Pre-Clinical SIG: AI in Medicinal Products

PSI Mentoring 2026

Date: Ongoing 6 month cycle beginning late April/early May 2026 Are you a member of PSI looking to further your career or help develop others - why not sign up to the PSI Mentoring scheme? You can expand your network, improve your leadership skills and learn from more senior colleagues in the industry.

Joint PSI/EFSPI Visualisation SIG 'Wonderful Wednesday' Webinars

Our monthly webinar explores examples of innovative data visualisations relevant to our day to day work. Each month a new dataset is provided from a clinical trial or other relevant example, and participants are invited to submit a graphic that communicates interesting and relevant characteristics of the data.

PSI Training Course: Effective Leadership – the keys to growing your leadership capabilities

This course will consist of three online half-day workshops. The first will be aimed at building trust, the backbone of leadership and a key to becoming effective. This is key to building a solid foundation. The second will be on improving communication as a technical leader. This workshop will focus on communication strategies for different stakeholders and will involve tips on effective communication and how to develop the skills of active listening, coaching and what improv can teach us about good communication. The final workshop will bring these two components together to help leaders become more influential. This will also focus on how to use Steven Covey’s 7-Habits, in particular Habits 4, 5 and 6, which are called the habits of communication. The workshops will be interactive, allowing you to practice the concepts discussed. There will be plenty of time for questions and discussion. There will also be reflective time where you can think about what you are learning and how you might experiment with it.

AIMS SIG - Open-Source Lunch Bites

Our monthly webinar series allows attendees to gain practical knowledge and skills in open-source coding and tools, with a focus on applications in the pharmaceutical industry. This month’s session, “Graphics Basics,” will introduce the fundamentals of producing graphics using the ggplot2 package.

PSI Career Young Virtual Event (Q1 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Inclusive and Impactful Conference Contributions

This webinar brings together three bitesize complementary sessions to help PSI contributors create conference presentations and posters that communicate clearly and inclusively. Participants will explore how to refine their message, prepare materials effectively, and adopt practical habits that support confident, accessible delivery. A focused, supportive session designed to elevate every contribution.

PSI Career Young Virtual Event (Q2 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q3 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

PSI Career Young Virtual Event (Q4 2026)

This networking event is aimed at statisticians that are new to the pharmaceutical industry who wish to meet colleagues from different companies and backgrounds.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

Speaker	Biography	Abstract
Dr. Liam Childs	Dr. Liam Childs received his Doctorate in Natural Sciences (Dr. rer. nat.) from the Max-Planck Institute for Molecular Plant Physiology, with a research focus on machine learning and bioinformatics. He has over two decades of experience in artificial intelligence, data science, and bioinformatics and is currently the AI Working Group Lead at the Paul-Ehrlich-Institut in Germany, where he is responsible for developing and implementing the institute's AI strategy. Throughout his career, Liam has held multiple academic and industry positions, including postdoctoral fellowships in bioinformatics at the German Cancer Research Center (DKFZ) and Humboldt University, Berlin, as well as a role as a Senior Software Developer at Gotthardt Healthgroup.	Artificial Intelligence (AI) is increasingly integrated into the development and regulation of medicinal products, offering new opportunities in drug discovery, clinical trials, and manufacturing. AI-driven approaches, particularly machine learning and bioinformatics, facilitate target identification, optimize patient selection in clinical trials, and enhance process control in pharmaceutical manufacturing. In the context of biomedicine, AI plays a critical role in predicting drug-target interactions, modeling peptide-MHC binding for immunotherapy, and improving the precision of CRISPR-Cas9 genome editing. Despite its potential, AI applications in medicinal products present challenges, including data quality, algorithm validation, and regulatory compliance. The necessity for robust performance metrics and risk assessment frameworks is paramount to ensure the reliability and safety of AI-driven methodologies.

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