How does Optibrium protect customer data?
The age of data The internet has transformed the way the world does business. As hardware and software have evolved,…
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.
The age of data The internet has transformed the way the world does business. As hardware and software have evolved,…
When are open-source drug discovery solutions a good fit? We should first clarify that when we’re speaking about open-source tools,…
What are parameters in machine learning models? The regular (non-hyper) parameters of an ML model are the numbers that it…