The recent advances in artificial intelligence (AI) and machine learning have driven the design of new expert systems and automated workflows that are able to model complex chemical and biological phenomena.  In recent years, AI approaches have been developed and actively deployed to facilitate computational and experimental studies of protein structure prediction, protein dynamics, drug design and  bioengineering [1].   The latest advances in structural characterization of proteins using cryo-EM, NMR, smFRET spectroscopy have highlighted the growing need for data-centric integrative biophysics approaches. These emerging methods and tools, including high-throughput sequencing, microscopy imaging, proteomics, metabolomics, and systems biology, involve utilization of AI and ML models trained on large datasets from different sources. AI-augmented integrative biophysics approaches will provide new strategies for drug design and development - from allosteric drug discovery to pathway-targeted design, systems medicine and protein engineering applications [2,3], The development of community-accessible tools that uniquely leverage the existing experimental and simulation knowledgebase to enable interrogation of  biological functions and mechanisms can provide a much needed boost to further innovation and integration of experimental and computational technologies empowered by booming AI field. Generative AI's ability to simulate and model scenarios is revolutionizing research and development across industries. From pharmaceuticals to automotive design, AI algorithms can predict outcomes, model scenarios and generate innovative solutions to complex problems. Generative AI foundational models take on the ambitious task of understanding how biology works. The recently unveiled frontier language model for biology ESM3 is the first generative model for biology that simultaneously reasons over the sequence, structure, and function of proteins. ESM3’s vocabulary bridges sequence, structure, and function all within the same language model [4]. The emerging Generative AI tool and LLMs enable  programmable biology with  a myriad of applications such as for medicine, biology research, and clean energy. 

The main objectives of the workshop are:

1. The overarching idea of this meeting is to build on the success of our previous conferences “Multiscale simulations of allosteric regulatory mechanisms…” (September 2018, Lugano) , “Quantifying Protein Dynamics and Allosteric regulation in the cell with emerging technologies: From Cryo-EM and NMR to Networks and Machine Learning” ( September 2021, Lausanne) and “Making the invisible protein life visible using integrative biophysical approaches: Structural and dynamic characterization of hidden protein states and allosteric landscapes"  ( October 2023, Lugano)   to Focus  on  the latest developments in   generative AI and LLMs with applications to structural biology, integrative biophysics, programmable bioengineering and drug discovery.
2. The main theme and focus of the Proposed Workshop is to develop bridges and seek new  opportunities for collaborations between   computer scientists, developers of generative AI technologies  and structural biology and biophysical communities.
3. Discuss progress in integrating new biophysical and structural biology advances with generative AI/LLMs tools  in predicting biological phenomena  across sequence-structure-function scales
4. Discuss progress of generative AI and biophysical tools    for  unveiling the invisible aspects of protein ‘life’ and predicting rare events and allosteric  regulation mechanisms
5. Discuss and develop strategic view and partnerships between generative AI and structural biology/integrative biophysics  communities across participating countries on  several major mega-projects and foundational/specialized AI applications (protein dynamics-to-function, protein design, programmable bioengineering and fully automated  end-to-end biomolecule and small molecule  drug discovery and development).
6. AI safety and biological challenges
7. Provide opportunities and engage  students and early-career researchers to discuss their projects in a poster session and contributed talks. 
8. Address gender inequality in science by promoting participation of  women and minorities.
9.  Promote networking between students, early-career and more experienced researchers.