How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities

TL;DR

This paper envisions leveraging AI to create detailed virtual cell models from biological data, enabling advanced simulations for understanding cell behavior, disease mechanisms, and drug discovery.

Contribution

It proposes a comprehensive framework for constructing AI-driven virtual cells, addressing data, evaluation, and community standards to advance biological modeling.

Findings

Identifies key capabilities for AI Virtual Cells.

Discusses challenges and opportunities in modeling.

Envisions applications in drug discovery and cellular response prediction.

Abstract

The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells. Here we propose a vision of leveraging advances in AI to construct virtual cells, high-fidelity simulations of cells and cellular systems under different conditions that are directly learned from biological data across measurements and scales. We discuss desired capabilities of such AI Virtual Cells, including generating universal representations of biological entities across scales, and facilitating interpretable in silico experiments to predict and understand their behavior using virtual instruments. We further address the challenges, opportunities and requirements to realize this vision including data needs, evaluation strategies, and community standards and engagement to ensure biological accuracy and broad utility. We envision a future where AI Virtual Cells help identify new drug targets, predict cellular responses to perturbations, as well as scale hypothesis exploration. With open science collaborations across the biomedical ecosystem that includes academia, philanthropy, and the biopharma and AI industries, a comprehensive predictive understanding of cell mechanisms and interactions has come into reach.