Martin Lercher

Prof.

Heinrich Heine University Düsseldorf

Germany

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Professor Martin Lercher is a faculty member at Heinrich Heine University Düsseldorf, Germany. His research areas include enzyme and transporter interactions, gene transfer mechanisms, and the application of machine learning in predicting biological interactions. He has contributed to numerous articles exploring topics such as protein-small molecule interactions and the efficiency of metabolic pathways in Escherichia coli.

Articles (20)

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2025

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2022

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2021

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2021

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2021

Collaborators (10)

Jennifer Oyler-Yaniv

Assistant Professor

Harvard Medical School

UNITED STATES

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Wolfram Liebermeister

Université Paris-Saclay

FRANCE

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Tobias Bollenbach

Professor Dr. at University of Cologne

University of Cologne

GERMANY

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Veronica Maurino

Professor

University of Bonn

GERMANY

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Rodney D. Priestley

Princeton University

UNITED STATES

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Oliver Ebenhöh

Heinrich Heine University Düsseldorf

GERMANY

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Itai Yanai

Adjunct Professor of Biochemistry and Molecular Pharmacology

NYU School of Medicine

UNITED STATES

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Martin K. M. Engqvist

SWEDEN

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Bodo Stern

Chief of Strategic Initiatives

Howard Hughes Medical Institute

UNITED STATES

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Manuel Balparda

University of Bonn

GERMANY

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Martin Lercher

Research Interests

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Articles (20)

DeepMolecules: a web server for predicting enzyme and transporter–small molecule interactions

Horizontal Gene Transfer Inference: Gene Presence–Absence Outperforms Gene Trees

Openness guides discovery

Most bacterial gene families are biased toward specific chromosomal positions

Teach creativity in science higher education

SPOT: A machine learning model that predicts specific substrates for transport proteins

A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships

DLKcat cannot predict meaningful kcat values for mutants and unfamiliar enzymes

It takes two to think

Viridiplantae-specific GLXI and GLXII isoforms co-evolved and detoxify glucosone in planta

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Optimal density of bacterial cells

Mathematical properties of optimal fluxes in cellular reaction networks at balanced growth

A general model to predict small molecule substrates of enzymes based on machine and deep learning

Make science disruptive again

Proteome efficiency of metabolic pathways in Escherichia coli increases along the nutrient flow

Growth‐mediated negative feedback shapes quantitative antibiotic response

An optimal growth law for RNA composition and its partial implementation through ribosomal and tRNA gene locations in bacterial genomes

On the optimality of the enzyme–substrate relationship in bacteria

Deep learning allows genome-scale prediction of Michaelis constants from structural features

Collaborators (10)

Jennifer Oyler-Yaniv

Wolfram Liebermeister

Tobias Bollenbach

Veronica Maurino

Rodney D. Priestley

Oliver Ebenhöh

Itai Yanai

Martin K. M. Engqvist

Bodo Stern

Manuel Balparda

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