What information do I need to justify my drug discovery software renewal?
Justifying software renewal requires more than highlighting features; you need quantifiable ROI data that resonates with budget holders. Whether it’s…
Surflex-QMOD integrates chemical structure and activity data to produce physically-realistic models for binding affinity prediction.
Here, we apply QMOD to a 3D-QSAR benchmark dataset and show broad applicability to a diverse set of targets. Testing new ligands within the QMOD model employs automated flexible molecular alignment, with the model itself defining the optimal pose for each ligand. QMOD performance was compared to that of four approaches that depended on manual alignments (CoMFA, two variations of CoMSIA, and CMF). QMOD showed comparable performance to the other methods on a challenging, but structurally limited, test set.
The QMOD models were also applied to test a large and structurally diverse dataset of ligands from ChEMBL, nearly all of which were synthesized years after those used for model construction. Extrapolation across diverse chemical structures was possible because the method addresses the ligand pose problem and provides structural and geometric means to quantitatively identify ligands within a model’s applicability domain. Predictions for such ligands for the four tested targets were highly statistically significant based on rank correlation.
Justifying software renewal requires more than highlighting features; you need quantifiable ROI data that resonates with budget holders. Whether it’s…
Set up for success with hands-on training sessions We start with dedicated training sessions where you and your team get…
Nearly all computational methods in the CADD field depend on parameters whose values are derived from various types of experimental…