ArticleCommunications chemistry2023
A mutation-induced drug resistance database (MdrDB).
Article in Communications chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 16 citations in OpenAlex.
- Atomic-level protein-ligand recognition with PBCNet2.0 for probe discovery.Nature chemical biology · 2026Article
- Deciphering resistance mechanisms to auxin-inducible protein degradation in mammalian cells.The Journal of biological chemistry · 2026Article
- Systematic evaluation of computational tools to predict the effects of mutations on protein-ligand binding affinity in the absence of experimental structures.Briefings in bioinformatics · 2026Article
- DRESIS 2.0: the comprehensive landscape of drug resistance information.Nucleic acids research · 2026Article
- Beyond Random Splits: Assessing the Generalization of Graph and Vector Models for WT-Structure-Only Drug Resistance Prediction under Protein-Disjoint Evaluation.Computational and structural biotechnology journal · 2026Article
- Deciphering acquired resistance mechanisms to sustained auxin-inducible protein degradation in cells and mice.bioRxiv : the preprint server for biology · 2025Article
- Computational analysis of missense mutations in squalene epoxidase associated with terbinafine resistance in clinically reported dermatophytes.Scientific reports · 2025Article
- Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.Molecular cancer · 2025Review
- Key amino acid residues govern the substrate selectivity of the transporter Xltr1p fromEngineering microbiology · 2024Article
- CAP-RNAseq: an integrated pipeline for functional annotation and prioritization of co-expression clusters.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mutation-induced drug resistance is a significant challenge to the clinical treatment of many diseases, as structural changes in proteins can diminish drug efficacy. Understanding how mutations affect protein-ligand binding affinities is crucial for developing new drugs and therapies. However, the lack of a large-scale and high-quality database has hindered the research progresses in this area. To address this issue, we have developed MdrDB, a database that integrates data from seven publicly available datasets, which is the largest database of its kind. By integrating information on drug sensitivity and cell line mutations from Genomics of Drug Sensitivity in Cancer and DepMap, MdrDB has substantially expanded the existing drug resistance data. MdrDB is comprised of 100,537 samples of 240 proteins (which encompass 5119 total PDB structures), 2503 mutations, and 440 drugs. Each sample brings together 3D structures of wild type and mutant protein-ligand complexes, binding affinity changes upon mutation (ΔΔG), and biochemical features. Experimental results with MdrDB demonstrate its effectiveness in significantly enhancing the performance of commonly used machine learning models when predicting ΔΔG in three standard benchmarking scenarios. In conclusion, MdrDB is a comprehensive database that can advance the understanding of mutation-induced drug resistance, and accelerate the discovery of novel chemicals.
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Registered trials
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.