We are also researching in hybrid computing (classical-quantum) and Deep Learning for De Novo drug design by designing very efficient latent spaces to learn and predict novel drugs structures.
Choosing the right biological targets based on robust analytical methods that promote a solid disease understanding.
Improving the pharmaco-kinetics & pharmaco-genomics modelling to close the existing gap between preclinical models and clinical trials
Identifying the most responsive patient population
Designing diagnosis kits for personalized precision medicine.
DeepBioInsights aims to improve the drug design process with the use of AI and Enhanced Deep sampling methods and shortening the costs and time needed for new drug approval.
We also build genetic toolkits for precision medicine to speed up the diagnosis procedure by designing advanced medical decision systems to assess the treatment response and possible outcome of side-effects.
Integration of different types of data is imperative to have more robust decision-making procedures through the design and deployment of intelligent expert systems.
Our goal is to close the existing technological gap between AI, deep learning methodologies, and drug design.
Precision Medicine needs the help of Data Science and Applied Mathematics to gain deeper knowledge and insight from complex genomic databases and reducing the uncertainty in medical decision-making.
We believe in improving health care and delivering it at the lowest costs possible according to the highest quality and ethical standards by using AI.
Most drug research projects fail in the Phase-II of clinical trials aimed at evaluating the candidate drug's efficacy and safety in patient populations.
Our aim is to identify more precise sets of compounds for novel therapeutics and speed up their development and final approval.