The push for greater productivity and accelerated R&D has led to wider adoption of predictive tools in the lab. Join our short webinar series where experts in the field discuss how to apply ionization (Day 1) and NMR spectral prediction (Day 2) to support confident decision-making and extending their use in today’s digital world.
Day 1: Leveraging Ionization Data in Drug Discovery—A Deep Dive
Elevate your understanding of when and how to apply ionization predictions (calculated pKa values). Industry experts will share insights, case studies, and practical advice to guide decisions that depend on ionization modelling in drug discovery.
- Accuracy requirements differ in early screening, lead optimization, and candidate selection. Learn how to assess the dependencies.
- Prediction or measurement? Understand when you need to measure values, or train algorithms.
- AI-enabled innovation, done right—set your data science projects up for success by ensuring you employ the right descriptors and parameters effectively in SAR modelling.
- Industry collaborations are important to the continual development of prediction algorithms. See highlights of select projects and model validation studies.
- Calculation speed is important for library screening. Hear about the algorithmic changes that have made predictions up to 7-times faster.
Day 2: Driving Decisions from NMR Spectra
Do you predict NMR spectra to confirm or speed up interpretations of experimental spectra? Do you use or want to use automated structure verification (ASV)? Learn how to accelerate your workflows and get more from NMR prediction software. Join NMR experts for practical advice to boost your confidence, improve the accuracy of predicted spectra, and help you tackle more demanding problems. We will discuss:
- Predicting spectra in desktop applications or your browser
- When is training predictive algorithms important?
- Accurate prediction of 2D spectra and optimizing instrument time
- Dealing with mixtures and complex samples (including biosequencing)
- Application of NMR predictions to digitalize analysis workflows