Computational Biology and Bioinformatics
Computational Biology and Bioinformatics approaches play an increasingly vital role in drug discovery pipeline. By developing and applying of state-of-the-art computational methods, we aim not only to identify better targets for therapeutic intervention in human diseases, but also to accelerate the design and development of small molecules and new therapeutic modalities in the treatment of these diseases.
Specifically, we achieve these goals through approaches like machine learning, artificial intelligence techniques, data science methods, large-scale data mining, biostatistics, development and application of new computational methods and novel statistical models.
Examples of the most impactful discoveries made by PIs in this domain include:
- Development of new computational methods for identifying disease driving genetic alterations in highly repetitive regions of the genome.
- Identification of potential cancer-driving RNA modifications in patients with cancer through large-scale genome mining.
- Identification of target genes that influence the risks of complex diseases, and in parallel, predicting the disease risk based on an individual’s genetic profiles.
- Generative AI models to design mRNA therapeutics with exceptional stability and protein yields.
