Evidence map›Paper›PMID 42864375›Full record

Articlenpj drug discovery2026

Interpretable machine learning framework for peptide lead optimization in ultralarge design spaces.

Yixiang Mao, Ruibo Zhang, Robert Sheridan, Dan Sindhikara, Dipannita Kalyani, Li Xiao, Amy B McCracken, Bryant Kim, Shasha Feng, Tianxiong Mi and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in npj drug discovery, 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

13 authors.

Yixiang MaoDepartment of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA.
Ruibo ZhangDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA. zrbbit@gmail.com.
Robert SheridanDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Dan SindhikaraDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Dipannita KalyaniDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Li XiaoDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Amy B McCrackenDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Bryant KimDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Shasha FengDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Tianxiong MiDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Christopher W PlummerDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Jennifer M JohnstonDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA.
Liying ZhangDiscovery Chemistry, Merck & Co., Inc., Rahway, NJ, USA. zhang.liying@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In modern peptide drug discovery, the incorporation of noncanonical amino acids (ncAAs) has become a powerful strategy to improve potency and key developability properties, including stability, solubility, and oral bioavailability. This shift has expanded peptide chemical diversity and design spaces well beyond natural residues, creating fundamental challenges for predictive methods (e.g., quantitative structure-activity relationship modeling) used in lead optimization: limited transferability from natural residues to novel ncAAs, limited interpretability, and poor scalability to ultralarge peptide spaces (>10

Identifiers

PMID42864375
PMCPMC13658789

What OpenQuestion holds

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Registered trials

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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 OpenQuestion graph.