KNIME Data Talks: Drug Discovery — From Hit Generation to the Clinic
KNIME Data Talks: Drug Discovery — From Hit Generation to the Clinic on June 1, 2022.
Speaker: Kenneth Longo (Wave Life Sciences Ltd)
Hear from Kenneth Longo how Wave Life Sciences is using KNIME in the early drug discovery process and combines well-established enterprise software solutions for molecule registration, cheminformatics and data acquisition. Find out how KNIME can save hours and days of work by standardizing and automating ETL, modeling and analytics processes.
Speaker: Emilie Pihan (Discngine)
Emilie demonstrates how KNIME is used at Evotec to improve the calibration step for structure-based virtual screening campaigns. Those steps taken using KNIME make the calibration easier, save time and improve the quality of the results.
Insights from Lead Optimization Efforts Using KNIME in Industry
Speaker: Thomas Kaiser (Avicenna Biosciences, Inc.)
Hear highlights of how KNIME has been integrated into drug development presented by Thomas Kaiser from Avicienna Biosciences. For example, how they have significantly improved translation of chemical or biological information into machine learning systems to successfully integrate ML, chemistry, and biology into a single environment.
Disclaimer: This recording could not be published.
Speaker: Aaron Hart (Idorsia Pharmaceuticals Ltd.)
The Resident-Intruder model is a standardized approach for monitoring stress and aggression levels in mice. Analyzing the video data from studies using this model is laborious, time-consuming and requires extensive manual scoring. Here Aaron Hart from Idorisa Pharmaceuticals Ltd. shows a method based on computer vision that helps automate this process. Orchestrated by KNIME, their solution is available to scientists on-demand, scores e.g. 10 hours of video in 30 minutes, and incurs zero idle costs.
Speaker: Robert Adams (Bayer Pharma AG)
Clinical data analysis is a manual and error-prone process where SAS is used as a standard proprietary software. Robert proposes KNIME as a powerful feature-complete and open source alternative for data preparation of clinical data transformation. He highlights the advantages of KNIME’s visual programming environment for pipeline optimization and automation, working with clinical data in a GxP regulated environment.
Speakers: RDKit (Greg Landrum), Vernalis (Stephen Roughley), OpenMS (Timo Sachsenberg)
Community extension developers introduce the software extensions they are currently working on and discuss the benefits of their most recent features. Timo highlights an example of analysis of MS data for proteomics with OpenMS nodes. Greg introduces extensive conformer generation and image generation for molecules with the RDKit nodes and Steve talks about additions to collection and binary compression nodes in the Vernalis extension.