Evidence map›Paper›PMID 42595785›Full record

ReviewNature medicine2026

How to build an AI-driven digital organism.

Le Song, Eran Segal, Eric Xing

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Le SongGenBio AI, Palo Alto, CA, USA.
Eran SegalGenBio AI, Palo Alto, CA, USA.ORCID http://orcid.org/0000-0002-6859-1164
Eric XingGenBio AI, Palo Alto, CA, USA. epxing@cs.cmu.edu.ORCID http://orcid.org/0000-0002-3683-4280

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biology lies at the core of medicine, pharmacy, public health and longevity. However, in the physical world, biology is often too complex to manipulate and too expensive and risky to tamper with. In this Perspective, we put forward a vision of using AI to model and simulate biology and life. We present our vision on how to address this challenge through the construction of an AI-driven digital organism (AIDO)-a system of integrated multiscale foundation models-in a modular, connectable and holistic fashion to reflect biological scales, connectedness and complexities. An AIDO opens up a safe, affordable and high-throughput alternative platform for predicting, simulating and programming biology at all levels, from molecules to cells to individuals. We envision that an AIDO is poised to trigger a new wave of better-guided wet-lab experimentation and better-informed first-principle reasoning, which can eventually help us better decode and improve life.

Indexed as

Artificial IntelligenceModels, BiologicalComputer SimulationHumans

Identifiers

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.