A solid foundation for the future

New methodologies to design drugs with increased efficacy and minimized toxicity

  • Increase efficacy in ligand identification [1]
  • Method that optimizes pharmaceutical candidates selection by high-throughput docking [2]
  • A new alternative to screen large chemical libraries: Flexi-pharma [3]
  • Deep Learning model that accelerates drug discovery
  • Machine-learning-based analysis for Pharmacokinetics [4]
  • Toxicity prediction using artificial intelligence

1. Cavasotto & Aucar. Front. Chem. 8:246 (2020); 2. Scardino, Bollini, Cavasotto. RSC Adv. 11, 35383 (2021); 3. Lans et al. J. Comput. Aided. Mol. Des. 34, 1063 (2020); 4. Di Filippo, Bollini, Cavasotto. Front. Chem. 9:714678 (2021).

Our goal is to bring new drugs to the clinic through a highly optimized process by using in-house developed machine learning methods and in silico simulation and modeling.

Our primary focus is in neglected and emerging infectious diseases

From the 20 threats with more pandemic potential, stated by the World Health Organization, three are viruses from the Coronaviridae family: 

  • SARS-CoV-2 
  • SARS 
  • MERS

Two are viruses from the Flaviviridae family 

  • Zika 
  • Dengue (already endemic in America, and increasing)

Our aim is to find drugs against the whole virus family gathering efforts against several threats at the same time