Ramakrishnan Natesan
Principal Data Scientist Amgen
Ramakrishnan Natesan is a Principal Data Scientist at Amgen, where he applies computational and data‑driven approaches to biologics discovery and early development. An interdisciplinary scientist by training, he focuses on integrating biophysics‑based modeling with machine learning to predict molecular attributes, de‑risk developability liabilities, and improve candidate selection. Ramakrishnan holds a PhD from the Indian Institute of Technology Madras and has published extensively across quantitative biology and protein therapeutics.
Seminars
As the conference closes, this panel will summarize technical and scientific insights to assess how far the computational and AI/ML space has progressed in biologics design and what must evolve by 2027 to confidently derisk early CMC and formulation decision-making using AI-driven approaches.
- As AI models are increasingly used to generate and prioritize biologics candidates, how much mechanistic or biophysical understanding is needed to advance molecules with confidence, particularly as programs approach early CMC?
- Based on the use cases shared across the last couple of days, where have predictive models clearly enabled better decisions, and where do computational x AI metrics still fall short of delivering scientific or manufacturing confidence?
- At which points do AI-driven insights most often break down between in silico design, wet-lab validation, and early CMC?
- Do we think that confidence scores and model rankings meaningfully reduce experimental burden, or simply reshuffle risk for liabilities like stability, aggregation, viscosity, and immunogenicity?
- How should discovery teams balance speed and exploration with the need for traceability and rigorous scientific justification when progressing biologics candidates?
- Looking to 2027, what evidence will be required for AI to be trusted to prioritize or terminate biologics programs ahead of major early CMC investment?
- Are current limitations in interpretability and confidence temporary, or enduring constraints that will continue to shape AI’s role in biologics R&D?
- Multiscale modeling enables us to think beyond single structure-based predictions for mAbs
- Bottom-up molecular dynamics approaches could be the key tool that connects molecular structure to its site-specific and bulk attributes
