This assistance can span a range of activities, including selection of representative HTS hits for Hit-to-Lead resynthesis, the design of individual compounds and lead-optimization libraries in early-stage lead optimization, or later-stage multi-parametric optimization to minimize potential ADME/TOX risks while maintaining activity.

BioPredict offers an established track record in applying state-of-the-art structure-based technology to the design of  compounds and libraries to increase activity.  Where multiple structures are available within a target class such as kinases, BioPredict has applied proprietary tools to not only optimize activity but also to optimize target specificity – a fundamental problem in many current target classes.  BioPredict scientists have used these methods successfully in past projects to:

• increase affinity of leading compounds by several orders of magnitude

• achieve exquisite target specificity to single targets

• achieve exquisite target specificity to dual desired targets

• identify alternative backup series patentably distinct from initial series

• increase the number of highly active compounds in a target series for SAR and patent purposes

• identify synthetic directions for compounds specific to a target subclass

When there is no structure available for the target protein or a close homolog,  BioPredicts scientists can design a synthetic strategy to explore a lead series SAR and to use it to optimize leads.  Ligand-based methods include derivation of three-dimensional pharmacophoric hypotheses, 3D superposition of molecules to develop of field-based 3D QSAR models, and application of learning-based methods.  Whether structure-based or ligand-based, lead optimization is intrinsically data intensive and proceeds best when it is hypothesis driven.  Computational methods become imperative as the number of compounds synthesized becomes more than a few dozen, not only to derive and evolve hypotheses but also to keep track of the diversity space already explored around a lead molecule,  to fold  explored space into existing hypotheses, and to identify portions of diversity space  not yet explored.  Later-stage lead optimization is multi parametric by nature.  Current drug discovery is an information-rich endeavor that can generate a wealth of information on affinity, ADMET, cross-reactivity, and more - all of which needs to be correlated with chemical structure to produce a viable clinical candidate.   BioPredict brings the experience and technologies required to tackle this data-driven paradigm effectively, bringing candidates to clinic faster providing detailed documentation for patent position and for filing.