Style Editor Docs
     
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Trainings
    • Upcoming Events
  • About
    • DataHow
    • Team
    • Careers
    • Contact
   info@datahow.ch
  • Home
  • Products & Services
    • DataHowLab
    • Professional Services
    • Innovation Hub
  • Resources
    • Technology
    • Publications
    • Bioprocess Blog
  • Education & Events
    • Schedule events & trainings
    • Trainings
    • Upcoming Events
  • About
    • DataHow
    • Team
    • Careers
    • Contact
    Publications  ·  01. March 2021

    Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation

    Full title: 

    Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation 

     

    Abstract:

    Successful biologics must satisfy multiple properties including activity and particular physicochemical features that are globally defined as developability. These multiple properties must be simultaneously optimized in a very broad design space of protein sequences and buffer compositions. In this context, artificial intelligence (AI), and especially machine learning (ML), have great potential to accelerate and improve the optimization of protein properties, increasing their activity and safety as well as decreasing their development time and manufacturing costs. We highlight the emerging applications of ML in biologics discovery and development, focusing on protein engineering, early biophysical screening, and formulation. We discuss the power of ML in extracting information from complex datasets and in reducing the necessary experimental effort to simultaneously achieve multiple quality targets. We finally anticipate possible future interventions of AI in several steps of the biological landscape.

    Full Publication
    tagPlaceholderTags:

    Write a comment

    Comments: 0
    draggable-logo

    Hagenholzstrasse.111 / 8050 Zurich/ info@datahow.ch 


    Schedule a Demo

    Edit here your navigation button
    is-switcher admin-only
    is-switcher admin-only
    is-switcher admin-only

     

    Main colors
       bg-primary
       bg-primary-light
       bg-primary-dark
       bg-secondary
       bg-secondary-dark
    Template sections
       body
       top-header
       header
       content
    Footer Styles
       background
       text color
       link color
       horizontal line
    Buttons
       style 1
       style 2
       style 3
    Other elements
      social icons
      navigation color
      subnav background
    Mobile navigation
       background color
       navigation color
    Template configurations
    g-font
    navigation styles
    size-17 weight-400
    content styles
    form-white
    footer styles
    o-form color-white
    Typography
    Heading H1
    weight-600
    Heading H2
    weight-600
    Heading H3
    weight-600
    Buttons
    weight-600 is-uppercase
    Animations
    wow animated fadeInUp

    Note:
    All changes made here will be applied to your entire website.

    About | Privacy Policy | Cookie Policy | Sitemap
    © Copyright 2022 - DataHow - All rights reserved
    Log out | Edit
    • Scroll to top