Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? This webinar featuring industry experts from Optibrium, Novartis and Collaborative Drug Discovery discusses how Inspyra uses dynamic learning to generate better compounds, allowing you to explore diverse chemistry or individual compound design. In this webinar, our panel of experts discuss their experiences of method development, real-life applications and the advances being made.

Topics include:

  • Nikolaus Stiefl, Director of Data Science at Novartis shares his experience gained whilst building a generative chemistry platform, including algorithms, backend and initial project applications. The use cases provide different insights into the pros and cons of this approach along with optimisation techniques.
  • Philip Cheung, Scientific Informatics Software Consultant at Collaborative Drug Discovery, explores some of the challenges faced when implementing novel machine learning models for generative chemistry – plus several interesting approaches taken to overcome them.
  • Matt Segall, CEO at Optibrium, discusses the challenges of applying generative chemistry methods in practice and describes how experienced chemists can guide generative methods to rigorously explore a wide range of optimisation strategies and more effectively target high-quality compounds for their projects.

Meet the speakers

Nikolaus Stiefl: Director of Data Science, Novartis

In 1999 Nik joined GlaxoWellcome for a one-year internship to learn more about what computer-aided drug design and cheminformatics means. In 2000, started his PhD studies in the lab of Knut Baumann in Wuerzburg being his first PhD student. Nik’s main interest was (and still is) QSAR and molecular descriptors as well as statistical validation. Nik spent his postdoc year with Eli Lilly, focussing on the implementation and application of reduced graphs for virtual screening in drug discovery. Joining Novartis in 2005 as a Research Investigator, Nik is now Director of Data Science, leading a team of experts in Machine Learning, and is project lead owner in a variety of technology projects with global teams and cross-organizational impact.

Nikolaus Stiefl

Philip Cheung: Scientific informatics software consultant, Collaborative drug discovery

Philip Cheung started his drug discovery career in the 90s, teaching robots how to do biology, building automation for drug discovery companies and academic institutions like the Institute for Systems Biology. He later moved onto work at Pfizer, where he helped build the Crystal Structure Database and the SBDD tools still used by Pfizer today. He later moved into the computation biology space, where he supported projects in Cancer and ophthalmology — including an NLP project that repositioned drugs from other indications into ophthalmology. After Pfizer, he moved onto Dart NeuroScience where he led the computational biology group for 10 years. For the last 4 years he’s been in the research group at CDD where he has been working on more machine learning tools.

Philip Cheung

Matt Segall: CEO, Optibrium

Matt has a Master of Science in computation from the University of Oxford and a PhD in theoretical physics from the University of Cambridge. As Associate Director at Camitro (UK), ArQule Inc. and then Inpharmatica, he led a team developing predictive ADME models and state-of-the-art intuitive decision-support and visualization tools for drug discovery. In January 2006, he became responsible for management of Inpharmatica’s ADME business, including experimental ADME services and the StarDrop software platform. Following acquisition of Inpharmatica, Matt became Senior Director responsible for BioFocus DPI’s ADMET division and in 2009 led a management buyout of the StarDrop business to found Optibrium.

The image shows Optibrium CEO Matthew Segall

Generative chemistry resources