TY - JOUR
T1 - Bioactive conformational biasing
T2 - A new method for focusing conformational ensembles on bioactive-like conformers
AU - Musafia, Boaz
AU - Senderowitz, Hanoch
PY - 2009/11/23
Y1 - 2009/11/23
N2 - Computational approaches that rely on ligand-based information for lead discovery and optimization are often required to spend considerable resources analyzing compounds with large conformational ensembles. In order to reduce such efforts, we have developed a new filtration tool which reduces the total number of ligand conformations while retaining in the final set a reasonable number of conformations that are similar (rmsd≤1 Å) to those observed in ligand-protein cocrystals (bioactive-like conformations). Our tool consists of the following steps: (1) Prefiltration aimed at removing ligands for which the probability of finding bioactive-like conformations is low. (2) Filtration based on a unique combination of two-/three-dimensional ligand properties. Within this paradigm, a filtration model is defined by its workflow and by the identity of the specific descriptors used for filtration. Thus, we developed multiple models based on a training set of 47 drug compounds and tested their performance on an independent test set of 24 drug compounds. For test set compounds after prefiltration, our best models have a success rate of ∼80% and were able to reduce the total number of conformations by 36% while maintaining a sufficiently large number of bioactive-like conformations and slightly increasing their proportion in the filtered ensemble. We were also able to reduce by 39% the number of conformations that are remote (rmsd > 2.5 Å) from the bioactive conformer (nonbioactive conformations). In accord with previous reports, prefiltration is shown to have a major effect on model performance. The role and performance of specific descriptors as filters is discussed in some detail, and future directions are proposed.
AB - Computational approaches that rely on ligand-based information for lead discovery and optimization are often required to spend considerable resources analyzing compounds with large conformational ensembles. In order to reduce such efforts, we have developed a new filtration tool which reduces the total number of ligand conformations while retaining in the final set a reasonable number of conformations that are similar (rmsd≤1 Å) to those observed in ligand-protein cocrystals (bioactive-like conformations). Our tool consists of the following steps: (1) Prefiltration aimed at removing ligands for which the probability of finding bioactive-like conformations is low. (2) Filtration based on a unique combination of two-/three-dimensional ligand properties. Within this paradigm, a filtration model is defined by its workflow and by the identity of the specific descriptors used for filtration. Thus, we developed multiple models based on a training set of 47 drug compounds and tested their performance on an independent test set of 24 drug compounds. For test set compounds after prefiltration, our best models have a success rate of ∼80% and were able to reduce the total number of conformations by 36% while maintaining a sufficiently large number of bioactive-like conformations and slightly increasing their proportion in the filtered ensemble. We were also able to reduce by 39% the number of conformations that are remote (rmsd > 2.5 Å) from the bioactive conformer (nonbioactive conformations). In accord with previous reports, prefiltration is shown to have a major effect on model performance. The role and performance of specific descriptors as filters is discussed in some detail, and future directions are proposed.
UR - http://www.scopus.com/inward/record.url?scp=72949085818&partnerID=8YFLogxK
U2 - 10.1021/ci900163t
DO - 10.1021/ci900163t
M3 - ???researchoutput.researchoutputtypes.contributiontojournal.article???
C2 - 19824683
AN - SCOPUS:72949085818
SN - 1549-9596
VL - 49
SP - 2469
EP - 2480
JO - Journal of Chemical Information and Modeling
JF - Journal of Chemical Information and Modeling
IS - 11
ER -