3 research outputs found
Multistep Reaction Based De Novo Drug Design: Generating Synthetically Feasible Design Ideas
We
describe a “multistep reaction driven” evolutionary
algorithm approach to de novo molecular design. Structures generated
by the approach include a proposed synthesis path intended to aid
the chemist in assessing the synthetic feasibility of the ideas that
are generated. The methodology is independent of how the design ideas
are scored, allowing multicriteria drug design to address multiple
issues including activity at one or more pharmacological targets,
selectivity, physical and ADME properties, and off target liabilities;
the methods are compatible with common computer-aided drug discovery
“scoring” methodologies such as 2D- and 3D-ligand similarity,
docking, desirability functions based on physiochemical properties,
and/or predictions from 2D/3D QSAR or machine learning models and
combinations thereof to be used to guide design. We have performed
experiments to assess the extent to which known drug space can be
covered by our approach. Using a library of 88 generic reactions and
a database of ∼20 000 reactants, we find that our methods
can identify “close” analogs for ∼50% of the
known small molecule drugs with molecular weight less than 300. To
assess the quality of the in silico generated synthetic pathways,
synthesis chemists were asked to rate the viability of synthesis pathways:
both “real” and in silico generated. In silico reaction
schemes generated by our methods were rated as very plausible with
scores similar to known literature synthesis schemes