How have you leveraged artificial intelligence in your biotech research? What's one unexpected way AI has enhanced your work?

Good Day, AI has become an integral part of my bio tech research, in particular for the analysis of large genomic data sets. What used to take weeks of manual work, AI does in hours we are seeing it identify patterns or potential drug targets which we may have over looked. Also we were very surprised at how well AI performed at predicting protein structures we put it in from of out early stage drug development and while we know it is not perfect, it also has helped us eliminate a lot of dead ends. It's a mixed bag but it definitely has improved our overall focus and efficiency of our processes which in turn has altered how we approach design of the actual experiments. If you decide to use this quote, I'd love to stay connected! Feel free to reach me at gregorygasic@vmedx.com and outreach@vmedx.com.

One of the most transformative but under-discussed uses of AI in biotech is in the preprocessing stage, specifically, preparing and labeling data for machine learning models. In fields like medical imaging or molecular analysis, the ability to consistently and accurately annotate vast datasets can make the difference between a breakthrough and a bottleneck. AI has enhanced this work in unexpected ways. In computer vision, for instance, automated pre-labeling can now identify cell structures, mark anomalies in microscopy images, or highlight regions of interest in MRI scans before a human expert reviews them. This doesn't replace medical expertise, instead, it speeds up the process, allowing specialists to focus on validation and deeper analysis. In biotech research, where every hour in the lab can be costly, this acceleration can shave weeks off experimental timelines. Another unexpected advantage is standardization. Human annotators can introduce subtle variations in labeling, but AI-assisted workflows can maintain consistent criteria across thousands of samples. This is critical for reproducibility, especially in collaborative, multi-site research. The takeaway: in biotech, AI's role isn't just in analyzing results, it's in making the research pipeline itself faster, more consistent, and more scalable. By combining automated pre-labeling with human verification, biotech teams can increase throughput without sacrificing accuracy, paving the way for faster innovation.

As a BD for a biopharma lab- and bio-IT service provider, I come into contact with a huge variety of therapies, diseases, compounds, etc... Even with a strong scientific basis, it can take time to really understand what my clients are working with, and what they need for their projects. AI has helped me to find clear, understandable answers to complex questions, allowing me to participate more easily in discussions.

Automated data processing reduced the process of finding out about funding and trends related to investment in biotech. Predictive modeling helped in market potential of the emerging therapies. The automated financial forecasting enhanced allotment of resources to research projects. The unexpected relationship between the innovations of biotech industries and investor behavior was disclosed through machine learning algorithms. Superior decision making fast-tracked the strategies of funding and growth of portfolio.