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Deep Learning in Drug Discovery

Molecular informatics, 2016-01, Vol.35 (1), p.3-14

2016

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Recurrent Neural Network Model for Constructive Peptide Design

Journal of chemical information and modeling, 2018-02, Vol.58 (2), p.472-479

2018

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Identification of new PNEPs indicates a substantial non-PEXEL exportome and underpins common features in Plasmodium falciparum protein export

PLoS pathogens, 2013-08, Vol.9 (8), p.e1003546-e1003546

2013

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modlAMP: Python for antimicrobial peptides

Bioinformatics (Oxford, England), 2017-09, Vol.33 (17), p.2753-2755

2017

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Designing antimicrobial peptides: form follows function

Nature reviews. Drug discovery, 2012-01, Vol.11 (1), p.37-51

2012

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Task‐Similarity is a Crucial Factor for Few‐Shot Meta‐Learning of Structure‐Activity Relationships

Chembiochem : a European journal of chemical biology, 2024-10, Vol.25 (19), p.e202400095-n/a

2024

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In silico design and optimization of selective membranolytic anticancer peptides

Scientific reports, 2019-08, Vol.9 (1), p.11282-11, Article 11282

2019

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Advancing drug discovery via GPU-based deep learning

Expert opinion on drug discovery, 2018-07, Vol.13 (7), p.579-582

2018

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Designing Anticancer Peptides by Constructive Machine Learning

ChemMedChem, 2018-07, Vol.13 (13), p.1300-1302

2018

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Neural multi-task learning in drug design

Nature machine intelligence, 2024-02, Vol.6 (2), p.124-137

2024

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Prospective de novo drug design with deep interactome learning

Nature communications, 2024-04, Vol.15 (1), p.3408-3408, Article 3408

2024

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Simulated Molecular Evolution for Anticancer Peptide Design

Angewandte Chemie International Edition, 2019-02, Vol.58 (6), p.1674-1678

International ed. in English, 2019

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Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

Nature machine intelligence, 2019-07, Vol.1 (7), p.307-315

2019

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De novo design of anticancer peptides by ensemble artificial neural networks

Journal of molecular modeling, 2019-05, Vol.25 (5), p.112-10, Article 112

2019

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Innovative Teacher Education with the Augmented Reality Device Microsoft HoloLens—Results of an Exploratory Study and Pedagogical Considerations

Multimodal technologies and interaction, 2021-08, Vol.5 (8), p.45

2021

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Scrutinizing MHC-I binding peptides and their limits of variation

PLoS computational biology, 2013-06, Vol.9 (6), p.e1003088-e1003088

2013

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Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

Communications chemistry, 2018-08, Vol.1 (1), Article 44

2018

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Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution

Angewandte Chemie International Edition, 2019-05, Vol.58 (21), p.7138-7142

International ed. in English, 2019

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Identification of E-cadherin signature motifs functioning as cleavage sites for Helicobacter pylori HtrA

Scientific reports, 2016-03, Vol.6 (1), p.23264, Article 23264

2016

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Concepts and applications of "natural computing" techniques in de novo drug and peptide design

Current pharmaceutical design, 2010-05, Vol.16 (15), p.1656-1665

2010

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The N-terminal extension of the P. falciparum GBP130 signal peptide is irrelevant for signal sequence function

International journal of medical microbiology, 2018-01, Vol.308 (1), p.3-12

2018

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An Unusual ERAD-Like Complex Is Targeted to the Apicoplast of Plasmodium falciparum

Eukaryotic Cell, 2009-08, Vol.8 (8), p.1134-1145

2009

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Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Biochemistry (Easton), 2020-10, Vol.59 (39), p.3772-3781

2020

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Rational Design of Membrane‐Pore‐Forming Peptides

Small (Weinheim an der Bergstrasse, Germany), 2017-10, Vol.13 (40), p.n/a

2017

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Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

Molecular informatics, 2017-01, Vol.36 (1-2), p.np-n/a

2017

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Deep Learning in Drug Discovery

Recurrent Neural Network Model for Constructive Peptide Design

Identification of new PNEPs indicates a substantial non-PEXEL exportome and underpins common features in Plasmodium falciparum protein export

modlAMP: Python for antimicrobial peptides

Designing antimicrobial peptides: form follows function

Task‐Similarity is a Crucial Factor for Few‐Shot Meta‐Learning of Structure‐Activity Relationships

In silico design and optimization of selective membranolytic anticancer peptides

Advancing drug discovery via GPU-based deep learning

Designing Anticancer Peptides by Constructive Machine Learning

Neural multi-task learning in drug design

Prospective de novo drug design with deep interactome learning

Simulated Molecular Evolution for Anticancer Peptide Design

Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis

De novo design of anticancer peptides by ensemble artificial neural networks

Innovative Teacher Education with the Augmented Reality Device Microsoft HoloLens—Results of an Exploratory Study and Pedagogical Considerations

Scrutinizing MHC-I binding peptides and their limits of variation

Scaffold hopping from natural products to synthetic mimetics by holistic molecular similarity

Identification of Chemokine Ligands by Biochemical Fragmentation and Simulated Peptide Evolution

Identification of E-cadherin signature motifs functioning as cleavage sites for Helicobacter pylori HtrA

Concepts and applications of "natural computing" techniques in de novo drug and peptide design

The N-terminal extension of the P. falciparum GBP130 signal peptide is irrelevant for signal sequence function

An Unusual ERAD-Like Complex Is Targeted to the Apicoplast of Plasmodium falciparum

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Rational Design of Membrane‐Pore‐Forming Peptides

Hybrid Network Model for “Deep Learning” of Chemical Data: Application to Antimicrobial Peptides

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