Join us on 19 June at 4:00 PM BST | 11:00 AM EDT | 8:00 AM PDT.

Binding affinity prediction continues to be a challenge in computer-aided drug design, especially in the absence of a high-quality target structure. To address this limitation, we present QuanSA, a 3D QSAR-based method that leverages machine learning algorithms to accurately estimate binding affinities in the absence of protein structural data and with limited ligand activity information.

Join Himani Tandon, PhD, to explore the theory and methodology behind QuanSA, see case study examples that demonstrate its application in lead optimisation scenarios, and learn how you can:

• Develop physically realistic and causal models for affinity prediction
• Extrapolate outside the chemical space of your training set
• Iteratively refine the accuracy and domain coverage of existing models
• Build informative models by leveraging the effective conformational search and accurate multiple structure alignment
• Incorporate confidence metrics to identify novel active compounds with greater reliability
• Enhance the performance of free energy perturbation (FEP+) calculations using an integrated approach, to improve resource management and decision making.