Generating drug candidates for preclinical evaluation

In complementing the possibilities offered by the genetic engineering techniques, the medicinal chemistry helps to obtain molecules with optimal performance.

The medicinal chemistry effort is centred around two different types of activities depending on the nature of the project considered.

  • A traditional Hit2Lead (H2L) and Lead Optimization (LO) approach for target-based projects with the support of computational chemistry for ligand-based design, virtual library generation and screening. Artificial Intelligence brings radical improvements to this process. Deep learning algorithms are used to further interpret the results generated by these libraries.
  • A synthetic biology approach for the secondary metabolites isolated from rare microorganisms and having an antimicrobial activity. This method relies on the Identification and characterization of novel Biosynthetic Gene Clusters (BGC). The expression of these genes clusters in proprietary bacterial chassis enables optimum production of the active natural product. The modification of the genes allows Biosynthesis and Structure Activity Relationship (BSAR) of the active molecules to improve their biological activities, physicochemical properties and safety profiles. Medicinal chemistry assists in studying the molecule structure, interpreting its activity and proposing ways of improving.

Both activities are generating candidates for preclinical evaluation to fight antimicrobial resistance.