Toward in Silico Modeling of Dynamic Combinatorial Libraries

Dynamic combinatorial
libraries (DCLs) display adaptive behavior,
enabled by the reversible generation of their molecular constituents
from building blocks, in response to external effectors, e.g., protein
receptors. So far, chemoinformatics has not yet been used for the
design of DCLswhich comprise a radically different set of
challenges compared to classical library design. Here, we propose
a chemoinformatic model for theoretically assessing the composition
of DCLs in the presence and the absence of an effector. An imine-based
DCL in interaction with the effector human carbonic anhydrase II (CA
II) served as a case study. Support vector regression models for the
imine formation constants and imine-CA II binding were derived from,
respectively, a set of 276 imines synthesized and experimentally studied
in this work and 4350 inhibitors of CA II from ChEMBL. These models
predict constants for all DCL constituents, to feed software assessing
equilibrium concentrations. They are publicly available on the dedicated
website. Models rationally selected two amines and two aldehydes predicted
to yield stable imines with high affinity for CA II and provided a
virtual illustration on how effector affinity regulates DCL members.