We use AI to augment the existing and develop novel tools utilizing computational chemistry (molecular simulations and docking) and machine learning to identify/design and validate the therapeutic candidates that display improved molecular interaction patterns. One of our project focuses on the molecular glue degraders and kinases as hosts.
Traditional mutational analyses may not capture all perturbations to the key pathways, especially where large number of genes are involved. While exploring DNA spatial organization, we demonstrated that the structural DNA parameters are better predictors of DNA mutagenesis than DNA sequence. We aim to use information about mutations at DNA sequence (3nt or codon) and DNA shape (5nt) to better classify gastrointestinal cancers and their subtypes (here IPMNs vs PDAC) with a goal to predict therapeutic response. Our goal is to advance pre-malignancy and malignancy classification by developing computational tools that use structural (conformational) descriptors of the human genome.
In this project, our goal is to develop an intelligent, ML-derived framework addressing the challenge of gastrointestinal cyst stratification (here IPMNs of pancreas) particularly from samples with limited data space (e.g., mutations and gene expression only). We aim to provide a promising early detection and risk stratification tool for PDAC patients by utilization of the advanced ML tools and multilevel analyses. Wy apply hybrid ML - NLP approach to the mutational data analyses where we derived the text-based information from the mutational profiles of patients’ genomes using the NLP and advanced ML encoders. We further integrate the mutational profiles with codon alterations to guide future analyses of the structural consequences of the mutations on the key proteins and improved drug design.
Non-canonical DNA structures detected along gene promoters and other coding regions during transcription and/or replication suggest their importance and relevance for cancer development. During DNA replication, the switch from error-free to error-prone specialized polymerases helps overcome the risk of stopping replication at G-quadruplexes and creates the primary source to acquire mutations. Furthermore, bypassing damaged or non-canonical DNA is recognized as a major mutagen in many cancer types that may lead to drug resistance. Our group is interested in detection of the overlapping non-canonical secondary structures along the genetic material including but not limited to G-quadruplexes and R-loops (DNA-RNA hybrids). Despite their mutagenic potential, these DNA structures can serve as therapeutic targets and lead to cancer suppression.