Classic ML vs Agentic AI for Protein Classification in Drug Discovery

No more Classic ML projects—Welcome to Agentic ML projects. Why?

In this session, you will find out how modern Artificial Intelligence (AI) is transforming protein classification and drug discovery by comparing classic Machine Learning (ML) approaches with emerging Agentic AI systems. We will explore how classic ML models—such as Random Forest and XGBoost—compare with next-generation Agentic AI pipelines that leverage multi-step reasoning, tool-based workflow orchestration, scalability, productivity, and adaptive workflows, making them better suited for complex problems. We will walk through a real-world protein classification problem in drug discovery, demonstrating how each approach processes biological sequence data and evaluates predictive performance.

The session will include:

• A classic ML pipeline demonstration
• An Agentic AI assistant built using modern frameworks
• Performance comparisons and key insights

The shift from static ML pipelines to Agentic AI systems represents a major evolution in how AI solutions are built and deployed. This session provides a hands-on, engineering-focused perspective on transitioning from traditional modeling approaches to intelligent, reasoning-based AI assistants capable of accelerating scientific discovery.

This event is designed for:

• Machine Learning Engineers
• Data Scientists
• Bioinformatics and Healthcare Professionals
• Students and instructors
• AI Engineers building biotech systems

Agenda:
5:30 – 6:00 pm: Networking and refreshments
6:00 – 6:10 pm: Welcome message by Ernest Bonat, Ph.D. (Dr.B)
6:10 – 7:30 pm: Presentation and open discussions
7:30 – 8:00 pm: Networking. The building is required to be empty by 8:00 pm

Location
Entrepreneur Collaborative Center
2101 East Palm Avenue, Tampa FL 33605

Parking
Free parking is available in the lot located directly north of the ECC building. Please do not park directly adjacent to the ECC facility.

Refreshments
Refreshments are generously provided by Dr. Matthew Schabath, Moffitt Distinguished Scholar and Program Co-Leader and Senior Member, Cancer Epidemiology Program at the H. Lee Moffitt Cancer Center.

Speakers
1. Ernest Bonat, Ph.D.
Senior GenAI Engineer
Specialist in the design and development of Machine Learning systems and Agentic Artificial Intelligence assistants for bioinformatics, computational biology, and healthcare.
2. Paul London, M.S.
Molecular Technologist | Data Scientist
Blending 6+ years of lab and biotech experience with applied machine learning and data analytics to build reproducible pipelines and data-driven solutions that bridge science and technology. I’m currently exploring the intersection of computational methods, AI, and life sciences to solve complex problems in the life sciences.