OpenAI Healthcare United States

de la Fuente Lab

de la Fuente Lab needed a faster, less trial-and-error way to discover antibiotic candidates against antimicrobial resistance because traditional molecule hunting can take years and requires difficult computational work across multiple scientific disciplines.

Around 5 million deaths are associated with bacterial AMR every year
We can now discover new antibiotic molecules in a few hours instead of in five or six years

Reported impact

Business outcome

The lab dramatically accelerated candidate discovery, enabled more non-programmers to participate in computational biology, and increased confidence that AI-assisted research can uncover life-saving antibiotic molecules.

  • Productivity Improvement
  • Scalability

Solution

de la Fuente Lab uses ChatGPT and Codex as an AI-assisted discovery environment for antimicrobial research. The team treats DNA and molecular biology as code, feeds discovery questions and experimental ideas into ChatGPT, and uses the model to help formulate algorithms that can search complex biological systems for promising antibiotic candidates. Codex then turns those ideas into scripts, plotting routines, and computational biology workflows that chemists, biologists, and engineers across the lab can actually run. Candidate molecules move from AI-assisted analysis into wet-lab synthesis and testing, creating a loop where computational suggestions inform experiments and lab results shape the next search. This setup lets the lab work at digital speed, shortens the path from hypothesis to candidate discovery, and broadens who can contribute to computational biology inside the team.

Data flow

Researchers frame molecular discovery problems in ChatGPT, turn promising approaches into code with Codex, run those workflows across biological data and molecular structures, and then pass the resulting candidates into wet-lab testing to validate which molecules can kill pathogens.

Solution architecture

2 components · 2 layers
  1. Serving
    • ChatGPT Helps researchers generate ideas and devise algorithms for identifying antibiotic candidates from biological data.
  2. Orchestration
    • Codex Turns research ideas into scripts and analysis workflows the lab can use for computational biology work.

Architecture clues

  • ChatGPT became the lab's communal brain for idea generation
  • The lab treats DNA and biology as code when searching for new antibiotics
  • The team spans chemists, biologists, computer scientists, and machine learning engineers
  • Wet-lab scientists synthesize and test molecules proposed by the computational workflows

Evidence from the source

ChatGPT has become an essential part of the de la Fuente Lab's workflow. It has become the lab's communal brain
People that five years ago would have never touched a computer other than to analyze data are now able to do computational biology
Tools like ChatGPT and Codex are enabling Dr. Cesar de la Fuente and team to take a novel, and faster, approach to identifying new molecules
We can now discover new antibiotic molecules in a few hours instead of in five or six years
Transparency Evidence & methodology 70/100 reference value · 88% confidence · 3 sources

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value70/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale5/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence88%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability23/25
    11 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance8/10
    The publication date comes from publisher-controlled metadata.
  • Source transparency9/10
    3 source references document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    de la Fuente Lab customer story

    First-party
    https://openai.com/index/accelerating-antibiotic-discovery/
    Source type
    Vendor-published customer story
    Published
    Sep 9, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date
  2. de la Fuente Lab / University of Pennsylvania

    CONTACT – DE LA FUENTE LAB

    First-party
    https://delafuentelab.engineering.upenn.edu/contact/

    The contact page lists the mailing address as "3610 Hamilton Walk ... Philadelphia, PA 19104," supporting a United States headquarters region.

    Source type
    Official company website
    Supports
    • Region
  3. de la Fuente Lab / University of Pennsylvania

    CONTACT – DE LA FUENTE LAB

    First-party
    https://delafuentelab.engineering.upenn.edu/contact/

    The contact page lists a Philadelphia, Pennsylvania mailing address and does not publish a numeric employee count.

    Source type
    Official organization website
    Supports
    • Region

Date evidence

Not separately sourced Industry, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.