Machine learning methods for accelerated generative equipment design for new medicines

Thomas Ralph

Machine learning methods for accelerated generative equipment design for new medicines

Thomas Ralph

Strathclyde Institute Of Pharmacy And Biomedical Sciences

Research output: Contribution to conference › Poster

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Abstract

Simulation of the virus inactivation process within coiled reactors can be solved using multi -physics software such as COMSOL, Ansys Fluent, etc. These simulations can take up to weeks to finish processing, creating a bottleneck when trying to discover new medicines. The goal of this project is to design a variety of neural networks using machine learning which will aim to decrease virus inactivation simulation time.

Original language	English
Pages	32-32
Number of pages	1
Publication status	Published - 16 May 2022
Event	CMAC Annual Open Day 2022 - Glasgow, United Kingdom Duration: 16 May 2022 → 18 May 2022

Conference

Conference	CMAC Annual Open Day 2022
Country/Territory	United Kingdom
City	Glasgow
Period	16/05/22 → 18/05/22

Keywords

machine learning
virus inactivation process
simulation

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Ralph-CMAC-2022-Machine-learning-methods-for-accelerated-generative-equipment-designFinal published version, 1.31 MBLicence: Strath_1

Cite this

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Machine learning methods for accelerated generative equipment design for new medicines

Abstract

Conference

Keywords

Access to Document

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