Biotech and Pharmaceutical

Take your pharmaceutical research and development to the next level.

Today’s biotech and pharmaceutical industries operate in a highly sensitive environment. New medicines provide patients with fewer side effects, fewer hospitalizations, improved quality of life, and, most importantly, longer lives. At the same time, drug discovery and R&D are time-consuming, resource-intensive, and expensive processes that are also subject to strict regulatory oversight. Pharmaceutical productivity is declining due to high saturation rates at various stages of drug research and development. To support this complex process of turning a scientific promise into a marketable drug, industry experts are increasingly advocating for the consistent use of mathematical modeling.

MathWorks® and COMSOL® products allow biotech and pharmaceutical companies to advance the design and optimization of pharmaceutical production, develop algorithms, process multidisciplinary data, easily manage their entire workflows, and perform modeling and simulation for drug discovery and development.

Engineers use our products to:

  • Verify and optimize the design of biopharmaceutical products
  • Aanalyze how designs of biopharmaceutical products work in vivo
  • Evaluate clinical information with the help of modeling and simulation that allow researchers to acquire data that would otherwise be impossible to obtain with in vivo trials.
  • Develop virtual prototypes and digital twins
  • Study and analyze different biomolecules’ affects on a living mechanism.
  • Analise biological systems at the molecular level with biosensors.
  • Optimize the manufacturing process on a large scale.

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Products

COMSOL Multiphysics®
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COMSOL Compiler™
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COMSOL Server™
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Electromagnetics Modules
AC/DC
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Wave Optics
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Ray Optics
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Plasma
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Semiconductor
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Fluid Flow & Heat Transfer Modules
Mixer
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Polymer Flow
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Microfluidics
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Porous Media Flow
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Subsurface Flow
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Pipe Flow
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Molecular Flow
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Metal Processing
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Heat Transfer
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Structural Mechanics & Acoustics Modules
Structural Mechanics
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Nonlinear Structural Materials
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Composite Materials
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Geomechanics
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Fatigue
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Rotordynamics
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Multibody Dynamics
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MEMS
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Acoustics
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Chemical Engineering Modules
Chemical Reaction Engineering
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Battery Design
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Fuel Cell & Electrolyzer
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Electrodeposition
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Corrosion
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Electrochemistry
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Multipurpose Products
Optimization Module
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Uncertainty Quantification Module
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Material Library
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Particle Tracing Module
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Liquid & Gas Properties Module
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Interfacing Products
LiveLink™ for MATLAB®
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LiveLink™ for Simulink®
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LiveLink™ for Excel®
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CAD Import Module
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Design Module
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ECAD Import Module
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LiveLink™ for SOLIDWORKS®
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LiveLink™ for Inventor®
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LiveLink™ for AutoCAD®
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LiveLink™ for Revit®
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LiveLink™ for PTC® Creo® Parametric™
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LiveLink™ for PTC® Pro/ENGINEER®
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LiveLink™ for Solid Edge®
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File Import for CATIA® V5
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Trainings

Biotech and Pharmaceutical Training Curricula

  • Biomedical Image Processing Curriculum

  • Bioinformatics Curriculum

  • Biomedical Signal Processing and Data Analysis

  • Blogs and user stories

    Stay up to date with the industry's latest trends and learn the best pactices to innovate and optimize your engineering designs.


    Reduced Order Modeling based on Neural Networks

    Reduced Order Modeling Based on Neural Networks

    In this blog, we discuss what is Reduced Order Modeling(ROM), how ROM can be used to solve problems quickly and maximize your time and effort, and how you can create a reduced order model based on Neural Networks in MATLAB.

    Ai robotics working on a car

    AI: Driving the Industry Towards Greater Success

    Artificial intelligence (AI) is seen as a promising technology that can help leading OEMs to maintain their position as market leaders. In this post, read about how AI is changing the manufacturing sector, as well as its potential advantages and potential drawbacks.

    Deep Learning with MATLAB

    Today we are living in a renaissance of artificial intelligence, Machine Learning, and Deep Learning, and everyone wants to be a part of this movement. But the question is if you interested in using deep learning technology, where do you begin?