), which require no data but cannot predict complex properties such as biomaterial mechanics, optical properties, or in vitro or in vivo behavior or efficacy. Thus, human intuition coupled with experimental trial and error is often state-of-the-art for design of new biomaterials. These conventional approaches to develop biomaterials are expensive and laborious and have significantly slowed the translation of new biomedical devices into clinical practice. To address this hurdle, innovative approaches include the use of ML and AI design with a long-term goal of a (bio)materials genome.  This webinar will overview the broader field of ML and AI and how advances in computational power coupled with big data analysis and innovations in machine learning, modeling, and simulation are enabling artificial intelligence to revolutionize biomaterials design and development.
  

Program

  • Anti-biofouling Monolayers: The Mechanism and Molecular Designs
    Tomohiro Hayashi
    , Tokyo Institute of Technology
      
  • Learning to Control AI Models for Accelerated Delivery
    Payel Das, AI Science, Thomas K. Watson Research Center, IBM Research
      
  • Coming Soon
    Debora Marks
    , Blavatnik Institute of Systems Biology, Harvard Medical School
      
  • Iterative Peptide Biomaterials Discovery with Maximum Entropy Methods and Deep Learning
    Andrew White
    , University of Rochester
      

Moderators


Danielle Benoit, University of Rochester

Danielle Benoit is a professor within the Department of Biomedical Engineering with appointments in Chemical Engineering and the Center for Musculoskeletal Research and is also the Director of Materials Science Program at the University of Rochester.  She directs the Therapeutic Biomaterials Laboratory, which specializes in the rational design of polymeric materials for regenerative medicine and drug delivery applications.  Her work has provided insights into the translation of tissue engineering strategies for bone allograft repair, development of pH-responsive nanoparticles for nucleic acid and small molecule drug delivery, and novel targeting strategies for bone-specific delivery of therapeutics. Benoit has been recognized by numerous awards and accolades for her research program including 2019 Class of AIMBE Fellows, the 2018 University of Maine Distinguished Alumni Award, the 2016 Kate Gleason Young Engineer of the Year Award, a 2015 Young Innovator Award in Cellular and Molecular Bioengineering, an NSF CAREER Award, and Alex’s Lemonade Stand Young Investigator Award. She is also a standing member of the NIH Biomaterials and Biointerfaces Study Section. Benoit received her undergraduate degree in Biological Engineering from the University of Maine and MS and PhD degrees in Chemical Engineering from the University of Colorado.  She then trained at the University of Washington where she was the Merck Fellow of the Damon Runyon Cancer Research Foundation. Benoit joined the faculty at the University of Rochester in 2010. 
  


Andrew White, University of Rochester

Andrew White graduated from Rose-Hulman Institute of Technology in 2008 with a BS degree in chemical engineering. While at Rose, he spent a year studying at the Otto-von Guericke Universität and the Max Planck Institute for Dynamics of Complex Technical Systems in Magdeburg, Germany. White completed a PhD degree in chemical engineering at the University of Washington in 2013. Next, White worked with Professor Greg Voth at University of Chicago as a postdoctoral fellow in the Institute for Biophysical Dynamics from 2013-2014. In Chicago, he developed new methods for combining simulations and experiments. White joined the University of Rochester in Chemical Engineering in 2015 and is currently an associate professor. He has joint appointments in the Chemistry Department, Biophysics, Materials Science, and Data Science programs. White received a National Science Foundation CAREER award in 2018 and an Outstanding Young Investigator Award from the National Institutes of Health in 2020. White has authored a textbook on deep learning for molecules and materials, which is freely available at https://whitead.github.io/dmol-book.

 

Speakers

Payel Das

Payel Das, AI Science, Thomas K. Watson Research Center, IBM Research

Payel Das is a principal research staff member and a manager at IBM Research AI, IBM Thomas J Watson Research Center. She has also been an adjunct associate professor at the department of Applied Physics and Applied Mathematics (APAM), Columbia University. She received her PhD degree in theoretical biophysics from Rice University. Currently, she leads research on trustworthy AI in the low-data regine and machine creativity. A central focus is on developing controllable generative AI and efficient black-box optimization techniques. The goal is to enable reliable modeling of complex systems and efficient synthesis of novel and useful designs for various downstream business and scientific applications, including drug discovery and material design. Das has served as the editorial advisory board member of the ACS Central Science Journal. She is the recipient of two IBM Outstanding Technical Achievement Awards (the highest technical award at IBM), two IBM Research Division Awards, one IBM Eminence and Excellence Award, and six IBM Invention Plateau Awards.

 


Tomohiro Hayashi, Tokyo Institute of Technology

Tomohiro Hayashi received his PhD degree in 2003 from Ruprecht-Karls-Universität Heidelberg. He joined Tokyo Institute of Technology in 2003 as a postdoc researcher and was promoted to an associate professor in 2010. His specialist areas are surface and interface science, scanning probe microscopy, materials informatics, and computer simulations. He has been awarded 11 academic prizes, including the Asahi Kasei award of the Society of Polymer Science, Japan (2011). A summary of his activities is given at http://lab.spm.jp/.