timepoints and modalities. This includes modeling gene expression, morphology, protein activity,
and other phenotypic changes under genetic or chemical interventions.
Explain<\/strong> these responses by identifying key biomolecular interactions, causal pathways, and
context-dependent regulatory mechanisms. Correct explanations support predictions by enabling
generalization beyond the training data and enable reasoning about counterfactuals and the
response of biological systems at higher levels of organization.
Discover<\/strong> new biological insights and actionable therapeutic hypotheses through lab-in-the-loop
experimentation, using the virtual cell as a world model for systematic hypothesis generation,
testing, and refinement.<\/p>\n\n\n\n
1.1 The Predict-Explain-Discover capabilities of virtual models<\/strong> 1.2 Virtual models without fully mechanistic simulation<\/strong> Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from …<\/p>\n","protected":false},"template":"","post_category":[],"class_list":["post-317","publications","type-publications","status-publish","hentry"],"yoast_head":"\n
What exactly makes virtual models so potentially valuable for drug discovery? They would enable
researchers to accurately<\/p>\n\n\n\n\n
To better understand what we mean, we give two examples using well-known cancer treatments; these
are only meant to motivate the concepts and not to imply that these would represent novel discoveries
if made today:
Pembrolizumab<\/strong>. A virtual patient could be used to discover that Pembrolizumab is a likely cancer
treatment by predicting a reduction in average tumor volume of a patient in response to treatment with
the drug and explaining that the drug blocks the Programmed Cell Death Protein 1 (PD-1) immune
checkpoint.
Vorinostat<\/strong>. A virtual cell could be used to discover that Vorinostat is an effective cancer treatment
by predicting the up-regulation of tumor suppression genes in response to treatment with the drug and
explaining that the drug inhibits histone deacetylase (HDAC) enzymes.
Throughout this paper, we will refer to these three capabilities as the Predict-Explain-Discover, or PED,
capabilities of virtual models, and claim that it is precisely the ability of virtual models to accurately
predict outcomes and explain them mechanistically that would make them powerful tools to discover
novel therapeutic insights. Figure 1 provides an illustration of the PED capabilities for a virtual cell
(see also Box 1 for more)<\/li>\n<\/ol>\n\n\n\n![\"\"](\"http:\/\/localhost\/wordpress\/valancelabs\/wp-content\/uploads\/2025\/04\/Frame-2087325822-1.png\")
<\/figure>\n\n\n\n
Given the current difficulties building fully mechanistic virtual model simulators, the question naturally
arises: can we build these models without resorting to fully mechanistic simulation? We argue that
four recent advances put this objective within reach today, particularly for virtual cells:<\/p>\n\n\n\n\n
We briefly describe each of these advances below:
AI\/ML. While traditional computational drug discovery techniques have struggled to deal with
biological complexity (Sams-Dodd, 2005; Swinney & Anthony, 2011; Waring et al., 2015), recent<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"