Brian B. Masek

NovoFLAP: A Ligand-Based De Novo Design Approach for the Generation of Medicinally Relevant Ideas

NovoFLAP is a computer-aided de novo design tool that generates medicinally relevant ideas for ligand-based projects. The approach combines an evolutionary algorithm (EA-Inventor) with a powerful ligand-based scoring function that uses both molecular shape and pharmacophore features in a multiconformational context (FLAP). We demonstrate that NovoFLAP can generate novel ideas that are not only appealing to design scientists but are also validated by comparison to compounds known to demonstrate activity at the desired biological target. NovoFLAP provides a novel computer-aided design technique that can be used to generate ideas that maintain desirable molecular attributes, such as activity at the primary biological target, while offering opportunities to surmount additional design challenges. Application to the design of the first nonbasic 5HT(1B) antagonist is presented.

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., peptides) by extracting torsional profiles from specific datasets and subsequently applying them to target molecules outside the reference set. The programs that handle creation of the knowledge-based torsional profiles and conformer generation per se are separate and so can be used independently or sequentially, depending on the task at hand. The conformational ensembles produced are contrasted with those generated using local minimization approaches. They are also quantitatively compared with a broader range of techniques in terms of speed and the ability to reproduce bound ligand conformations found in complexes with proteins.

Muse+TriposScore: a ligand-based de novo design approach

Successful drug discovery often requires optimization against a set of biological and physical properties. We describe our work on multi-parameter approaches to ligand-based de novo design and studies that demonstrate its ability to successfully generate lead hops or scaffold hops between known classes of ligands for some example receptors. We describe a multi-criteria scoring function incorporating molecular shape similarity, molecular fingerprint similarity, and a number of popular “Lipinski-like” molecular properties. Muse is based on an evolutionary algorithm that operates on an initial population of structures to invent new structures with improved scores. Muse is unique in that it has the ability to work with any user-defined scoring function that provides results in the form of a numerical evaluation of designed structures. Several retrospective studies on various targets demonstrate the ability of the above mentioned approach to generate novel ideas that are not only appealing to design scientists but are also validated by comparison to compounds known to demonstrate activity at the desired biological target. Specific examples where this approach has generated either significant modifications of existing molecular frameworks or structurally new molecular templates relative to design starting points (i.e., lead hopping) will be provided.