Artificial Intelligence Virtual Cells (AIVCs) represent a paradigm shift in life science, offering unprecedented potential for in silico experimentation in biomedical research. However, how to construct robust AIVCs remains unclear. Here, we introduce the Westlake AI Virtual Cell – Yeast (WAY) project, a pioneering initiative to develop a comprehensive AIVC for Saccharomyces cerevisiae. Our approach is built on three essential data pillars: a priori knowledge, static architecture, and dynamic states. We emphasize the critical role of perturbation proteomics in capturing cellular dynamics, drawing from our previous success in developing neural network ODE models for predicting drug sensitivity and combination synergy in cancer cell lines. The WAY project aims to conduct large-scale, multi-omics, and multi-modal data collection through extensive perturbation experiments on yeast. By integrating diverse data types, including literature knowledge, multi-omics, structures and imaging, among others, we will employ a closed-loop active learning system that combines AI algorithms with automated experimentation. This iterative approach will eventually develop AIVCs that could evolve autonomously, continuously refining their predictive capabilities. The WAY project represents the first step towards creating a fully functional, self-evolving virtual yeast cell, with implications for advancing our understanding of cellular biology, drug discovery, and synthetic biology. We invite contributions from the scientific community to the WAY project.
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