High Throughput Discovery

The automation of physical experiments through robotics has resulted in a scaled-up discovery cycle. High throughput screening and combinatorial chemistry offer access to huge sets of candidate compounds; however, time and economic considerations require a selection of only a subset of this vast space for physical testing. MOE provides a complete methodology for analyzing large HTS datasets and using the analysis to design focused combinatorial libraries.

VSA Descriptors MOE’s VSA descriptors are surface area based descriptors for logP, molar refractivity and partial charge properties. These descriptors show weak inter-descriptor correlation and correlate highly to many physico-chemical properties. The VSA descriptors are based on 2D connection tables making them ideal for HTS QSAR.

HTS-Binary QSAR MOE’s patented Binary QSAR methodology is ideal for building pass/fail models from high error content data and standard molecular descriptors. Use the resulting probabilistic models (based on Bayesian statistical inference) as a biasing agent in the design of focused combinatorial libraries.

Focused Combinatorial Library Design Design focused libraries using a product-based methodology that ranks individual reagents according to likelihood that they are part of an active compound. The ranking can be based on various types of models including linear and binary QSAR, fingerprint, pharmacophore and composite models. A Monte Carlo technique is used to avoid enumeration of the library allowing for reagent ranking in extremely large virtual libraries.

Diverse Combinatorial Library Design Use a Monte Carlo sampling technique to design large diverse combinatorial libraries. With this product-based methodology, full enumeration of the virtual library is avoided making it possible to extract diverse subsets when the chemistry space is extremely large.

Combinatorial Library Enumeration Build moderately sized combinatorial libraries in MOE using a combinatorial library enumerator. Symmetric substitution, peptide substitution, bidentate connections and ring creation are supported (with appropriate treatment of chirality). Compounds are output to a MOE molecular database for subsequent visualization and analysis.

RECAP Analysis and Synthesis Analyze large collections of compounds to produce fragments resulting from retrosynthetic rules. Use the resulting fragments in a de novo synthesis methodology to produce novel chemical structures that have an increased likelihood of synthetic accessibility. Specify heavy atom mean and variance to control the size distribution on the randomly generated structures. Apply leadlike/druglike, QSAR/QSPR predictive models or 3D pharmacophore filters for de novo virtual screening applications.