Facilitating Target Identification Processes with AI: Challenges and Prospects

Life Sciences, Drug Discovery & Development, Fundamental Research,
  • Tuesday, June 07, 2022

Target identification has been an important challenge in developing therapeutics, significantly affecting its success rate. Thanks to the endeavour to address this issue and the recent drastic advance in technologies, an enormous amount of multilayered information surrounding the related biological entities has been accumulated. Artificial intelligence (AI) has been introduced as a promising tool to handle the abundant data and extract the information efficiently. AI is contributing to this area in many aspects based on diverse data sources.

Standigm has been focusing on diverse AI/machine learning (ML) approaches to identify novel targets. This has resulted in the construction of a highly flexible and modularised platform to streamline the target research processes.

This proprietary platform for target identification comprises four different approaches:

  • A deep learning model based on a knowledge graph
  • A Natural Learning Processing (NLP) engine extracting information from scientific literature
  • A random-walk model based on a modified protein-protein interaction (PPI) network and omics data
  • A genome-scale metabolic model incorporating genetic information

This system enables the users to get insights of novel drug targets and is currently contributing to initiate the company’s internal pipelines with minimised human intervention. Simultaneously, Standigm is actively evaluating the platform through diverse collaborations with pharmaceutical companies, biopharma companies, universities and research institutes.

Register for this webinar to learn about the introduction of AI-based approaches for target identification. Attendees will explore the potential opportunities and prospects of target identification processes with AI, including the importance of collaborative efforts.


Dr. Heejung Koo, , Standigm UK

Dr. Heejung Koo, Vice President of Strategic Alliances, Standigm UK

As a VP of Strategic Alliances of Standigm UK, Heejung Koo, PhD is focusing on establishing diverse collaborations regarding novel target identification and validation based on Standigm’s proprietary AI platform for target research. Prior to Standigm UK, Heejung was leading the Bio-Platform Team at Standigm Inc., contributing the conceptual design and application of AI-based target identification approaches at the biology domain in order to enhance and accelerate drug discovery. She received a PhD in systems biology from Pohang University of Science and Technology (POSTECH), applying systems biology technologies for biological interpretation of omics data. She is the co-author of several publications about AI target research. She has conducted collaborative target identification projects covering various indications including NASH, IPF and TNBC.

Message Presenter

Who Should Attend?

This webinar will appeal to researchers, scientists, academicians and professionals from pharmaceutical companies, biopharma companies as well as universities and research institutes with interests in adopting AI in target research for diverse indications.

Professional roles or responsibilities including, but not limited to:

  • Chief Scientific Officers
  • Project Managers
  • Principal Investigators
  • Research Scientists
  • Research Directors
  • Post-doctoral Researchers
  • Research Associates

What You Will Learn

Attendees will learn about:

  • Utilizing knowledge graph in target identification
  • Applying artificial intelligence (AI) to genome-scale metabolic models
  • Challenges and prospects of applying AI in drug target identification

Xtalks Partner


Standigm is a workflow AI-driven drug discovery company headquartered in Seoul, South Korea and subsidized in US and UK. Standigm has proprietary AI platforms encompassing novel target identification to compound design, to generate commercially valuable drug pipelines. Founded in 2015, Standigm has established an early stage drug discovery workflow AI to generate multiple First-in-Class compounds within seven months. Pursuing full-stack, AI-driven industrializing drug discovery, Standigm has achieved the automation of molecular design workflow, and the automation effort has been expanding to the whole drug discovery process based on Standigm AI platforms, including Standigm ASK™ for novel target discovery, Standigm BEST™ for novel compound generation.

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