AI for Fundamental Biology: Insights into RNA & Genetically Coded Protein Synthesis

The genetic code is at the center of all life on earth as it specifies how information encoded in RNA is decoded into sequences of amino acids, i.e proteins. My research is exploring whether the genomes of extant organisms can provide insights into how the genetic code emerged, such that information encoded in RNA is decoded by cellular machinery into proteins. An important strategy in this research is the abstraction of genome sequences into "text", making it possible to leverage rapid advances in AI/ natural language processing (NLP) algorithms in tackling this challenge. The results of this work could have implications on our understanding of the earliest stages of life on earth and provide insights for the engineering of RNA for medical and non-medical applications.   

AI for Drug Discovery

My research is exploring the use of various AI-based approaches for the de novo design of small molecule drugs with a focus on preemptive design of broad-spectrum antiviral therapeutics against a wide range of viruses including SARS-CoV-2. Another area of focus is on using AI-based approaches to enhance safety, efficacy and equitable access of gene-based therapeutics including RNA medicines. 

AI in Clinical Medicine and Global Health

My research on AI as applied to clinical medicine and global health is focussed on developing computational models for predicting disease risk and progression using widely available laboratory test results combined with insights from human genetics and microbiome studies. Disease areas of focus include gastrointestinal diseases such as inflammatory bowel disease (IBD), GI cancers and viral hepatitis. 

 

Another research focus area is on developing strategies for enhancing equitable development of AI for medical applications through open innovation and adoption of computing frameworks that enhance privacy, security, auditability and verifiability of AI models.