Update Events “accelerate-lunchtime-seminar-series-5”

Author: bp374

_events/accelerate-lunchtime-seminar-series-5.md

@@ -3,7 +3,7 @@ layout: event-single
 title: Accelerate Lunchtime Seminar Series
 start: October 27, 2025 12:00 PM
 end: October 27, 2025 1:00 PM
-image: /assets/uploads/2025-10-27-lunchtime-seminar.png
+image: /assets/uploads/2025-10-27-lunchtime-seminar-2-.png
 ---
 Join us to find out more about research taking place in AI for Science across the Accelerate Science community.
 
@@ -17,10 +17,10 @@ Dr Sergio Bacallado de Lara, Department of Pure Maths and Mathematical Statis
 
 The scarcity of high-quality public data presents a major hurdle for applying deep learning to drug discovery. This is particularly acute in the search for new antibiotics, where active compounds are rare. In this talk, I will discuss how my group has addressed this “low-data” challenge by applying transfer learning with deep graph neural networks (DGNNs) to discover novel antibacterials. By pre-training models on large, general chemical datasets before fine-tuning them on small, specific antibacterial screens, we were able to virtually screen over a billion compounds. Supported by a £15,000 grant from the Accelerate Programme, this computational work led to the experimental validation of several potent, low-toxicity compounds with broad-spectrum activity against critical ESKAPE pathogens, achieving a ~54% hit rate from our prioritized candidates. This project highlights a broader story about bridging the gap between computational theory and experimental practice. I’ll touch upon the significant challenges in developing robust benchmarks for molecular machine learning, a critical step for ensuring our models are truly learning meaningful chemical principles. Furthermore, I will share how our group, based in the maths faculty, collaborated with experimental facilities to validate our computational hypotheses, turning predictive models into tangible results. My goal is to encourage others in the Cambridge AI community to develop methodologies that are not only computationally novel but are also geared towards practical, hypothesis-driven validation, demonstrating how even modest funding can generate significant real-world impact.
 
-**Talk Title TBC**
+**The Denario Project: Multi-Agent Systems for Autonomous Scientific Discovery**
 
 Dr Boris Bolliet, Department of Physics
 
-*Abstract coming soon.*
+We present Denario, an AI multi-agent system designed to be a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the literature, developing research plans, writing and executing code, making plots, and drafting a scientific paper. Denario is built as a modular system, and therefore, can perform either very specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent (<https://github.com/CMBAgents/cmbagent>) as a deep-research backend. We describe Denario and its modules in detail and illustrate its capabilities by presenting multiple AI-generated papers generated by it. These papers cover many scientific disciplines, such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, and planetary science. Denario can also perform research combining ideas from different disciplines. We publicly release the code at <https://github.com/AstroPilot-AI/Denario>. A Denario demo can also be run directly on the web at <https://huggingface.co/spaces/astropilot-ai/Denario>, and the full app will be deployed on the cloud.
 
 These seminars are open to members of the University of Cambridge. For further details, please email accelerate-science@cst.cam.ac.uk.