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Overcoming the Trade-Off Between Speed and Accuracy in Computer-Aided Drug Design

Published on by s_mehta | Updated on

CADD in an industrial setting requires high accuracy with maximum throughput to drive the drug discovery process. The SILCS technology, in conjunction with the CGenFF program, attains this at an unprecedented level through the use of pre-computed FragMaps that can be used throughout the drug discovery and optimization process. This includes the ability to iteratively improve the predictability of the FragMaps during the optimization process as well as extract atomic detail contributions to ligand binding that can facilitate interpretation of experimental data, offering an additional competitive advantage.

The inclusion of contributions from protein or RNA flexibility, desolvation contributions of the protein and RNA as well as the ligands and ligand-protein interactions in the precomputed SILCS FragMaps yields the combination of high accuracy and computational efficiency. This information content offers the ability to identify novel allosteric sites, perform virtual screening through both pharmacophore and SILCS docking approaches, rapidly estimate relative ligand binding affinities without requirement of an experimental structure of the lead compound. Beyond small molecule drug development the comprehensive nature of the SILCS FragMaps allows for analysis of protein-protein interactions that may be combined with docking of excipients, buffers and ions to the full protein surface to guide the formulation of protein-based biologics including monoclonal antibodies.

Key Learning Objectives:

SILCS is based on pre-computation of an ensemble, termed FragMaps, offering a 1000-fold or more savings in time and resources over competing CADD technologies at a level of accuracy consistent with FEP.
Pre-computed FragMaps can be utilized iteratively throughout all aspects of a medicinal chemistry campaign including the ability to iteratively improve the predictability of the FragMaps.
SILCS FragMaps include contributions from protein flexibility and desolvation allowing for the identification of novel allosteric binding sites as well as taking into account local protein or RNA conformational changes to binding estimates.

Who Should Attend:

Computational chemists working in drug design and development
Medicinal chemists interested in applying an accessible CADD technology during ligand design
Formulation scientists working in protein-based biologics therapeutic development

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