Sameer Soi
VP, Data & AI Abalone Bio
With experiences spanning health tech, synthetic biology, and drug discovery, Dr. Soi has spearheaded initiatives leveraging data and machine learning to drive impact for patients.
At Atomwise, as Director of Drug Discovery Data Science, he led the development of uncertainty quantification methods for deep learning models of small molecule binding as well as tools for evaluating and improving similarity-based searches for hit-to-lead generation.
At Zymergen, he managed the productionization of statistical and machine learning models that supported the company’s portfolio of high- throughput screening projects. Before that, at Grand Rounds (now Included Health) he engineered a platform enabling the scaling of machine learning for clinical outcomes models across indications for which he received a patent.
Seminars
- How can dynamic modeling, data integration, and cross-disciplinary collaboration be connected to create a truly cohesive GPCR discovery ecosystem?
- What operational barriers must be overcome to achieve genuine AI-enabled translational science across academia and industry?
- What does the roadmap look like for structure-guided and AI-assisted GPCR discovery to reshape drug development over the next decade?
- Identifying GPCR antibody agonists from millions of NGS reads by learning sequence-activity maps with fine-tuned protein large language models
- Integrating internal and public datasets to evaluate antibody developability to go beyond activity and increase the probability of success
- Designing novel antibodies with high activity and superior developability by leveraging generative models to manage key trade-offs
