CycPeptMPDB: A Comprehensive Database of Membrane Permeability of Cyclic Peptides
Author:
Affiliation:
1. Department of Computer Science, School of Computing, Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
2. Middle-Molecule IT-based Drug Discovery Laboratory (MIDL), Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan
Funder
Japan Agency for Medical Research and Development
Japan Society for the Promotion of Science
Japan Science and Technology Agency
Publisher
American Chemical Society (ACS)
Subject
Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c01573
Reference29 articles.
1. Utilizing graph machine learning within drug discovery and development
2. The Challenge of Drugging Undruggable Targets in Cancer: Lessons Learned from Targeting BCL-2 Family Members
3. Macrocyclic Peptides as Drug Candidates: Recent Progress and Remaining Challenges
4. Plasma protein binding prediction focusing on residue-level features and circularity of cyclic peptides by deep learning
5. The RaPID Platform for the Discovery of Pseudo-Natural Macrocyclic Peptides
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1. Interpretable molecular encodings and representations for machine learning tasks;Computational and Structural Biotechnology Journal;2024-12
2. CREMP: Conformer-rotamer ensembles of macrocyclic peptides for machine learning;Scientific Data;2024-08-09
3. Peptide-specific chemical language model successfully predicts membrane diffusion of cyclic peptides;2024-08-09
4. MuCoCP: a priori chemical knowledge-based multimodal contrastive learning pre-trained neural network for the prediction of cyclic peptide membrane penetration ability;Bioinformatics;2024-07-27
5. CycPeptMP: enhancing membrane permeability prediction of cyclic peptides with multi-level molecular features and data augmentation;Briefings in Bioinformatics;2024-07-25
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