Identifying new clotting factor XIa inhibitors in virtual high‐throughput screens using PCA‐GA‐SVM models and signature

Abstract

Blood Clotting Factor XI is an important actor in the clotting mechanism: it activates downstream zymogen involved in the clotting process. It can be targeted for activation or inhibition depending on treatment goals to enhance or inhibit clotting. In terms of antithrombosis treatment, Factor XI has emerged as a promising target to focus on. In this work, an iterative virtual high-throughput screening pipeline was proposed that can supplement current efforts to find inhibitors. The first iteration identified 11 compounds to test with 3 active for a hit-rate of 27.3%. The second iteration of the pipeline identified another 11 compounds to test with 7 active for a hit-rate of 63.6%. © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 2018.

Pharmaceutical Machine Learning: Virtual High-Throughput Screens Identifying Promising and Economical Small Molecule Inhibitors of Complement Factor C1s

“…It is interesting that the hit-rates of this study are comparable to our earlier work considering that the fraction of actives in this study was much smaller (0.118) than in previous studies (0.490 [ 63 ], 0.563 [ 64 ], 0.315 [ 81 ]). This is one indication that the pipeline and the models generated are robust and can be applied in situations where there is less data on the desired class, which are the majority of situations in drug discovery.…”