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.

A knowledge-based approach to generating diverse but energetically representative ensembles of ligand conformers.

This paper describes a new and efficient stochastic conformational sampling method for generating a range of low-energy molecule conformations. Sampling can be tailored to a specific structural domain (e.g.

Structural unit analysis identifies lead series and facilitates scaffold hopping in combinatorial chemistry.

Combinatorial chemistry and high-throughput screening technologies produce huge amounts of data on a regular basis. Sieving through these libraries of compounds and their associated assay data to identify appropriate series for follow-up is a daunting task, which has created a need for computational techniques that can find coherent islands of structure-activity relationships in this sea. Structural unit analysis (SUA) examines an entire data set so as to identify the molecular substructures or fragments that distinguish compounds with high activity from those with average activity.