Hi, welcome to my page. My name is Nishanth Merwin and I'm currently a data scientist at BioSymetrics Inc., based out of Toronto. I specialize in machine learning for chemical and bioactivity data sets. Previously, I completed graduate research under Nathan Magarvey where I built AI/ML strategies for integrating multiomic data towards the goal of accelerating drug discovery (e.g. DeepRiPP). Many of the technologies I developed here were applied towards industrial drug discovery through my work with Adapsyn Bioscience.
I'm always happy to chat about the intersection of AI and biochemistry.
Skills
- Python 3
- Chemoinformatics (RDKit, CDK)
- Machine learning (Clustering, classification, etc.)
- Data visualization (ggplot2, D3.js, matplotlib)
- Full stack web development (Flask, Vue)
- Git version control
- NGS Data management and analysis (PacBio, Illumina)
- Wrangling around with software until I find a solution
Publications
- Merwin NJ, Mousa WK, Dejong CA, Skinnider MA, Cannon MJ, Li H, Dial K, Gunabalasingam M, Johnston CW, Magarvey NA. Proceedings of the National Academy of Sciences 2020; doi:10.1073/pnas.1901493116
- Skinnider MA, Johnston CW, Merwin NJ, Dejong CA, Magarvey NA. Global analysis of prokaryotic tRNA-derived cyclodipeptide biosynthesis. BMC Bioinformatics 2018; doi:10.1186/s12864-018-4435-1
- Skinnider MA, Johnston CW, Merwin NJ, Dejong CA, Magarvey NA. Global analysis of prokaryotic tRNA-derived cyclodipeptide biosynthesis. BMC Bioinformatics 2018; doi:10.1186/s12864-018-4435-1
- Mousa WK, Athar B, Merwin NJ, Magarvey NA. Antibiotics and specialized metabolites from the human microbiota. Natural Product Reports 2017; doi:10.1039/c7np00021a
- Skinnider MA, Merwin NJ, Johnston CW, Magarvey NA. PRISM 3: expanded prediction of natural product chemical structures from microbial genomes. Nucleic Acids Research 2017; doi:10.1093/nar/gkx320
- Skinnider MA, Johnston CW, Edgar RE, Dejong CA, Merwin NJ, Rees PN, et al. Genomic charting of ribosomally synthesized natural product chemical space facilitates targeted mining. Proceedings of the National Academy of Sciences 2016; doi:10.1073/pnas.1609014113