Evidence map›Paper›PMID 42129086›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2026

Antibody Affinity Maturation by Computational Design.

Esam Tolba Abualrous, Wade Miller, Daniel Andrew Cannon

Abstract read
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In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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.

Esam Tolba AbualrousSchrödinger GmbH, Life Sciences Software, Mannheim, Germany.
Wade MillerSchrödinger Inc, Cambridge, MA, USA.
Daniel Andrew CannonSchrödinger GmbH, Life Sciences Software, Mannheim, Germany. dan.cannon@schrodinger.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This chapter aims to provide the reader with an overview of how to leverage computational algorithms toward improving the affinity of an antibody for a particular target. In most cases, structural information is required; however, the prerequisite information is not always available. Here, we take a bottom-up approach that will guide readers through scenarios where data such as antibody apo or complex structures are unknown. The focus of the chapter is on how computational tools and workflows can be used to address these limitations when attempting to identify an affinity-matured antibody variant with desirable properties. We begin with an overview of antibody structure prediction, followed by predictions of antibody-antigen complexes, antibody affinity, stability, and developability. Finally, we conclude this chapter with guidance notes and best practices for antibody computational design.

Indexed as

AntibodiesAntibody AffinityComputational BiologyProtein EngineeringAlgorithmsAntigen-Antibody ComplexHumansImmunoinformaticsProtein ConformationSoftwareAntibodiesAntigen-Antibody ComplexAffinity maturationAntibody developabilityAntibody engineeringComputational protein designIn silico mutagenesisProtein–protein dockingProtein structure prediction

Identifiers

What OpenQuestion holds

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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 OpenQuestion graph.