Opening Remarks Room: FD5

Patrick Bryant: Structure prediction and design of protein interactions Room: FD5 The advent of AlphaFold revolutionized our understanding of protein structure prediction, delivering atomic-level accuracy for individual proteins. In this talk, I will present our advances in leveraging and extending the capabilities of predictive models like AlphaFold to predict and design functional protein complexes. Our work builds on the structural insights provided by AlphaFold, developing methodologies to predict and design binders directly from sequence data. This approach has led to the creation of EvoBind, a platform capable of designing cyclic peptide binders with sub-nanomolar affinities in a single shot.

Anneli Kruve: Discovering toxic chemicals with machine learning Room: FD5 Development of chemical industry over the last century enabled our comfortable life; however, the technology is imperfect and many unknown chemicals with unknown effects end up in the consumer products directly impacting humans or are emitted to the environment thorough waste streams. Due to the fact that these chemicals are unknown, novel data driven methods are needed detecting and understanding the impact of these chemicals. In this talk I will give an overview of the machine learning approaches that have been recently developed for discovering toxic chemicals and how combining chemical methods with machine learning can improve over understanding of the chemical exposure.

Golnaz Taheri: From Genes to Algorithms: How AI is Reshaping Life sciences Room: FD5 In this talk, I explore the transformative role of AI and ML in tackling some of the most critical challenges in computational biology. I will discuss how AI/ML, as powerful tools, offer solutions to decode complex biological systems, predict drug interactions, and advance cancer research. Highlighting the synergy between innovative methods and practical applications, this presentation focuses on two key research areas: identifying critical genes and pathways in cancer using deep learning and developing a novel framework for drug-drug interaction prediction using biological networks. By integrating cutting-edge AI/ML techniques with real-world challenges, we aim to contribute to shaping the future of life sciences.

Chun-Biu Li: If you can't explain machine learning simply, you don't understand it well enough Room: FD5 Despite their high popularity in both academic and industrial applications, machine learning (ML) and AI usually give the impression of blackbox models, existence of artifacts, counterintuitive results, diversity of methods, complicated training, etc. In this talk, I will share my research and teaching experiences in ML/AI in order to promote the importance of building up intuitive and comprehensive understanding of ML/AI methods for students and researchers who are interested in applying ML/AI to their studies.

Panel discussion and closing remarkds Room: FD5

Networking session Room: FD5